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

Feature Protein pair Coverage(1) Predicted protein interaction Coverage(2)
Structural similarity 39,439,520 11.48% 116,077 36.65%
Structural distance 39,439,520 11.48% 116,077 36.65%
Preserved interface size 31,625,234 9.21% 100,964 31.88%
Fraction of preserved interface 31,625,234 9.21% 100,964 31.88%
Biological process ontology 210,586,502 61.32% 299,301 94.49%
Molecular function ontology 211,428,752 61.57% 294,459 92.96%
Cellular component ontology 39,867,985 11.61% 182,806 57.71%
Gene coexpression 204,353,436 59.51% 266,063 84.00%
Phylogenetic profile 343,416,528 100.00% 316,747 100.00%
Interolog 679,718 0.20% 45,621 14.40%
Rosetta stone 979,649 0.29% 1,205 0.38%
Coverage(1): the number of protein pairs with an available given feature was divided by the total number of possible pairs.
Coverage(2): the number of prediced 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