ArticleDatabase : the journal of biological databases and curation2017
PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.
Article in Database : the journal of biological databases and curation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants.Plants (Basel, Switzerland) · 2023Review
- GreeningDB: A Database of Host-Pathogen Protein-Protein Interactions and Annotation Features of the Bacteria Causing Huanglongbing HLB Disease.International journal of molecular sciences · 2021Article
- alfaNET: A Database of Alfalfa-Bacterial Stem Blight Protein-Protein Interactions Revealing the Molecular Features of the Disease-causing Bacteria.International journal of molecular sciences · 2021Article
- Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.Proteomes · 2018Article
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10 authors.
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Abstract
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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