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Computational framework for analysis of prey–prey associations in interaction proteomics identifies novel human protein–protein interactions and networks

Computational framework for analysis of prey–prey associations in interaction proteomics identifies novel human protein–protein interactions and networks
Computational framework for analysis of prey–prey associations in interaction proteomics identifies novel human protein–protein interactions and networks
Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.
protein-protein interaction network, affinity purification-mass spectrometry, interactome
1535-3893
4476-4487
Saha, Sudipto
77b0a09f-c013-4418-b3b5-659cca46fe9d
Dazard, Jean-Eudes
f8129953-4050-4766-bb43-9d1fcdc59f9f
Xu, Hua
69806e6d-71bb-485e-89ef-7221ab8f8bc2
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae
Saha, Sudipto
77b0a09f-c013-4418-b3b5-659cca46fe9d
Dazard, Jean-Eudes
f8129953-4050-4766-bb43-9d1fcdc59f9f
Xu, Hua
69806e6d-71bb-485e-89ef-7221ab8f8bc2
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae

Saha, Sudipto, Dazard, Jean-Eudes, Xu, Hua and Ewing, Rob M. (2012) Computational framework for analysis of prey–prey associations in interaction proteomics identifies novel human protein–protein interactions and networks. Journal of Proteome Research, 11 (9), 4476-4487. (doi:10.1021/pr300227y).

Record type: Article

Abstract

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.

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Accepted/In Press date: 30 July 2012
e-pub ahead of print date: 30 July 2012
Published date: 7 September 2012
Keywords: protein-protein interaction network, affinity purification-mass spectrometry, interactome
Organisations: Centre for Biological Sciences

Identifiers

Local EPrints ID: 345230
URI: http://eprints.soton.ac.uk/id/eprint/345230
ISSN: 1535-3893
PURE UUID: 449474eb-7123-40db-9761-35ef6f8af3d3
ORCID for Rob M. Ewing: ORCID iD orcid.org/0000-0001-6510-4001

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Date deposited: 14 Nov 2012 11:08
Last modified: 15 Mar 2024 03:44

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Contributors

Author: Sudipto Saha
Author: Jean-Eudes Dazard
Author: Hua Xu
Author: Rob M. Ewing ORCID iD

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