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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}
}
  • 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
  • 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
  • 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 distributionsmore » 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.« less
  • Assessment of differential protein abundance from the observed properties of detected peptides is an essential part of protein profiling based on shotgun proteomics. However, the abundance observed for degenerate peptides may be due to contributions from multiple proteins that are affected differently by a given treatment. Excluding degenerate peptides eliminates this ambiguity but may significantly decrease the number of proteins for which abundance estimates can be obtained. Peptide degeneracy within a family of biologically related proteins does not cause ambiguity if family members have a common response to treatment. Based on this concept, we have developed an approach for includingmore » degenerate peptides in the analysis of differential protein abundance in protein profiling. Data from a recent proteomics study of lung tissue from mice exposed to lipopolysaccharide, cigarette smoke, and a combination of these agents is used to illustrate our method. Starting from data where about half of the protein identifications involved degenerate peptides, 82% of the affected proteins were grouped into families, based on FASTA annotation, with closure on peptide degeneracy. In many cases, a common abundance relative to control was sufficient to explain ion-current peak areas for peptides, both unique and degenerate, that identified biologically-related proteins in a peptide-degeneracy closure group. Based on these results, we propose that peptide-degeneracy closure groups provide a way to include abundance data for degenerate-peptides in quantitative protein profiling by high throughput mass spectrometry.« less
  • A simple and effective subcellular proteomic method for fractionation and analysis of gram-negative bacterial cytoplasm, periplasm, inner, and outer membranes was applied to Shewanella oneidensis to gain insight into its subcellular architecture. A combination of differential centrifugation, Sarkosyl solubilization, and osmotic lysis was used to prepare subcellular fractions. Global differences in protein fractions were observed by SDS PAGE and heme staining, and tryptic peptides were analyzed using high-resolution LC-MS/MS. Compared to crude cell lysates, the fractionation method achieved a significant enrichment (average ~2-fold) in proteins predicted to be localized to each subcellular fraction. Compared to other detergent, organic solvent, andmore » density-based methods previously reported, Sarkosyl most effectively facilitated separation of the inner and outer membranes and was amenable to mass spectrometry, making this procedure ideal for probing the subcellular proteome of gram-negative bacteria via LC-MS/MS. With 40% of the observable proteome represented, this study has provided extensive information on both subcellular architecture and relative abundance of proteins in S. oneidensis and provides a foundation for future work on subcellular organization and protein-membrane interactions in other gram-negative bacteria.« less