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Title: Statistically Inferring Protein-Protein Associations with Affinity Isolation LC-MS/MS Assays

Abstract

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes Odds estimation. We then applied our LRT-Bayes algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. We conclude that the experimental protocol including the LRT-Bayes algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.

Authors:
 [1];  [2];  [2];  [3];  [4];  [2];  [3];  [3];  [3];  [2];  [2];  [2];  [3];  [2];  [2]
  1. Montana State University
  2. Pacific Northwest National Laboratory (PNNL)
  3. ORNL
  4. {Greg} B [ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
931196
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Proteome Research; Journal Volume: 6; Journal Issue: 9
Country of Publication:
United States
Language:
English
Subject:
protein-protein interaction affinity isolation LC-MS/MS likelihood

Citation Formats

Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Pelletier, Dale A, Hurst, Gregory, Cannon, Bill, Auberry, Deanna L, Schmoyer, Denise D, McDonald, W Hayes, White, Amanda M., Hooker, Brian, Victry, Kristin D, Buchanan, Michelle V, Kerry, Vladimir, and Wiley, Steven. Statistically Inferring Protein-Protein Associations with Affinity Isolation LC-MS/MS Assays. United States: N. p., 2007. Web. doi:10.1021/pr0701106.
Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Pelletier, Dale A, Hurst, Gregory, Cannon, Bill, Auberry, Deanna L, Schmoyer, Denise D, McDonald, W Hayes, White, Amanda M., Hooker, Brian, Victry, Kristin D, Buchanan, Michelle V, Kerry, Vladimir, & Wiley, Steven. Statistically Inferring Protein-Protein Associations with Affinity Isolation LC-MS/MS Assays. United States. doi:10.1021/pr0701106.
Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Pelletier, Dale A, Hurst, Gregory, Cannon, Bill, Auberry, Deanna L, Schmoyer, Denise D, McDonald, W Hayes, White, Amanda M., Hooker, Brian, Victry, Kristin D, Buchanan, Michelle V, Kerry, Vladimir, and Wiley, Steven. Mon . "Statistically Inferring Protein-Protein Associations with Affinity Isolation LC-MS/MS Assays". United States. doi:10.1021/pr0701106.
@article{osti_931196,
title = {Statistically Inferring Protein-Protein Associations with Affinity Isolation LC-MS/MS Assays},
author = {Sharp, Julia L. and Anderson, Kevin K. and Daly, Don S. and Pelletier, Dale A and Hurst, Gregory and Cannon, Bill and Auberry, Deanna L and Schmoyer, Denise D and McDonald, W Hayes and White, Amanda M. and Hooker, Brian and Victry, Kristin D and Buchanan, Michelle V and Kerry, Vladimir and Wiley, Steven},
abstractNote = {Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes Odds estimation. We then applied our LRT-Bayes algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. We conclude that the experimental protocol including the LRT-Bayes algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.},
doi = {10.1021/pr0701106},
journal = {Journal of Proteome Research},
number = 9,
volume = 6,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}