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

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• 作者：Julia L. Sharp ; Kevin K. Anderson ; Gregory B. Hurst ; Don S. Daly ; Dale A. Pelletier ; William R. Cannon ; Deanna L. Auberry ; Denise D. Schmoyer ; W. Hayes McDonald ; Amanda M. White ; Brian S. Hooker ; Kris
• 刊名：Journal of Proteome Research
• 出版年：2007
• 出版时间：September 2007
• 年：2007
• 卷：6
• 期：9
• 页码：3788 - 3795
• 全文大小：163K
• 年卷期：v.6,no.9(September 2007)
• ISSN：1535-3907

文摘

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasinglypopular approach for mapping protein interactions. However, systematic and random assay errorsfrom 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 authenticinteractions 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 experimentallyusing data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, inconjunction with the experimental protocol, inferred with high confidence authentic interacting proteinsfrom 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 associationwith a large number of baits as an artifact of the measurement. We conclude that the experimentalprotocol including the LRT-Bayes' algorithm produces results with high confidence but moderatesensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modelingassumptions, estimating parameters, and evaluating the significance of results in protein associationstudies.