Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational analysis of protein interaction networks for infectious diseases.

ABSTRACT: Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

SUBMITTER: Pan A

PROVIDER: S-EPMC7110031 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Computational analysis of protein interaction networks for infectious diseases.

Pan Archana A Lahiri Chandrajit C Rajendiran Anjana A Shanmugham Buvaneswari B

Briefings in bioinformatics 20150810 3

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as o ...[more]

PMID: 26261187

Similar Datasets

A computational analysis of protein-protein interaction networks in neurodegenerative diseases.

Project description:BACKGROUND: Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network. RESULTS: Specific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways. CONCLUSION: Our findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.

| S-EPMC2443111 | biostudies-literature

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Project description:Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn's disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn's disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

| S-EPMC5021324 | biostudies-literature

Discovery of protein interaction networks shared by diseases.

Project description:The study of protein-protein interactions is essential to define the molecular networks that contribute to maintain homeostasis of an organism's body functions. Disruptions in protein interaction networks have been shown to result in diseases in both humans and animals. Monogenic diseases disrupting biochemical pathways such as hereditary coagulopathies (e.g. hemophilia), provided a deep insight in the biochemical pathways of acquired coagulopathies of complex diseases. Indeed, a variety of complex liver diseases can lead to decreased synthesis of the same set of coagulation factors as in hemophilia. Similarly, more complex diseases such as different cancers have been shown to result from malfunctions of common proteins pathways. In order to discover, in high throughput, the molecular underpinnings of poorly characterized diseases, we present a statistical method to identify shared protein interaction network(s) between diseases. Integrating (i) a protein interaction network with (ii) disease to protein relationships derived from mining Gene Ontology annotations and the biomedical literature with natural language understanding (PhenoGO), we identified protein-protein interactions that were associated with pairs of diseases and calculated the statistical significance of the occurrence of interactions in the protein interaction knowledgebase. Significant correlations between diseases and shared protein networks were identified and evaluated in this study, demonstrating the high precision of the approach and correct non-trivial predictions, signifying the potential for discovery. In conclusion, we demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks, possibly providing insight into the underlying molecular mechanisms of phenotypes and biological processes disrupted in related diseases.

| S-EPMC2886192 | biostudies-other

Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources.

Project description:Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.

| S-EPMC3861891 | biostudies-other

Cocirculation of infectious diseases on networks.

Project description:We consider multiple diseases spreading in a static configuration model network. We make standard assumptions that infection transmits from neighbor to neighbor at a disease-specific rate and infected individuals recover at a disease-specific rate. Infection by one disease confers immediate and permanent immunity to infection by any disease. Under these assumptions, we find a simple, low-dimensional ordinary differential equations model which captures the global dynamics of the infection. The dynamics depend strongly on initial conditions. Although we motivate this Rapid Communication with infectious disease, the model may be adapted to the spread of other infectious agents such as competing political beliefs, or adoption of new technologies if these are influenced by contacts. As an example, we demonstrate how to model an infectious disease which can be prevented by a behavior change.

| S-EPMC3839111 | biostudies-literature

The computational capabilities of many-to-many protein interaction networks.

Project description:Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

| S-EPMC10318606 | biostudies-literature

A computational exploration of resilience and evolvability of protein-protein interaction networks.

Project description:Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

| S-EPMC8639913 | biostudies-literature

Specificity in PDZ-peptide interaction networks: Computational analysis and review.

Project description:Globular PDZ domains typically serve as protein-protein interaction modules that regulate a wide variety of cellular functions via recognition of short linear motifs (SLiMs). Often, PDZ mediated-interactions are essential components of macromolecular complexes, and disruption affects the entire scaffold. Due to their roles as linchpins in trafficking and signaling pathways, PDZ domains are attractive targets: both for controlling viral pathogens, which bind PDZ domains and hijack cellular machinery, as well as for developing therapies to combat human disease. However, successful therapeutic interventions that avoid off-target effects are a challenge, because each PDZ domain interacts with a number of cellular targets, and specific binding preferences can be difficult to decipher. Over twenty-five years of research has produced a wealth of data on the stereochemical preferences of individual PDZ proteins and their binding partners. Currently the field lacks a central repository for this information. Here, we provide this important resource and provide a manually curated, comprehensive list of the 271 human PDZ domains. We use individual domain, as well as recent genomic and proteomic, data in order to gain a holistic view of PDZ domains and interaction networks, arguing this knowledge is critical to optimize targeting selectivity and to benefit human health.

| S-EPMC7138185 | biostudies-literature

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks.

Project description: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.

| S-EPMC3777680 | biostudies-literature

Protein interaction networks in neurodegenerative diseases: From physiological function to aggregation.

Project description:The accumulation of protein inclusions is linked to many neurodegenerative diseases that typically develop in older individuals, due to a combination of genetic and environmental factors. In rare familial neurodegenerative disorders, genes encoding for aggregation-prone proteins are often mutated. While the underlying mechanism leading to these diseases still remains to be fully elucidated, efforts in the past 20 years revealed a vast network of protein-protein interactions that play a major role in regulating the aggregation of key proteins associated with neurodegeneration. Misfolded proteins that can oligomerize and form insoluble aggregates associate with molecular chaperones and other elements of the proteolytic machineries that are the frontline workers attempting to protect the cells by promoting clearance and preventing aggregation. Proteins that are normally bound to aggregation-prone proteins can become sequestered and mislocalized in protein inclusions, leading to their loss of function. In contrast, mutations, posttranslational modifications, or misfolding of aggregation-prone proteins can lead to gain of function by inducing novel or altered protein interactions, which in turn can impact numerous essential cellular processes and organelles, such as vesicle trafficking and the mitochondria. This review examines our current knowledge of protein-protein interactions involving several key aggregation-prone proteins that are associated with Alzheimer's disease, Parkinson's disease, Huntington's disease, or amyotrophic lateral sclerosis. We aim to provide an overview of the protein interaction networks that play a central role in driving or mitigating inclusion formation, while highlighting some of the key proteomic studies that helped to uncover the extent of these networks.

| S-EPMC9234719 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data