The Bait Compatibility Index: Computational Bait Selection for Interaction Proteomics Experiments

Abstract

Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the "bait compatibility index", a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.