Large-scale De Novo Prediction of Physical Protein-Protein Association

Abstract

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future.Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genomewide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular.

In silico prediction of physical protein interactions and characterization of interactome orphans

“…proteins (fewer than five known interactions). We also used these data sets to evaluate four other prediction methods 11,12,15,16 . We determined the overlaps between top predictions of each method, excluding any PPIs used in training, and these six reference sets (Fig.…”

Protein-protein interaction networks: probing disease mechanisms using model systems

“…Nevertheless, experimentally confirmed PPIs stemming from two-hybrid system, AP-MS and small-scale interaction studies account for less than 25% of all human PPIs predicted by certain sources [83]. This gap in knowledge has motivated the development of innovative computational procedures for de novo prediction of PPIs, which are not based on direct experimental evidence.…”

Structural Bioinformatics of the Interactome

“…In general, combining weak evidence for membership in an interactome can result in more successful predictors. Such approaches include Bayesian classifiers (18, 81, 112, 118, 194, 195), decision tree approaches (169) support vector machines (SVMs) (34, 101, 140), random forest classifiers (26, 27, 45), and a host of other techniques (13, 20, 74, 122, 126, 187). …”