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The bait compatibility index: computational bait selection for interaction proteomics experiments

The bait compatibility index: computational bait selection for interaction proteomics experiments
The bait compatibility index: computational bait selection for interaction proteomics experiments
Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation- based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the “bait compatibility index”, a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.
1535-3893
4972-4981
Saha, Sudipto
77b0a09f-c013-4418-b3b5-659cca46fe9d
Kaur, Parminder
f80876f2-f12c-4579-9b77-71ab7e3569ae
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae
Saha, Sudipto
77b0a09f-c013-4418-b3b5-659cca46fe9d
Kaur, Parminder
f80876f2-f12c-4579-9b77-71ab7e3569ae
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae

Saha, Sudipto, Kaur, Parminder and Ewing, Rob M. (2010) The bait compatibility index: computational bait selection for interaction proteomics experiments. Journal of Proteome Research, 9 (10), 4972-4981. (doi:10.1021/pr100267t).

Record type: Article

Abstract

Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation- based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the “bait compatibility index”, a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.

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Published date: August 2010
Organisations: Faculty of Natural and Environmental Sciences, Centre for Biological Sciences

Identifiers

Local EPrints ID: 340657
URI: http://eprints.soton.ac.uk/id/eprint/340657
ISSN: 1535-3893
PURE UUID: a5e39a24-d4a8-4331-ab24-4e3aec2cc6d5
ORCID for Rob M. Ewing: ORCID iD orcid.org/0000-0001-6510-4001

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Date deposited: 28 Jun 2012 10:06
Last modified: 24 Sep 2019 00:38

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