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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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Detalles Bibliográficos
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
Descripción
Sumario: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