Exploring How Workflow Variations in Denaturation-Based Assays Impact Global Protein-Protein Interaction Predictions

Area of Science:

• Proteomics
• Systems Biology
• Biochemistry

Background:

• Protein-protein interactions (PPIs) are crucial for cellular functions.
• Denaturation-based assays like thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA) are used to map global PPI networks.
• Understanding how different denaturation methods influence PPI network characterization is essential.

Purpose of the Study:

• To experimentally and computationally characterize the effect of denaturation-based assays on PPI network mapping.
• To compare PPI networks identified by TPCA and I-PISA, including overlapping and distinct protein sets.
• To assess the impact of protein properties and subcellular localization on PPI detection within these workflows.

Main Methods:

• Generation of paired TPCA and I-PISA datasets.
• Experimental and computational analysis of soluble and insoluble protein fractions.
• Assessment of protein physical properties (size, complexity, hydrophobicity) and subcellular localization.
• Evaluation of sample amount reduction and alternative quantification methods (label-free DIA vs. TMT DDA).

Main Results:

• Distinct PPI networks are captured by TPCA and I-PISA, influenced by protein properties and localization.
• Insoluble fractions significantly expand the detectable PPI landscape.
• Integrating data from soluble and insoluble fractions enhances network informativeness.
• TPCA performance remains robust with a 500x reduction in sample input.
• Label-free DIA TPCA offers performance comparable to TMT DDA TPCA.

Conclusions:

• Denaturation-based assays provide complementary insights into PPI networks.
• The inclusion of insoluble fractions is critical for comprehensive PPI mapping.
• Workflow optimization, including sample reduction and label-free quantification, offers practical improvements for global PPI network analysis.