Modeling contaminants in AP-MS/MS experiments.
Identification of protein-protein interactions (PPI) by affinity purification (AP) coupled with tandem mass spectrometry (AP-MS/MS) produces large data sets with high rates of false positives. This is in part because of contamination at the AP level (due to gel contamination, nonspecific binding to the TAP columns in the context of tandem affinity purification, insufficient purification, etc.). In this paper, we introduce a Bayesian approach to identify false-positive PPIs involving contaminants in AP-MS/MS experiments. Specifically, we propose a confidence assessment algorithm (called Decontaminator) that builds a model of contaminants using a small number of representative control experiments. It then uses this model to determine whether the Mascot score of a putative prey is significantly larger than what was observed in control experiments and assigns it a p-value and a false discovery rate. We show that our method identifies contaminants better than previously used approaches and results in a set of PPIs with a larger overlap with databases of known PPIs. Our approach will thus allow improved accuracy in PPI identification while reducing the number of control experiments required.
McGill Centre for Bioinformatics and School of Computer Science, McGill University, Montréal, Canada.
Mathieu
M
Lavallée-Adam
Philippe
P
Cloutier
Benoit
B
Coulombe
Mathieu
M
Blanchette
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002846:Chromatography, Affinity; D008956:Models, Chemical; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D053719:Tandem Mass Spectrometry / Q000379:methods*