SAINT: probabilistic scoring of affinity purification-mass spectrometry data

Abstract

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification-mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

Figure 1. Probability model in SAINT

a–b Interaction data in the presence (a) and absence (b) of control purifications. Top: schematic of the experimental AP-MS procedure; Bottom: illustration of a spectral count interaction table. c. Modeling spectral count distributions for true and false interactions. For the interaction between prey i and bait j, SAINT utilizes all relevant data for the two proteins, as shown in the column of the bait (green) and the data in the row of the prey (orange) in a and b. d. Probability is calculated for each replicate by application of Bayes rule, and a summary probability is calculated for the interaction pair (i,j).

Figure 2. Analysis of TIP49 and DUB datasets

a. Benchmarking of filtered interactions in the TIP49 dataset by the overlap with interactions previously reported in BioGRID and iRefWeb databases. b. Co-annotation of interaction partners to common GO terms in Biological Processes in the TIP49 dataset. c. Benchmarking against BioGRID and iRefWeb in the DUB dataset. d. Co-annotation to GO terms in the DUB dataset.