Genome-wide inference of protein interaction network and its application to the study of crosstalk in Arabidopsis abscisic acid signaling

Abstract

Protein-protein interactions (PPIs) are essential to almost all cellular processes. To better understand the relationships of proteins in Arabidopsis (Arabidopsis thaliana), we have developed a genome-wide protein interaction network (AraPPINet) that is inferred from both three-dimensional structures and functional evidence and that encompasses 316,747 high-confidence interactions among 12,574 proteins. 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 cross talk between ABA and cytokinin signaling in the control of root growth. Therefore, we demonstrate the power of AraPPINet (http://netbio. sjtu.edu.cn/arappinet/) as a resource for discovering gene function in converging signaling pathways and complex traits in plants.

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

“…It is well worth exploring how well modeling‐based PPI networks developed from rice‐derived data sets compare with networks built by simply transferring interactions from orthologous gene pairs from the existing Arabidopsis PPI network (Zhang et al ., ). The latter approach does not require modeling using rice annotations or any of the rice‐derived experimental data.…”

“…This feature benefited from modeling with rice annotations and experimental data, allowing RicePPINet to discover PPIs specific for rice, which suggests that species‐specific modeling and optimization could effectively improve the accuracy and coverage of PPI prediction. In addition, we employed structural information in rice PPI prediction, which has been demonstrated to improve the reliability of predicted PPI networks by reducing false positives from a large number of non‐interacting protein pairs at the genome‐wide level (Zhang et al ., , ; Garzón et al ., ). All these factors contributed to the high predictability of RicePPINet for PPI discovery in rice.…”

“…The structural features included structural similarity, structural distance, preserved interface size and fraction of the preserved interface derived from structural alignments of protein structure homology models with complexes. The structural feature values were calculated for each interaction model as previously described (Zhang et al ., ).…”

“…The phylogenetic profile similarity and Rosetta stone protein were computed following the protocol previously described by Zhang et al . ().…”

“…A critical advantage of structure‐based approaches is their ability to identify the putative conserved interface, providing more information than any non‐structure‐based methods. Structure‐based methods have been demonstrated to be powerful for PPI prediction in model organisms, including Arabidopsis (Zhang et al ., , ; Garzón et al ., ). The availability of structure‐based PPI network models of Arabidopsis should be useful for other plants such as rice, as many processes are conserved between the two species; however, some rice gene networks associated with specific phenotypes are different to that in Arabidopsis because of high levels of evolutionary diversity between these two model plants (Devos et al ., ).…”