Journal Article

Structure-based prediction of protein–protein interactions on a genome-wide scale

Thu, Chan Aye, Qiang, Li, Maniatis, Tom, Lefebvre, Celine, Bisikirska, Brygida, Zhang, Qiangfeng Cliff, Petrey, Donald, Califano, Andrea, Hunter, Tony, Deng, Lei, Honig, Barry, Accili, Domenico, Shi, Yu

2012

Overview

Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues. Protein interactions predicted The analysis of protein-interaction networks is essential to an understanding of the regulatory processes in a living cell. Many methods have been developed with a view to predicting protein–protein interactions (PPIs) at a genome-wide level, although the differences obtained using these approaches suggest that there are still factors unaccounted for. Barry Honig and colleagues have developed a new way of predicting PPIs that is based on the proteins' three-dimensional structures and functional data. Tests of several predictions of the new algorithm, known as PREPPI, confirm the accuracy of the results. The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms 1 , 2 . Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification 3 , as well as from manual curation of experiments on individual systems 4 . A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs) 5 , 6 . Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages 7 , 8 , 9 . Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/92/475/2290 / Algorithms / Analytical, structural and metabolic biochemistry / Animals / Bayes Theorem / Biological and medical sciences / Brain - metabolism / Cadherins - metabolism / Comparative studies / Fundamental and applied biological sciences. Psychology / Genomes / Genomics / High-Throughput Screening Assays / Homology (Biology) / Humanities and Social Sciences / Humans / Interactions. Associations / Intermolecular phenomena / Kinases / letter / Matrix Attachment Region Binding Proteins - metabolism / Methods / Mice / Models, Molecular / Molecular and cellular biology / Molecular biophysics / multidisciplinary / Phylogeny / PPAR gamma - metabolism / Protein Binding / Protein Conformation / Protein Interaction Mapping - methods / Protein Interaction Maps / Protein Kinases - chemistry / Protein Kinases - metabolism / Protein-protein interactions / Proteins / Proteins - chemistry / Proteins - metabolism / Proteome - chemistry / Proteome - metabolism / Proteomics - methods / Reproducibility of Results / ROC Curve / Saccharomyces cerevisiae - chemistry / Saccharomyces cerevisiae - metabolism / Science / Structure / Suppressor of Cytokine Signaling Proteins - metabolism / Transcription Factors - metabolism / Yeast / Yeasts