Abstract
BackgroundThe large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.ResultsIn this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.ConclusionWe conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.
Highlights
The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes
ENTS performance relative to experimental predictions We assessed the performance of ENTS by calculating the area underneath the ROC curve (AUC) for testing data consisting of no overlap with the training data at the level of protein interaction and no overlap with any protein pairs used to calculate pairwise domain LOD scores
ENTS performance relative to other classifiers We obtained whole-genome predictions of PPIs for the organisms on which the classifiers were trained (i.e., S. cerevisiae, H. sapiens and A. thaliana), as well as for species that were not used in training the predictors
Summary
The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. Despite the biological importance of PPIs and the availability of high-throughput screening methods in recent years, experimentally-verified PPI networks remain sparsely populated, especially with respect to the amount of sequence data currently available. High throughput approaches such as automated yeast twohybrid screens and tandem affinity purification/mass spectrometry have detected thousands of binary PPIs in animal and fungal model organisms such as Homo sapiens [2], Saccharomyces cerevisiae [3], and Drosophila melanogaster [4], yet the current size of the interactome belonging to the experimental workhorse of the plant kingdom, Arabidopsis thaliana, only constitutes approximately 3% of its expected size [5]. Other methods rely on an ensemble of functional data, such as genome-wide measures of co-expression and colocalization, which is often not available for non-model organisms
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