Two new strategies to improve the ranking of proteins identified by affinity purification and mass spectrometry: a case study using for two zinc finger transcription factors, Snai1 and Pogz.

Thursday, October 11, 2012 — Poster Session IV
2:00 p.m. – 4:00 p.m.	Natcher Conference Center, Building 45	NCI	PROTEOM-1

Authors

• T Andresson
• S Das
• A Bosley
• GW Alvord
• O Quinones
• Z Xiao
• X Ye
• T Veenstra

Abstract

A fundamental understanding of the composition of a protein complex is essential to delineate the function of poorly characterized proteins. Affinity purification of a protein of interest followed by LC-MS/MS is commonly used to interrogate protein interactions. However, an inherent problem with this method is the high number of non-specific background proteins which can significantly impeach the identification of true interacting proteins. Here we describe two methods developed to systematically deal with the background. The first method is based on a new statistical approach to mitigate background proteome variability. It utilizes a background database constructed from 18 controls and a beta-binomial probability distribution to rank the identified proteins based on their likelihoods of being true interacting proteins. The second method uses a label free quantitation to determine each protein’s relative abundance profile, following differential expression of the protein of interest, in an attempt to find potential interacting proteins whose abundance mirrors that of the target protein. Any protein that follows the same trend is of interest and is ranked high on the list of potential interacting proteins. Both strategies performed better that the traditional methods of processing affinity purification data when tested on Pogz and Snai1 pull down data sets.

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