SAINT-MS1: Protein–Protein Interaction Scoring Using Label-free Intensity Data in Affinity Purification-Mass Spectrometry Experiments

We present a statistical method SAINT-MS1 for scoring
protein–protein
interactions based on the label-free MS1 intensity data from affinity
purification-mass spectrometry (AP-MS) experiments. The method is
an extension of Significance Analysis of INTeractome (SAINT), a model-based
method previously developed for spectral count data. We reformulated
the statistical model for log-transformed intensity data, including
adequate treatment of missing observations, that is, interactions
identified in some but not all replicate purifications. We demonstrate
the performance of SAINT-MS1 using two recently published data sets:
a small LTQ-Orbitrap data set with three replicate purifications of
single human bait protein and control purifications and a larger drosophila
data set targeting insulin receptor/target of rapamycin signaling
pathway generated using an LTQ-FT instrument. Using the drosophila
data set, we also compare and discuss the performance of SAINT analysis
based on spectral count and MS1 intensity data in terms of the recovery
of orthologous and literature-curated interactions. Given rapid advances
in high mass accuracy instrumentation and intensity-based label-free
quantification software, we expect that SAINT-MS1 will become a useful
tool allowing improved detection of protein interactions in label-free
AP-MS data, especially in the low abundance range.