scholarly journals Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1097
Author(s):  
Yan Yan ◽  
Feng Jiang ◽  
Xinan Zhang ◽  
Tianhai Tian
Keyword(s):  
Dynamic Model ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Activity Data ◽  
Asymmetric Networks ◽  
The Individual

Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

2015 ◽  
Vol 90 (4) ◽  
pp. 1973-1987 ◽  
Author(s):  
Stacy L. DeBlasio ◽  
Juan D. Chavez ◽  
Mariko M. Alexander ◽  
John Ramsey ◽  
Jimmy K. Eng ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Host Protein ◽  
Host Proteins ◽  
Content Type ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Binding Interfaces

ABSTRACTDemonstrating 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 theLuteoviridaeand with unrelated viruses in theHerpesviridaeandAdenoviridae. Functional analysis of three PLRV-interacting host proteinsin plantausing 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.IMPORTANCEThe 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.

scholarly journals RNA regulons and the RNA-protein interaction network

2012 ◽  
Vol 3 (5) ◽  
pp. 403-414 ◽  
Author(s):  
Jochen Imig ◽  
Alexander Kanitz ◽  
André P. Gerber
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Network ◽  
Coordinated Control ◽  
Non Coding Rnas ◽  
Rna Interaction

AbstractThe development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell’s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which – by analogy with DNA regulons in bacteria – refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specific and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNA-binding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded first insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ‘buffers’ to handle stochasticity in cellular transcription.

scholarly journals The latent geometry of the human protein interaction network

2017 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Pablo Mier ◽  
Miguel A. Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Biologically Relevant ◽  
Human Protein Interaction ◽  
Connection Probability ◽  
Cellular Localisation ◽  
Complex Architecture

AbstractTo mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

scholarly journals BIOINFORMATICS APPROACH ON ACTION AND MECHANISM OF BROMELAIN IN HEPATOCELLULAR CARCINOMA

2021 ◽  
pp. 122-129
Author(s):  
SUSHMA S MURTHY ◽  
BALA NARSAIAH T
Keyword(s):  
Hepatocellular Carcinoma ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
P53 Gene ◽  
Computational Method ◽  
Mesenchymal Transition ◽  
In Silico Studies ◽  
Data Points

Objective: The objective of the study was to understand biomolecular interactions of Bromelain and its networking with p53 and β-catenin by a computational method of analysis in Hepatocellular carcinoma (HCC) condition. Methodology: The protein interaction partners for p53 and β-catenin involved in the progression of HCC were collected from National Center for Biotechnology Information. We collected data points and standardized the data points for our data analysis from the public database. We used Cytoscape 3.8.2 version plug-in for constructing a Protein-Protein interaction network. We constructed a pathway network using Biorender.com. Results: The protein interactions concerning p53 and β-catenin are identified and a network is constructed. A total of 18 and 34 nodes were identified which are involved in down-regulation and up-regulation of β-catenin and a total of 30 and 27 nodes for homosapiens are identified which are involved in the downregulation and upregulation of the p53 gene. We identified different pathways which trigger and impact the p53 and Wnt/β- catenin signaling pathways as potential target sites for Bromelain to arrest the progression of cancer Conclusion: In conclusion, our in silico studies anti-cancer activity of Bromelain in HCC relating its effect on apoptosis, cell differentiation, mesenchymal transition, p53 signaling, and Wnt/β-catenin signaling pathways.

Protein interaction studies for understanding the tremor pathway in Parkinson’s disease.

2020 ◽  
Vol 19 ◽  
Author(s):  
Nitu Dogra ◽  
Ruchi Jakhmola Mani ◽  
Deepshikha Pande Katare
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Literature Mining ◽  
Protein Protein Interactions ◽  
Calcium Ion Channels ◽  
Progression Of Disease ◽  
Different Sources

Background: Tremor is one of the most noticeable features, which occurs during the early stages of Parkinson’s disease (PD). It is one of the major pathological hallmarks and does not have any interpreted mechanism. In this study we have framed a hypothesis and deciphered protein-protein interactions between the proteins involved in impairment in sodium and calcium ion channels and thus cause synaptic plasticity leading to a tremor. Methods: Literature mining for retrieval of proteins was done using Science Direct, PubMed Central, SciELO and JSTOR databases. A well thought approach was used and a list of differentially expressed proteins in PD was collected from different sources. A total of 71 proteins were retrieved and a protein interaction network was constructed between them by using Cytoscape.v.3.7. The network was further analysed using BiNGO plugin for retrieval of overrepresented biological processes in Tremor-PD datasets. Hub nodes were also generated in the network. Results: The Tremor-PD pathway was deciphered which demonstrates the cascade of protein interactions that might lead to tremors in PD. Major proteins involved were LRRK2, TUBA1A, TRAF6, HSPA5, ADORA2A, DRD1, DRD2, SNCA, ADCY5, TH etc. Conclusion: In the current study it is predicted that ADORA2A and DRD1/DRD2 are equally contributing to the progression of disease by inhibiting the activity of adenylyl cyclase and thereby increases the permeability of the blood brain barrier causing an influx of neurotransmitters and together they alter the level of dopamine in the brain which eventually leads to tremor.

Data mining of differentially expressed genes in epithelial ovarian carcinoma: Implications in precise medicine

2021 ◽  
Vol 21 ◽  
Author(s):  
Jie Zhao ◽  
Hongjie Gao ◽  
Yun He
Keyword(s):  
Gene Expression ◽  
Ovarian Carcinoma ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Epithelial Ovarian Carcinoma ◽  
Hub Genes ◽  
Diagnosis And Prognosis

Background: Epithelial ovarian carcinoma (EOC) is a ubiquitous gynecological malignancy with complicated pathogenesis. Genetic risk factors and pathways involved in the prognosis of this cancer are not yet understood completely. Determining genetic markers with diagnostic and prognostic values would pave the way for efficient management of cancer. Objective: This study aimed to investigate the genes and the regulatory networks involved in the occurrence and prognosis of EOC through different bioinformatics analysis tools. In addition, recent advances in using bioinformatic analysis approach based on the genes and regulatory networks, particularly differentially expressed genes (DEGs), in improving the diagnosis and prognosis of EOC are discussed. Methods: The gene expression profiles of GSE18520, GSE54388, and GSE27651 were downloaded from the Gene Expression Omnibus (GEO) database and further analyzed with different analyses in R language. Current literature on using bioinformatics based on DEGs and associated regulatory networks to improve the diagnosis and prognosis of EOC were reviewed. Results: Analyses of the gene expression levels between the malignant tissue against normal tissue unveiled 163 DEGs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the target genes using clusterProfiler package, and Cytoscape package was employed to assess the protein interaction network of these genes. The protein-protein interaction network was analyzed using the CytoHubba plug-in to identify 20 hub genes. In addition, we analyzed the prognosis of the hub genes using the Kaplan-Meier survival analysis that revealed evident differences in the prognosis of 13 genes. The malignant tissues exhibited a differential expression of 12 genes against healthy tissues, as shown by Gene Expression Profiling Interactive Analysis (GEPIA) analysis. Conclusion: Findings of this study revealed 12 genes to be significantly up-regulated, and the prognosis was significantly different, which could be employed to potentially target EOC in clinical practice.

scholarly journals Interactome Data and Databases: Different Types of Protein Interaction

2004 ◽  
Vol 5 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Javier De Las Rivas ◽  
Alberto de Luis
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Short Review ◽  
Experimental Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Bioinformatic Tool ◽  
Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

ClusFCM: AN ALGORITHM FOR PREDICTING PROTEIN FUNCTIONS USING HOMOLOGIES AND PROTEIN INTERACTIONS

2008 ◽  
Vol 06 (01) ◽  
pp. 203-222 ◽  
Author(s):  
CAO NGUYEN ◽  
MICHAEL MANNINO ◽  
KATHELEEN GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Direct Interaction ◽  
Interaction Network ◽  
Roc Curves ◽  
Fuzzy Cognitive Maps ◽  
Supplementary Information ◽  
Protein Interaction Data ◽  
Protein Functions ◽  
Grid Database

We introduce a new algorithm, called ClusFCM, which combines techniques of clustering and fuzzy cognitive maps (FCM) for prediction of protein functions. ClusFCM takes advantage of protein homologies and protein interaction network topology to improve low recall predictions associated with existing prediction methods. ClusFCM exploits the fact that proteins of known function tend to cluster together and deduce functions not only through their direct interaction with other proteins, but also from other proteins in the network. We use ClusFCM to annotate protein functions for Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), and Drosophila melanogaster (fly) using protein–protein interaction data from the General Repository for Interaction Datasets (GRID) database and functional labels from Gene Ontology (GO) terms. The algorithm's performance is compared with four state-of-the-art methods for function prediction — Majority, χ2 statistics, Markov random field (MRF), and FunctionalFlow — using measures of Matthews correlation coefficient, harmonic mean, and area under the receiver operating characteristic (ROC) curves. The results indicate that ClusFCM predicts protein functions with high recall while not lowering precision. Supplementary information is available at .

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