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

## TL;DR

This study compares different methods for mapping protein interactions and shows how adjusting workflows can improve results, especially when working with small samples.

## Contribution

The study introduces optimized low-sample workflows and highlights the value of insoluble fractions in capturing unique protein interactions.

## Key findings

- Insoluble fractions in TPCA and I-PISA workflows reveal unique protein interaction populations.
- Label-free DIA TPCA performs as well as traditional TMT DDA workflows with much less sample input.
- Influenza A infection-driven changes in protein interactions are better captured by combining soluble and insoluble workflow data.

## Abstract

Protein denaturation-based assays, such as thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA), are powerful tools for characterizing global protein–protein interaction (PPI) networks. These workflows utilize different denaturation methods to probe PPIs, i.e., thermal- or ion-based. How denaturation differences influence PPI network mapping remained to be better understood. Here, we provide an experimental and computational characterization of the effect of the denaturation-based PPI assay on the observed PPI networks. We establish the value of both soluble and insoluble fractions in PPI prediction, determine the ability to minimize sample amount requirement, and assess different relative quantification methods during virus infection. Generating paired TPCA and I-PISA datasets, we define both overlapping sets of proteins and distinct PPI networks specifically captured by these methods. Assessing protein physical properties and subcellar localizations, we show that size, structural complexity, hydrophobicity, and localization influence PPI detection in a workflow-specific manner. We show that the insoluble fractions expand the detectable PPI landscape, underscoring their value in these workflows. Focusing on selected PPI networks (cytoskeletal and DNA repair), we observe the detection of distinct functional populations. Using influenza A infection as a model for cellular perturbation, we demonstrate that the integration of PPI predictions from soluble and insoluble workflows enhances the ability to build biologically informative and interconnected networks. Examining the effects of reducing starting material for TPCA assays, we find that PPI prediction quality remains robust when using a single well of a 96-well plate, a ∼500× reduction in sample input from usual workflows. Introducing simple workflow modifications, we show that label-free data-independent acquisition (DIA) TPCA yields performance comparable to the traditional tandem mass tag (TMT) data-dependent acquisition (DDA) TPCA workflow. This work provides insights into denaturation-based assays, highlights the value of insoluble fractions, and offers practical improvements for enhancing global PPI network mapping.

•Identify differences in physical properties of TPCA and I-PISA detected PPIs.•Insoluble fractions in TPCA and I-PISA provide access to unique PPI populations.•Optimize a DIA TPCA workflow for small sample amounts.•Influenza A-driven PPI remodeling captured by soluble and insoluble TPCA workflows.

Identify differences in physical properties of TPCA and I-PISA detected PPIs.

Insoluble fractions in TPCA and I-PISA provide access to unique PPI populations.

Optimize a DIA TPCA workflow for small sample amounts.

Influenza A-driven PPI remodeling captured by soluble and insoluble TPCA workflows.

Here, we explore differences in denaturation-based assays for characterizing global protein–protein interaction (PPI) networks, assessing thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA) workflows. Specific physical properties of proteins are found to be influential for capturing divergent PPI networks. We establish that, apart from soluble fractions, the insoluble fractions obtained from these workflows access unique PPI populations. We optimize a soluble and insoluble TPCA workflow for low sample amount, applied to characterizing influenza A infection-driven PPI network remodeling.