SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass...

Area of Science:

• Proteomics
• Biochemistry
• Bioinformatics

Background:

• Affinity purification-mass spectrometry (AP-MS) is crucial for identifying protein interactions.
• Existing methods often rely on spectral count data, which may not fully leverage modern mass spectrometry capabilities.
• Label-free quantification using MS1 intensity offers a more sensitive approach.

Purpose of the Study:

• To introduce SAINT-MS1, a novel statistical method for scoring protein-protein interactions.
• To adapt the Significance Analysis of INTeractome (SAINT) method for label-free MS1 intensity data.
• To improve the detection of protein interactions in AP-MS experiments, particularly for low-abundance proteins.

Main Methods:

• Developed a statistical model for log-transformed MS1 intensity data.
• Incorporated methods to handle missing observations across replicate purifications.
• Extended the existing SAINT (Significance Analysis of INTeractome) framework.

Main Results:

• Demonstrated SAINT-MS1 performance on human and Drosophila AP-MS datasets.
• Showcased improved recovery of orthologous and literature-curated interactions compared to spectral count-based SAINT.
• Validated the method's utility with both LTQ-Orbitrap and LTQ-FT generated data.

Conclusions:

• SAINT-MS1 provides a robust statistical framework for analyzing label-free AP-MS data.
• The method enhances the detection of protein-protein interactions, especially in the low abundance range.
• SAINT-MS1 is expected to be a valuable tool for proteomics research given advancements in mass spectrometry.