Background proteome correction promotes confident identification of dynamic protein-protein interactions between different biological contexts

Affinity purification-mass spectrometry (AP-MS) is a tool that allows researchers to study how proteins interact with each other within cells. This method has become increasingly popular due to its simplicity and high sensitivity. However, beyond simply observing the interactions between target proteins, scientists are also interested in how these interactions change in response to various biological events, such as disease or the use of small molecules to modify target proteins.

These changes can not only affect the interactions between the primary proteins of interest, but also alter the expression levels of other proteins that are not the main focus of the study. This can cause issues, as the proteins with altered expression may stick more or less to the purification matrix used in AP-MS, potentially leading researchers to mistakenly identify these as interacting with the target protein.

Current methods do not always take into account these differences in the background proteome, which can lead to an increase in both false positive and false negative results.

In this study, the researchers focused on a protein called O-GlcNAc transferase (OGT) and aimed to improve the reliability of identifying dynamic protein-protein interactions (PPIs) using AP-MS. They introduced a technique to add specific tags to OGT within mouse embryonic stem cells (mESCs) and use this modified version of OGT in AP-MS experiments to identify its interacting partners.

The team found that accurately representing the beads used in the purification process, which can vary depending on the type of matrix used, is crucial for correctly identifying true PPIs and avoiding false positives and negatives. This became particularly important when the researchers studied how OGT's interactions changed when it was inhibited via OSMI-4, a molecule known to affect gene expression.

The treatments resulted in different compositions of proteins binding to the beads, leading to variations in the background levels of proteins that appeared as interactions. To address these issues, the researchers introduced straightforward experimental controls and a statistical framework that could account for the differences in background proteomes, allowing for a precise comparison between the treated and control conditions.

By incorporating these considerations into PPI dynamic studies, researchers can improve the accuracy and reproducibility of their findings.