Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

BMC Bioinformatics
Surabhi Maheshwari, Michal Brylinski

Abstract

Deciphering 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. In 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 confid...Continue Reading

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Citations

Dec 9, 2017·Current Protocols in Bioinformatics·Max KotlyarIgor Jurisica
Nov 9, 2018·EcoSal Plus·Peter D KarpIan Paulsen

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Datasets Mentioned

BETA
RND14944

Methods Mentioned

BETA
X-ray
NMR
interaction prediction
structure-based prediction
nuclear translocation
structure-based predictions

Software Mentioned

ZDOCK
e Thread
Rank
ModRefiner
FindSite
Rank PPI
PPI
SimDock
Modeller
MEGADOCK

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