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

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 tha...

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Autores principales: Maheshwari, Surabhi, Brylinski, Michal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427563/
https://www.ncbi.nlm.nih.gov/pubmed/28499419
http://dx.doi.org/10.1186/s12859-017-1675-z
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author Maheshwari, Surabhi
Brylinski, Michal
author_facet Maheshwari, Surabhi
Brylinski, Michal
author_sort Maheshwari, Surabhi
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1675-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-54275632017-05-15 Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks Maheshwari, Surabhi Brylinski, Michal BMC Bioinformatics Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1675-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-12 /pmc/articles/PMC5427563/ /pubmed/28499419 http://dx.doi.org/10.1186/s12859-017-1675-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Maheshwari, Surabhi
Brylinski, Michal
Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5427563/
https://www.ncbi.nlm.nih.gov/pubmed/28499419
http://dx.doi.org/10.1186/s12859-017-1675-z
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