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• W2912743845 abstract "Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction 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 protein-protein interactions 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 cosubcellular 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 naively 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 http://bar.utoronto.ca/interactions2/." @default.
• W2912743845 created "2019-02-21" @default.
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• W2912743845 date "2019-01-24" @default.
• W2912743845 modified "2023-10-14" @default.
• W2912743845 title "Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer" @default.
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• W2912743845 doi "https://doi.org/10.1104/pp.18.01216" @default.
• W2912743845 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6446796" @default.
• W2912743845 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30679268" @default.
• W2912743845 hasPublicationYear "2019" @default.
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