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Efficient prediction of human protein-protein interactions at a global scale

BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a glob...

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Autores principales: Schoenrock, Andrew, Samanfar, Bahram, Pitre, Sylvain, Hooshyar, Mohsen, Jin, Ke, Phillips, Charles A, Wang, Hui, Phanse, Sadhna, Omidi, Katayoun, Gui, Yuan, Alamgir, Md, Wong, Alex, Barrenäs, Fredrik, Babu, Mohan, Benson, Mikael, Langston, Michael A, Green, James R, Dehne, Frank, Golshani, Ashkan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272565/
https://www.ncbi.nlm.nih.gov/pubmed/25492630
http://dx.doi.org/10.1186/s12859-014-0383-1
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author Schoenrock, Andrew
Samanfar, Bahram
Pitre, Sylvain
Hooshyar, Mohsen
Jin, Ke
Phillips, Charles A
Wang, Hui
Phanse, Sadhna
Omidi, Katayoun
Gui, Yuan
Alamgir, Md
Wong, Alex
Barrenäs, Fredrik
Babu, Mohan
Benson, Mikael
Langston, Michael A
Green, James R
Dehne, Frank
Golshani, Ashkan
author_facet Schoenrock, Andrew
Samanfar, Bahram
Pitre, Sylvain
Hooshyar, Mohsen
Jin, Ke
Phillips, Charles A
Wang, Hui
Phanse, Sadhna
Omidi, Katayoun
Gui, Yuan
Alamgir, Md
Wong, Alex
Barrenäs, Fredrik
Babu, Mohan
Benson, Mikael
Langston, Michael A
Green, James R
Dehne, Frank
Golshani, Ashkan
author_sort Schoenrock, Andrew
collection PubMed
description BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-42725652015-01-02 Efficient prediction of human protein-protein interactions at a global scale Schoenrock, Andrew Samanfar, Bahram Pitre, Sylvain Hooshyar, Mohsen Jin, Ke Phillips, Charles A Wang, Hui Phanse, Sadhna Omidi, Katayoun Gui, Yuan Alamgir, Md Wong, Alex Barrenäs, Fredrik Babu, Mohan Benson, Mikael Langston, Michael A Green, James R Dehne, Frank Golshani, Ashkan BMC Bioinformatics Research Article BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users. BioMed Central 2014-12-10 /pmc/articles/PMC4272565/ /pubmed/25492630 http://dx.doi.org/10.1186/s12859-014-0383-1 Text en © Schoenrock et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
Schoenrock, Andrew
Samanfar, Bahram
Pitre, Sylvain
Hooshyar, Mohsen
Jin, Ke
Phillips, Charles A
Wang, Hui
Phanse, Sadhna
Omidi, Katayoun
Gui, Yuan
Alamgir, Md
Wong, Alex
Barrenäs, Fredrik
Babu, Mohan
Benson, Mikael
Langston, Michael A
Green, James R
Dehne, Frank
Golshani, Ashkan
Efficient prediction of human protein-protein interactions at a global scale
title Efficient prediction of human protein-protein interactions at a global scale
title_full Efficient prediction of human protein-protein interactions at a global scale
title_fullStr Efficient prediction of human protein-protein interactions at a global scale
title_full_unstemmed Efficient prediction of human protein-protein interactions at a global scale
title_short Efficient prediction of human protein-protein interactions at a global scale
title_sort efficient prediction of human protein-protein interactions at a global scale
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272565/
https://www.ncbi.nlm.nih.gov/pubmed/25492630
http://dx.doi.org/10.1186/s12859-014-0383-1
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