Literature DB >> 22729399

Proteome-wide prediction of protein-protein interactions from high-throughput data.

Zhi-Ping Liu1, Luonan Chen.   

Abstract

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.

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Year:  2012        PMID: 22729399      PMCID: PMC4875394          DOI: 10.1007/s13238-012-2945-1

Source DB:  PubMed          Journal:  Protein Cell        ISSN: 1674-800X            Impact factor:   14.870


  82 in total

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4.  Network motifs: simple building blocks of complex networks.

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5.  BIND: the Biomolecular Interaction Network Database.

Authors:  Gary D Bader; Doron Betel; Christopher W V Hogue
Journal:  Nucleic Acids Res       Date:  2003-01-01       Impact factor: 16.971

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Authors:  Zhi-Ping Liu; Ling-Yun Wu; Yong Wang; Xiang-Sun Zhang; Luonan Chen
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7.  Inferring protein interactions from experimental data by association probabilistic method.

Authors:  Luonan Chen; Ling-Yun Wu; Yong Wang; Xiang-Sun Zhang
Journal:  Proteins       Date:  2006-03-01

8.  The human disease network.

Authors:  Kwang-Il Goh; Michael E Cusick; David Valle; Barton Childs; Marc Vidal; Albert-László Barabási
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Review 9.  Protein networks in disease.

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Authors:  T Dandekar; B Snel; M Huynen; P Bork
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  19 in total

1.  Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features.

Authors:  Ziyun Ding; Daisuke Kihara
Journal:  Curr Protoc Protein Sci       Date:  2018-06-21

2.  A Cross-Linking-Aided Immunoprecipitation/Mass Spectrometry Workflow Reveals Extensive Intracellular Trafficking in Time-Resolved, Signal-Dependent Epidermal Growth Factor Receptor Proteome.

Authors:  Yue Chen; Mei Leng; Yankun Gao; Dongdong Zhan; Jong Min Choi; Lei Song; Kai Li; Xia Xia; Chunchao Zhang; Mingwei Liu; Shuhui Ji; Antrix Jain; Alexander B Saltzman; Anna Malovannaya; Jun Qin; Sung Yun Jung; Yi Wang
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3.  TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation.

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4.  Nerve Injury-Induced Neuronal PAP-I Maintains Neuropathic Pain by Activating Spinal Microglia.

Authors:  Jiayin Li; Haixiang Shi; Hui Liu; Fei Dong; Zhiping Liu; Yingjin Lu; Luonan Chen; Lan Bao; Xu Zhang
Journal:  J Neurosci       Date:  2019-11-19       Impact factor: 6.167

5.  In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms.

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Review 6.  Computational Network Inference for Bacterial Interactomics.

Authors:  Katherine James; Jose Muñoz-Muñoz
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7.  Protein-Protein Interactions as New Targets for Ion Channel Drug Discovery.

Authors:  Svetla Stoilova-McPhie; Syed Ali; Fernanda Laezza
Journal:  Austin J Pharmacol Ther       Date:  2013-12-31

8.  Protein-protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis.

Authors:  Chaofan Ji; Xupeng Cao; Changhong Yao; Song Xue; Zhilong Xiu
Journal:  J Ind Microbiol Biotechnol       Date:  2014-05-31       Impact factor: 3.346

Review 9.  Structural bioinformatics of the interactome.

Authors:  Donald Petrey; Barry Honig
Journal:  Annu Rev Biophys       Date:  2014       Impact factor: 12.981

10.  Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

Authors:  Zhi-Ping Liu
Journal:  Curr Genomics       Date:  2015-02       Impact factor: 2.236

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