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Database (Oxford). 2017 Jan 1;2017(1). doi: 10.1093/database/baw170.

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

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College of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China.
Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science and State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China.
School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China.
School of Medical Engineering, Hefei University of Technology, Hefei 230009, China.
United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Kearneysville, WV 25430, USA.
Agricultural Research Organization (ARO), The Volcani Center, 50250 Bet Dagan, Israel.


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.

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