Literature DB >> 21786137

Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana.

Zhi-Gang Li1, Fei He, Ziding Zhang, You-Liang Peng.   

Abstract

Ralstonia solanacearum is a devastating bacterial pathogen that has an unusually wide host range. R. solanacearum, together with Arabidopsis thaliana, has become a model system for studying the molecular basis of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in the infection process, and some PPIs can initiate a plant defense response. However, experimental investigations have rarely addressed such PPIs. Using two computational methods, the interolog and the domain-based methods, we predicted 3,074 potential PPIs between 119 R. solanacearum and 1,442 A. thaliana proteins. Interestingly, we found that the potential pathogen-targeted proteins are more important in the A. thaliana PPI network. To facilitate further studies, all predicted PPI data were compiled into a database server called PPIRA (http://protein.cau.edu.cn/ppira/). We hope that our work will provide new insights for future research addressing the pathogenesis of R. solanacearum.

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Year:  2011        PMID: 21786137     DOI: 10.1007/s00726-011-0978-z

Source DB:  PubMed          Journal:  Amino Acids        ISSN: 0939-4451            Impact factor:   3.520


  13 in total

Review 1.  Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches.

Authors:  Armand G Ngounou Wetie; Izabela Sokolowska; Alisa G Woods; Urmi Roy; Katrin Deinhardt; Costel C Darie
Journal:  Cell Mol Life Sci       Date:  2013-04-12       Impact factor: 9.261

2.  Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets.

Authors:  Raghav Kataria; Rakesh Kaundal
Journal:  Front Plant Sci       Date:  2022-06-21       Impact factor: 6.627

Review 3.  Computational Network Inference for Bacterial Interactomics.

Authors:  Katherine James; Jose Muñoz-Muñoz
Journal:  mSystems       Date:  2022-03-30       Impact factor: 7.324

4.  Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches.

Authors:  Sitanshu S Sahu; Tyler Weirick; Rakesh Kaundal
Journal:  BMC Bioinformatics       Date:  2014-10-21       Impact factor: 3.169

Review 5.  Computational approaches for prediction of pathogen-host protein-protein interactions.

Authors:  Esmaeil Nourani; Farshad Khunjush; Saliha Durmuş
Journal:  Front Microbiol       Date:  2015-02-24       Impact factor: 5.640

6.  Prediction of cassava protein interactome based on interolog method.

Authors:  Ratana Thanasomboon; Saowalak Kalapanulak; Supatcharee Netrphan; Treenut Saithong
Journal:  Sci Rep       Date:  2017-12-08       Impact factor: 4.379

7.  PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.

Authors:  Junyang Yue; Danfeng Zhang; Rongjun Ban; Xiaojing Ma; Danyang Chen; Guangwei Li; Jia Liu; Michael Wisniewski; Samir Droby; Yongsheng Liu
Journal:  Database (Oxford)       Date:  2017-01-01       Impact factor: 3.451

8.  Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses.

Authors:  Hong Li; Yuan Zhou; Ziding Zhang
Journal:  Front Plant Sci       Date:  2017-05-29       Impact factor: 5.753

9.  Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.

Authors:  Dario Di Silvestre; Andrea Bergamaschi; Edoardo Bellini; PierLuigi Mauri
Journal:  Proteomes       Date:  2018-06-03

Review 10.  Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.

Authors:  Padhmanand Sudhakar; Kathleen Machiels; Bram Verstockt; Tamas Korcsmaros; Séverine Vermeire
Journal:  Front Microbiol       Date:  2021-05-11       Impact factor: 5.640

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