Protein-protein interactions (PPIs) are essential to almost all cellular processes. To better understand the relationships of proteins, we have developed a computational approach combining three-dimensional structures (e.g. structural similarity, structural distance, preserved interface size and fraction of preserved interface) with functional evidences (e.g. biological process ontology, molecular function ontology, cellular component ontology, gene coexpression, phylogenetic profile, interolog and rosetta stone) to construct reliable PPI networks on a genome-wide scale.

AraPPINet - Protein-Protein Interaction Network for Arabidopsis

AraPPINet 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.

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RicePPINet - Protein-Protein Interaction Network for Rice

RicePPINet covers 16,895 non-transposable-element (non-TE) related proteins with 708,819 high-confidence interactions (download riceppinet full dataset) at a 71% true positive rate. Performance evaluation showed the efficiency of RicePPINet-based methods in prioritizing candidate genes involved in complex agronomic traits, was approximately 2-11 times better than random prediction.

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This work is supported by grant (no. 31371229) from the National Natural Science Foundation of China.