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Plant Physiol. 2019 Jan 24. pii: pp.01216.2018. doi: 10.1104/pp.18.01216. [Epub ahead of print]

Proteome-Wide, Structure-Based Prediction of Protein-protein Interactions / New Molecular Interactions Viewer.

Author information

University of Toronto CITY: Toronto STATE: ON. Canada [CA].
University of Toronto CITY: Toronto Canada [CA].
Ontario Institute for Cancer Research CITY: Toronto STATE: Ontario POSTAL_CODE: M5G 0A3 Canada [CA].
University of California - Davis CITY: Davis STATE: CA United States Of America [US].
Princeton University CITY: Princeton STATE: NJ United States Of America [US].
Lunenfeld-Tanenbaum Research Institute CITY: Toronto STATE: ON. Canada [CA].
University of California, Davis CITY: Davis STATE: California POSTAL_CODE: 95616 United States Of America [US].
Virginia Tech, Blacksburg, VA N/A CITY: N/A POSTAL_CODE: N/A United States Of America [US].
RWTH Aachen University CITY: Aachen Germany [DE].
University of Toronto CITY: Toronto STATE: ON. POSTAL_CODE: M5S 3B2 Canada [CA]


Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction (PPI) network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary PPIs in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the top-ranking predicted interactions, as shown by co-subcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naïvely tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ~2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at

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