Interactome analyses of Salmonella pathogenicity islands reveal SicA indispensable for virulence

Lahiri, Chandrajit; Pawar, Shrikant; Sabarinathan, Radhakrishnan; Ashraf, Md. Izhar; Chand, Yamini; Chakravortty, Dipshikha

doi:10.1016/j.jtbi.2014.08.013

Cited by 26 publications

(39 citation statements)

References 36 publications

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“…The authors constructed the network from the available F o r P e e r R e v i e w interactions from STRING database and analysed it with the common and rarely used centrality measures to decide upon the most indispensable one. They benchmarked their theoretical finding through analyses of networks built from the expressed gene products of two different microarray data and arrived at the same point with respect to such indispensable protein issue [42]. The outcome of these two works clearly would be the positive side of the analyses of PPI networks for generating systems biomedicine where the goal would be to harm the pathogen without harming the host and avoiding rapid development of antimicrobial resistance.…”

Section: Applications In Systems Biomedicinementioning

confidence: 98%

“…However, the ultimate idea of a protein in a network to be important lies in the fact that it should be connected to other important proteins in the network. EC might come into play in such cases [42]. EC brings out the relative importance of the proteins in the network by weighting the connections to other important proteins compared to those of low importance [43].…”

Section: Topological Analyses To Identify An Important Proteinmentioning

confidence: 99%

“…After this core, a step further decomposes the network, thereby making this the innermost core with highly connected proteins, interacting with each other. Thus, they can be considered to be the most important ones [42].…”

Section: Network Decomposition To Identify Set Of Important Proteinsmentioning

confidence: 99%

“…This is due to the biological phenomenon of proteins aggregating into complexes, rendering them as functional modules which unify the cohesive components of a molecular function. The identification of such highly correlated functional modules of proteins clique centrality (MCC), maximum neighbourhood centrality (MNC) and density of maximum neighbourhood centrality (DMNC) are offshoots of these concepts and has been utilised lately [42]. Such densely connected subnetworks are expected to form functional units to carry out unique biological processes.…”

Section: Modularity Analyses and Functional Annotation Of Clustered Pmentioning

confidence: 99%

“…With the main target of the present review being the application of the PPI networks in biomedicine, cases to unravel the molecular basis of disease, by studying disease related An indirect study on a smaller scale upon Salmoenlla Pathogenecity Island Type III secretion system was carried out to build a methodology of targeting the indispensable proteins from amongst a conglomerate [42]. The authors constructed the network from the available F o r P e e r R e v i e w interactions from STRING database and analysed it with the common and rarely used centrality measures to decide upon the most indispensable one.…”

Section: Applications In Systems Biomedicinementioning

confidence: 99%

See 4 more Smart Citations

Computational analysis of protein interaction networks for infectious diseases

Pan¹,

Lahiri²,

Rajendiran³

et al. 2015

Brief Bioinform

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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.

show abstract

Section: Applications In Systems Biomedicinementioning

confidence: 98%

Section: Topological Analyses To Identify An Important Proteinmentioning

confidence: 99%

Section: Network Decomposition To Identify Set Of Important Proteinsmentioning

confidence: 99%

Section: Modularity Analyses and Functional Annotation Of Clustered Pmentioning

confidence: 99%

Section: Applications In Systems Biomedicinementioning

confidence: 99%

See 3 more Smart Citations

Computational analysis of protein interaction networks for infectious diseases

Pan¹,

Lahiri²,

Rajendiran³

et al. 2015

Brief Bioinform

Self Cite

View full text Add to dashboard Cite

show abstract

Antiviral compound screening, peptide designing, and protein network construction of influenza a virus (strain a/Puerto Rico/8/1934 H1N1)

Saikia

Bordoloi

Sarmah

et al. 2018

Drug Development Research

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Plant‐based antiviral therapy is the current need for holistic health care management, which can be achieved through screening of phytochemicals and designing of antiviral peptides. There exist certain host's factors which are directly involved for rapid viral replication causing worldwide pandemic. A total of 177 phytochemicals from Ocimum sanctum (L.), Tinospora cordifolia (Thunb.) Miers, Cinnamomum camphora (L.) J. Presl., Allium sativum (L.), Curcuma longa (L.), and Aloe vera (L.) Burm. f. were evaluated for their affinity to all viral proteins of H1N1. Applying drug filters and keeping the threshold of such filters relative to the standards, 82 compounds were found suitable for further analysis. Consensus scoring system was used for screening top ligands from 82 compounds, which screened the top 12 compounds. Highly conserved regions (>80%) which were hydrophilic, flexible, antigenic, and also charged were screened out as potent antiviral peptides. The viral proteins were taken as the targets for the modeled peptides for protein–protein docking. Further, host‐pathogen interacting network was constructed to unveil host factors involved in viral replication, from which unique protein clusters representing their involvement in viral reproduction were selected through mapping with pathway databases. Twelve compounds and five peptides were found to be highly effective against all the proteins of H1N1. Based on the uniqueness, 13 clusters of proteins were obtained which are engaged in cellular process, namely, viral reproduction, fructose‐6‐phosphate metabolism, nitrogen compound metabolism, biosynthesis, cellular process, oligodendrocyte development, localization, multiorganism process, primary metabolism, response to unfolded protein, metabolism, and response to protein and catabolism.

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Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm

Pawar

2019

Bioinformatics and Biomedical Engineering

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Interactome analyses of Salmonella pathogenicity islands reveal SicA indispensable for virulence

Cited by 26 publications

References 36 publications

Computational analysis of protein interaction networks for infectious diseases

Computational analysis of protein interaction networks for infectious diseases

Antiviral compound screening, peptide designing, and protein network construction of influenza a virus (strain a/Puerto Rico/8/1934 H1N1)

Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm

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