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

Abstract

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification3, as well as from manual curation of experiments on individual systems4. 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 stages7–9. 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, PrePPI, that 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 significant 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.

A Systems Approach to Drug Discovery in Alzheimer's Disease

“…Over the last few years, we have pioneered computational and experimental methods for the accurate dissection of tissue-and cell-specific molecular interaction networks, including those controlling transcriptional(protein-DNA) [10,11,16,17], post-transcriptional(RNA-RNA and protein-RNA) [18][19][20][21], signal transduction and other posttranslational processes protein-protein interactions (PPI and metabolic) [22][23][24][25][26], and drug interactions [27][28][29][30]. These methods and datasets allow for the reconstruction of the regulatory and signaling logic of specific cell types by combining specific knowledge about regulatory mechanisms (e.g.…”

“…Following reverse engineering analysis with these algorithms, each TF signaling protein and microRNA represented in an interactome is causally associated with a regulon containing dozens to hundreds of highly accurate and cell-context-specific targets and substrates. For instance, predictions by the ARACNe, MINDy, PrePPI algorithms have been typically validated at a very high rate (~70%) by chromatin immunoprecipitation, gene expression profiling following silencing, coimmunoprecipitation and other relevant assays [12,19,24,25,34,35]. Other systems approaches have also been applied successfully in AD [36][37][38][39], and our focus here is to maintain clarity rather than review the entire literature.…”

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

“…Structural information has been used successfully to validate or improve large-scale PPI networks (Prieto and De Las Rivas, 2010;Zhang et al, 2012). Our analysis demonstrated that structural evidence could increase the reliability of predicted PPI networks by reducing false positives from the large amount of data regarding noninteracting protein pairs at the genome-wide scale (Supplemental Fig.…”

“…Protein docking is a promising method for discovering protein interactions that is based on three-dimensional structural information, but it remains a challenging and computationally demanding task to predict PPIs on a genome-wide scale (Wass et al, 2011). Alternatively, knowledge-based methods that utilize the structural similarity of protein pairs to interface a known protein complex have been used in PPI prediction (Aytuna et al, 2005;Zhang et al, 2012;Mosca et al, 2013). The common strategy of these structure-based methods is to find a suitable template complex for the two query protein structures; the prediction is then based on the structural similarity of the two protein models to the template complex.…”

Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale

“…Rhodes et al used a naive Bayes model that combines information for ortholog interaction, co-expression, shared gene ontology terms, and enriched domaindomain interaction pairs to predict novel interactions in human [30]. Zhang et al showed that 3D structural information for protein complexes and protein monomers can be very helpful in the prediction of PPIs, when combined with non-structural-based methods [31]. With the use of only protein sequence evolutionary coupling information derived from carefully generated multiple sequence alignments, interactions can be predicted with high accuracy and at the resolution of single residues [32 ].…”