pr300227y_si_006.xls (88 kB)
Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks
dataset
posted on 2012-09-07, 00:00 authored by Sudipto Saha, Jean-Eudes Dazard, Hua Xu, Rob M. EwingLarge-scale protein–protein interaction data sets
have been
generated for several species including yeast and human and have enabled
the identification, quantification, and prediction of cellular molecular
networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent
methodology for large-scale analysis of protein complexes, performed
by immunopurifying a specific “bait” protein and its
associated “prey” proteins. The analysis and interpretation
of AP-MS data sets is, however, not straightforward. In addition,
although yeast AP-MS data sets are relatively comprehensive, current
human AP-MS data sets only sparsely cover the human interactome. Here
we develop a framework for analysis of AP-MS data sets that addresses
the issues of noise, missing data, and sparsity of coverage in the
context of a current, real world human AP-MS data set. Our goal is
to extend and increase the density of the known human interactome
by integrating bait–prey and cocomplexed preys (prey–prey
associations) into networks. Our framework incorporates a score for
each identified protein, as well as elements of signal processing
to improve the confidence of identified protein–protein interactions.
We identify many protein networks enriched in known biological processes
and functions. In addition, we show that integrated bait–prey
and prey–prey interactions can be used to refine network topology
and extend known protein networks.