A network-based pathway-expanding approach for pathway analysis

Zhang, Qiaosheng; Li, Jie; Xie, Haozhe; Xue, Hanqing; Wang, Yadong

doi:10.1186/s12859-016-1333-x

Cited by 6 publications

(5 citation statements)

References 81 publications

(42 reference statements)

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“…Binding affinity defined as the strength of protein–protein interaction, which is related with the cellular functions of those proteins. This binding energy can be expressed as a physiochemical parameter, known as dissociation constant (Kd) 46 , 47 . Prediction of binding affinity between spike protein fragment and ACE2 receptor following contact-based prediction of binding affinity method 46 in protein–protein complexes, using PRODIGY web server 47 has been performed.…”

Section: Methodsmentioning

confidence: 99%

Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2

Basu¹,

Sarkar

Maulik

2020

Sci Rep

216

161

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Angiotensin converting enzyme 2 (ACE2) (EC:3.4.17.23) is a transmembrane protein which is considered as a receptor for spike protein binding of novel coronavirus (SARS-CoV2). Since no specific medication is available to treat COVID-19, designing of new drug is important and essential. In this regard, in silico method plays an important role, as it is rapid and cost effective compared to the trial and error methods using experimental studies. Natural products are safe and easily available to treat coronavirus affected patients, in the present alarming situation. In this paper five phytochemicals, which belong to flavonoid and anthraquinone subclass, have been selected as small molecules in molecular docking study of spike protein of SARS-CoV2 with its human receptor ACE2 molecule. Their molecular binding sites on spike protein bound structure with its receptor have been analyzed. From this analysis, hesperidin, emodin and chrysin are selected as competent natural products from both Indian and Chinese medicinal plants, to treat COVID-19. Among them, the phytochemical hesperidin can bind with ACE2 protein and bound structure of ACE2 protein and spike protein of SARS-CoV2 noncompetitively. The binding sites of ACE2 protein for spike protein and hesperidin, are located in different parts of ACE2 protein. Ligand spike protein causes conformational change in three-dimensional structure of protein ACE2, which is confirmed by molecular docking and molecular dynamics studies. This compound modulates the binding energy of bound structure of ACE2 and spike protein. This result indicates that due to presence of hesperidin, the bound structure of ACE2 and spike protein fragment becomes unstable. As a result, this natural product can impart antiviral activity in SARS CoV2 infection. The antiviral activity of these five natural compounds are further experimentally validated with QSAR study.

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Section: Methodsmentioning

confidence: 99%

Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2

Basu¹,

Sarkar

Maulik

2020

Sci Rep

216

161

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“…Cancer etiology and progression is currently understood to be driven primarily by molecular and genetic mechanisms 1,2 . Cancer is caused by the interactions of multiple genes and pathways.…”

Section: Introductionmentioning

confidence: 99%

A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Zhang

Chen

et al. 2019

Sci Rep

Self Cite

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Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.

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“…Gene Association Network-based Pathway Analysis (GANPA) improves upon this process by adding gene weighting to determine gene non-equivalence within pathways [19]. Similarly, a novel method was recently proposed for assessing the significance of pathways by constructing weighted gene-gene interaction networks for normal and cancerous tissue samples [20]. These interaction networks were subsequently used to expand pathways for each set of samples and compare their topologies.…”

Section: Unbiased Methodsmentioning

confidence: 99%

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Vijayakumar

Conway

Lió

et al. 2017

Briefings in Bioinformatics

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Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

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A network-based pathway-expanding approach for pathway analysis

Cited by 6 publications

References 81 publications

Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2

Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2

A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

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