August 13, Tue 2011
1:00 pm, MRB 100 Conference Room
Dr. Alexey Nesvizhskii
Center for Computational Medicine and Bioinformatics, University of Michigan Medical School
Computational methods for modeling AP/MS protein-protein interaction data
Affinity purification followed by mass spectrometry (AP/MS) has become a commonly used method for the identification of protein-protein interactions and protein complexes. In this presentation, we will review the common challenges of AP/MS data and present our recent developments in this area. First, we are pursuing several strategies for addressing the problem of false positive interactions. We will describe a novel statistical approach, Significance Analysis of Interactome (SAINT), which utilizes label-free information for assigning a confidence measure to individual interactions. We will also present a novel method for clustering of AP/MS data for improved reconstruction of protein complexes, as well as for detection of quantitative changes in the composition of protein complexes as a function of the cell state. Finally, we are also developing a new resource - the Contaminant Repository for Affinity Purification (CRAP). Our main objective here is to create a central repository to store, annotate, statistically analyze and disseminate lists of protein contaminants likely to be observed in AP/MS studies.