Abstract
BackgroundDeciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.ResultsIn this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway.ConclusionsProtein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.
Highlights
Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms
This study shows that combining computational modeling, structural bioinformatics, machine learning, and function annotation provides a powerful methodology for the bottom-up assembly of protein-protein interaction networks
A probability score reported by the Random Forest Classifier (RFC) is used together with annotation filters according to Gene Ontology terms (Fig. 1f) to make the final decision whether or not the constructed dimer is biologically relevant (Fig. 1g)
Summary
Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. Protein-protein interactions (PPIs) are ubiquitous and play crucial roles in all biological processes within and between cells by mediating signaling pathways in cellular networks and controlling intracellular communication [1]. Since complex biological systems are governed by sophisticated networks of PPIs, associations between proteins determine the behavior of the cell. Genome-sequencing projects provide comprehensive datasets of biological sequences and numerous postgenomic projects are largely focused on the exploration and analysis of PPIs across proteomes [2, 3]. The number of possible PPIs in an organism can be scaled as the square of the total number of monomeric proteins, yielding an estimated number of disparate protein complexes in the order of millions. High-throughput approaches allow the large-scale detection of protein-interaction partners in many organisms. The PPI data is being produced at a swift pace, the major issues in using
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