Abstract
Protein-protein interactions (PPIs) play fundamental roles in various cellular processes. Here, we present a new version of computational interactome that contains more than 345,000 predicted PPIs involving about 51.2% of the Arabidopsis proteins. Compared to the earlier version, the updated AraPPINet displays a higher accuracy in predicting protein interactions through performance evaluation with independent datasets. In addition to the experimental verifications of the previous version, the new version has been subjected to further validation test that demonstrates its ability to discover novel PPIs involved in hormone signaling pathways. Moreover, network analysis shows that many overlapping proteins are significantly involved in the interactions which mediated the crosstalk among plant hormones. The new version of AraPPINet provides a more reliable interactome which would facilitate the understanding of crosstalk among hormone signaling pathways in plants.
Highlights
Protein-protein interactions (PPIs) play important roles in many cellular processes, including DNA replication, transcription, translation, and signal transduction
The feature based on the cellular component ontology is used to capture the cellular co-localization of proteins for PPI prediction, which could avoid the false interactions raised by spatially separated proteins (Huh et al, 2003)
Proteins interacted with the core proteins of hormone signaling pathway were predicted by the updated AraPPINet
Summary
Protein-protein interactions (PPIs) play important roles in many cellular processes, including DNA replication, transcription, translation, and signal transduction. A number of complementary computational approaches, such as gene fusion (Marcotte et al, 1999), phylogenetic profiling (Pellegrini et al, 1999), gene co-expression (Grigoriev, 2001), gene neighborhood (Rhodes et al, 2005), and interolog (Matthews et al, 2001), have been developed for prediction of PPIs based on genomic context in complete genomes. Computational predictions of PPIs based on the structural context have gained much attention due to the rapid growth of protein structures (Zhang et al, 2012; Rose et al, 2015). Unlike genomic context-based methods, structure-based approaches allow for a much more detailed analysis of PPIs, which can determine the physical characteristics of the interactions and residues at the protein interface
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