Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks
Abstract Background 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 s...
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doaj-20bb921261a74a06ae9e6a3bf306bdab2020-11-25T01:01:55ZengBMCBMC Bioinformatics1471-21052017-05-0118111410.1186/s12859-017-1675-zAcross-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networksSurabhi Maheshwari0Michal Brylinski1Department of Biological Sciences, Louisiana State UniversityDepartment of Biological Sciences, Louisiana State UniversityAbstract Background 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. Results 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 confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. Conclusions Protein 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.http://link.springer.com/article/10.1186/s12859-017-1675-zProtein-protein interactionsProtein dockingStructural bioinformaticsMachine learningGene Ontology filterseFindSitePPI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Surabhi Maheshwari Michal Brylinski |
spellingShingle |
Surabhi Maheshwari Michal Brylinski Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks BMC Bioinformatics Protein-protein interactions Protein docking Structural bioinformatics Machine learning Gene Ontology filters eFindSitePPI |
author_facet |
Surabhi Maheshwari Michal Brylinski |
author_sort |
Surabhi Maheshwari |
title |
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
title_short |
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
title_full |
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
title_fullStr |
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
title_full_unstemmed |
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
title_sort |
across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2017-05-01 |
description |
Abstract Background 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. Results 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 confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. Conclusions Protein 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. |
topic |
Protein-protein interactions Protein docking Structural bioinformatics Machine learning Gene Ontology filters eFindSitePPI |
url |
http://link.springer.com/article/10.1186/s12859-017-1675-z |
work_keys_str_mv |
AT surabhimaheshwari acrossproteomemodelingofdimerstructuresforthebottomupassemblyofproteinproteininteractionnetworks AT michalbrylinski acrossproteomemodelingofdimerstructuresforthebottomupassemblyofproteinproteininteractionnetworks |
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