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PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions

Penicillium expansum, the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium–crop interaction is a m...

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Autores principales: Yue, Junyang, Zhang, Danfeng, Ban, Rongjun, Ma, Xiaojing, Chen, Danyang, Li, Guangwei, Liu, Jia, Wisniewski, Michael, Droby, Samir, Liu, Yongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467543/
https://www.ncbi.nlm.nih.gov/pubmed/28365721
http://dx.doi.org/10.1093/database/baw170
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author Yue, Junyang
Zhang, Danfeng
Ban, Rongjun
Ma, Xiaojing
Chen, Danyang
Li, Guangwei
Liu, Jia
Wisniewski, Michael
Droby, Samir
Liu, Yongsheng
author_facet Yue, Junyang
Zhang, Danfeng
Ban, Rongjun
Ma, Xiaojing
Chen, Danyang
Li, Guangwei
Liu, Jia
Wisniewski, Michael
Droby, Samir
Liu, Yongsheng
author_sort Yue, Junyang
collection PubMed
description Penicillium expansum, the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium–crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein–protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium-Crop Protein–Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen–plant systems and available domain–domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. Database URL: http://bdg.hfut.edu.cn/pcppi/index.html
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spelling pubmed-54675432017-06-19 PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions Yue, Junyang Zhang, Danfeng Ban, Rongjun Ma, Xiaojing Chen, Danyang Li, Guangwei Liu, Jia Wisniewski, Michael Droby, Samir Liu, Yongsheng Database (Oxford) Database Tool Penicillium expansum, the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium–crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein–protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium-Crop Protein–Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen–plant systems and available domain–domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. Database URL: http://bdg.hfut.edu.cn/pcppi/index.html Oxford University Press 2017-02-26 /pmc/articles/PMC5467543/ /pubmed/28365721 http://dx.doi.org/10.1093/database/baw170 Text en © The Author(s) 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Tool
Yue, Junyang
Zhang, Danfeng
Ban, Rongjun
Ma, Xiaojing
Chen, Danyang
Li, Guangwei
Liu, Jia
Wisniewski, Michael
Droby, Samir
Liu, Yongsheng
PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title_full PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title_fullStr PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title_full_unstemmed PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title_short PCPPI: a comprehensive database for the prediction of Penicillium–crop protein–protein interactions
title_sort pcppi: a comprehensive database for the prediction of penicillium–crop protein–protein interactions
topic Database Tool
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467543/
https://www.ncbi.nlm.nih.gov/pubmed/28365721
http://dx.doi.org/10.1093/database/baw170
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