Abstract
We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus 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 improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome.
Highlights
Mapping the human protein interactome has important implications for understanding basic biology and human disease at the molecular level [1]
The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information
The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally
Summary
Mapping the human protein interactome has important implications for understanding basic biology and human disease at the molecular level [1]. High-throughput (HT) experimental techniques such as yeast two-hybrid and tandem affinity purification have been developed and applied to discover protein-protein interactions (PPIs) in multiple organisms on a genomewide scale [2]. These approaches have inherent limitations, leading to a substantial false positive rate [2, 3] with many interactions likely undiscovered due to high rates of false negatives [2, 4, 5]. The development of reliable computational approaches to identify PPIs is an important alternative to HT experimental techniques [6, 7]. Interactions determined by HT experiments and computationally have been deposited in databases such as STRING [14] and PrePPI [13]
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