Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Abstract

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein-protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006;Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct

Protein Interactome and Its Application to Protein Function Prediction

“…In other words, their method can be easily applied, not only to yeast but to other species with less known localization and/or interaction information. Actually they predicted subcellular localization of fly, human, and Arabidopsis (Lee et al, 2008;Lee et al, 2010b) using protein interactions. The results of both works showed that the prediction worked well for the other organisms and could find real localizations of some unknown proteins (Figures 6-7).…”

“…Generated models for the location prediction for Fly (A), Human (B), and Arabidopsis (C) (adapted from Lee et al, 2008 andLee et al, 2010b). …”

“…Lee et al curated IntAct Arabidopsis thaliana PPI dataset (Aranda et al, 2010) using the DCkNN method, which was proposed before and which showed good performance (Lee et al, 2010b). They also showed that the DC-kNN is applicable to other organisms.…”

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

“…According to an empirical estimation of the size of the protein interactome, experimentally determined interactions represent a small fraction (approximately 6%) of the entire protein interactome, and the relationships among most proteins remain to be discovered (Arabidopsis Interactome Mapping Consortium, 2011). To complement existing experimental resources, genomewide plant PPI networks have been developed by computational approaches (Cui et al, 2008;Lee et al, 2010;Lin et al, 2011;Wang et al, 2012Wang et al, , 2014. However, these methods attempt to predict PPIs using only nonstructural information.…”

Proteome-wide remodeling of protein location and function by stress

“…(31). Previously, we observed that the co-occurrence of sequence, structure, or function between a protein and its interacting partners is a strong predictor of joint subcellular location (32). We applied forward selection to choose feasible feature sets of high predictive power from the pool of generated individual and network protein features, with a network neighborhood restricted to nearest neighbors within distance 2, using a divide-and-conquer k-nearest-neighbor method (Fig.…”