AraPPINet - Protein-Protein Interaction Network for Arabidopsis
The global Arabidopsis PPI network (AraPPINet) that was inferred from both three-dimensional structures and functional evidence and that encompasses 316,747 high-confidence interactions among 12,574 proteins(download arappinet full dataset). AraPPINet exhibited high predictive power for discovering protein interactions at a 50% true positive rate and for discriminating positive interactions from similar protein pairs at a 70% true positive rate. Experimental evaluation of a set of predicted PPIs demonstrated the ability of AraPPINet to identify novel protein interactions involved in a specific process at an approximately 100-fold greater accuracy than random protein-protein pairs in a test case of abscisic acid (ABA) signaling. Genetic analysis of an experimentally validated, predicted interaction between ARR1 and PYL1 uncovered crosstalk between ABA and cytokinin signaling in the control of root growth. Therefore we demonstrate the power of AraPPINet as a resource for discovering gene function in converging signaling pathways and complex traits in plant.
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Features used in machine learning to predict Arabidopsis protein-protein interactions
Coverage(1): the number of protein pairs with an available given feature was divided by the total number of possible pairs.
||Predicted protein interaction
|Preserved interface size
|Fraction of preserved interface
|Biological process ontology
|Molecular function ontology
|Cellular component ontology
Coverage(2): the number of predicted protein-protein interactions with an available given feature was divided by the total number of predicted interactions.
Please cite: Fangyuan Zhang, Shiwei Liu, Ling Li, Kaijing Zuo, Lingxia Zhao, and Lida Zhang. Genome-Wide Inference of Protein-Protein Interaction Networks Identifies Crosstalk in Abscisic Acid Signaling. Plant Physiology. 2016. 171: 1511-1522. doi:10.1104/pp.16.00057. Abstract