RESEARCH ARTICLE Predicting Peptide-Mediated Interactions on a Genome-Wide Scale

We describe a method to predict protein-protein interactions (PPIs) formed between struc-tured domains and short peptide motifs. We take an integrative approach based on consen-sus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly im-proved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a struc-ture-based PPI prediction method that, so far, has been limited to interactions formed be-tween two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome. Author Summary Complexes formed between a structured domain on one protein and an unstructured pep-tide on another are ubiquitous. However, they are often quite difficult to detect experimen-tally. The development of computational approaches to predict domain-motif interaction