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. 2011 Feb 4;10(2):886-95.
doi: 10.1021/pr100795z. Epub 2010 Dec 31.

Modeling contaminants in AP-MS/MS experiments

Mathieu Lavallée-Adam  1 , Philippe Cloutier, Benoit Coulombe, Mathieu Blanchette
Affiliations

Modeling contaminants in AP-MS/MS experiments

Mathieu Lavallée-Adam et al. J Proteome Res. .

Abstract

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.

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Figures

Figure 1
Figure 1
Decontaminator workflow. First, control and induced experiments are pooled to build a noise model for each. These noise models are then used to assign a p-value to each prey obtained upon the induction of a bait. Finally, a false discovery rate is calculated for each p-value.
Figure 2
Figure 2
Plots of both the posterior distributions of M¯pNI and posterior distributions of M¯b,pI for three different interactions. The blue curve is the posterior distribution of M¯pNI and the red curve is the posterior distribution of M¯b,pI. Orange X’s positions on the x-axis are observed Mascot scores of the prey in control experiments. When the prey was not detected for a given control, an X is drawn on the x-axis close to the origin. The blue X’s position on the x-axis is the Mascot score of the prey in the induced experiment for the corresponding bait. (a) RPAP3-POLR2E interaction is an example of an interaction considered valid by the algorithm. (b) RPAP3-SAMD1 interaction is an example of an interaction where the prey is considered as a contaminant. (c) KPNA2-ACTB is an example of an interaction that is predicted as positive, even though ACTB is often observed as a contaminant (but with lower Mascot scores) in control experiments.
Figure 3
Figure 3
Cumulative distributions of the FDRs obtained from Decontaminator and the Z-score approaches. Each curve shows the number of interactions that can be predicted positive, as a function of the false discovery rate tolerated.
Figure 4
Figure 4
Cumulative distributions of the FDRs obtained from Decontaminator with 14, 12, 10, 8, and 6 controls. For sets of controls of size smaller than 14, we show the average cumulative distributions over 100 randomly selected subsets of controls.
Figure 5
Figure 5
Fraction of positive predictions present in the HPRD and BioGrid merged databases (y-axis), for a varying number of predicted interactions (x-axis) by six filtering methods (MRatio, MScore, Z-score, SAINT without hub labeling, SAINT with hub labeling and Decontaminator).
Figure 6
Figure 6
Fraction of positively predicted PPIs for which the interacting partners share at least a 10%-specific GO term (y-axis), for a given number of predicted interactions (x-axis) by six filtering methods (MRatio, MScore, Z-score, SAINT without hub labeling, SAINT with hub labeling and Decontaminator).
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