Bootstrapping the Interactome: Unsupervised Identification of Protein Complexes in Yeast

Abstract

Protein interactions and complexes are major components of biological systems. Recent genome-wide applications of tandem affinity purification (TAP) in yeast have increased significantly the available information on such interactions. From these experiments, protein complexes were predicted with different approaches first from the individual experiments only and later from their combination. The resulting predictions showed surprisingly little agreement and all of the corresponding methods rely on additional training data. In this article, we present an unsupervised algorithm for the identification of protein complexes which is independent of the availability of additional complex information. Based on a bootstrap approach, we calculated intuitive confidence scores for interactions which are more accurate than previous scoring metrics. The complexes determined from this confidence network are of similar quality as the complexes identified by the best supervised approaches. Despite the similar quality of the latest predictions and our predictions, considerable differences are still observed between all of them. Nevertheless, the set of consistently identified complexes is more than four times as large as for the first two studies. Our results illustrate that meaningful and reliable complexes can be determined from the purification experiments alone. As a consequence, the approach presented in this article is easily applicable to large-scale TAP experiments for any organism.