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Title: Inferring Protein Associations Using Protein Pulldown Assays

Abstract

Background: One method to infer protein-protein associations is through a “bait-prey pulldown” assay using a protein affinity agent and an LC-MS (liquid chromatography-mass spectrometry)-based protein identification method. False positive and negative protein identifications are not uncommon, however, leading to incorrect inferences. Methods: A pulldown experiment generates a protein association matrix wherein each column represents a sample from one bait protein, each row represents one prey protein and each cell contains a presence/absence association indicator. Our method evaluates the presence/absence pattern across a prey protein (row) with a Likelihood Ratio Test (LRT), computing its p-value with simulated LRT test statistic distributions after a check with simulated binomial random variates disqualified the large sample 2 test. A pulldown experiment often involves hundreds of tests so we apply the false discovery rate method to control the false positive rate. Based on the p-value, each prey protein is assigned a category (specific association, non-specific association, or not associated) and appraised with respect to the pulldown experiment’s goal and design. The method is illustrated using a pulldown experiment investigating the protein complexes of Shewanella oneidensis MR-1. Results: The Monte Carlo simulated LRT p-values objectively reveal specific and ubiquitous prey, as well as potential systematic errors.more » The example analysis shows the results to be biologically sensible and more realistic than the ad hoc screening methods previously utilized. Conclusions: The method presented appears to be informative for screening for protein-protein associations.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
899480
Report Number(s):
PNNL-SA-49349
6504; KP1501021; TRN: US200708%%308
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 2006 Joint Statistical Meetings: Statistics for an Uncertain World: Meeting Global Challenges, August 6-10, Seattle, WA, 5 pages
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; AFFINITY; DESIGN; PROTEINS; STATISTICS; proteins; Monte Carolo; pulldown experiment; Environmental Molecular Sciences Laboratory

Citation Formats

Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Auberry, Deanna L., Borkowski, John J., and Cannon, William R.. Inferring Protein Associations Using Protein Pulldown Assays. United States: N. p., 2007. Web.
Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Auberry, Deanna L., Borkowski, John J., & Cannon, William R.. Inferring Protein Associations Using Protein Pulldown Assays. United States.
Sharp, Julia L., Anderson, Kevin K., Daly, Don S., Auberry, Deanna L., Borkowski, John J., and Cannon, William R.. Thu . "Inferring Protein Associations Using Protein Pulldown Assays". United States. doi:.
@article{osti_899480,
title = {Inferring Protein Associations Using Protein Pulldown Assays},
author = {Sharp, Julia L. and Anderson, Kevin K. and Daly, Don S. and Auberry, Deanna L. and Borkowski, John J. and Cannon, William R.},
abstractNote = {Background: One method to infer protein-protein associations is through a “bait-prey pulldown” assay using a protein affinity agent and an LC-MS (liquid chromatography-mass spectrometry)-based protein identification method. False positive and negative protein identifications are not uncommon, however, leading to incorrect inferences. Methods: A pulldown experiment generates a protein association matrix wherein each column represents a sample from one bait protein, each row represents one prey protein and each cell contains a presence/absence association indicator. Our method evaluates the presence/absence pattern across a prey protein (row) with a Likelihood Ratio Test (LRT), computing its p-value with simulated LRT test statistic distributions after a check with simulated binomial random variates disqualified the large sample 2 test. A pulldown experiment often involves hundreds of tests so we apply the false discovery rate method to control the false positive rate. Based on the p-value, each prey protein is assigned a category (specific association, non-specific association, or not associated) and appraised with respect to the pulldown experiment’s goal and design. The method is illustrated using a pulldown experiment investigating the protein complexes of Shewanella oneidensis MR-1. Results: The Monte Carlo simulated LRT p-values objectively reveal specific and ubiquitous prey, as well as potential systematic errors. The example analysis shows the results to be biologically sensible and more realistic than the ad hoc screening methods previously utilized. Conclusions: The method presented appears to be informative for screening for protein-protein associations.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 01 00:00:00 EST 2007},
month = {Thu Feb 01 00:00:00 EST 2007}
}

Conference:
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  • 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 interactingmore » 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.« less
  • 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 interactingmore » proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. The algorithm can discriminate against a background of prey proteins that are detected in association with a large number of baits as an artifact of the measurement. 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.« less
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