PXD068620-1

PXD068620 is an original dataset announced via ProteomeXchange.

Title	Exploring How Workflow Variations in Denaturation-Based Assays Impact Global Protein-Protein Interaction Predictions (Influenza A Data)
Description	Denaturation-based assays such as thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA) are powerful tools for mapping global protein-protein interaction (PPI) networks. These workflows utilize different denaturation methods to probe PPIs, however, how these differences influence which PPIs are detected has remined unexplored. Here, we generated paired TPCA and I-PISA datasets, for the first time considering both the soluble and insoluble fractions generated by these methods, to investigate differences in PPI network predictions. While both workflows detected highly overlapping sets of proteins, they identified distinct PPI networks. Utilizing sequence-predicted protein physical properties we show that and subcellar localizations of proteins, we show that protein properties such as size, structural complexity, hydrophobicity, and localization appear influence which workflows detect which PPIs. Notably, insoluble fractions provided unique insights, expanding the detectable PPI landscape and underscoring their value in proteomics workflows. Through analyzing differentially detected PPIs within a small cytoskeleton related PPI network, we show that these workflows may be detecting distinct functional populations for any given protein. Furthermore, we show that by integrating PPI predictions from multiple workflows more biologically informative and interconnected networks can be constructed. We also examined the effects of reducing starting material and using a label-free data-independent acquisition (DIA) TPCA workflow on PPI prediction quality. Despite a ~500x reduction in sample input, PPI prediction quality remained robust, demonstrating the feasibility of TPCA in sample-limited contexts, such as rare cell types. Additionally, we show that, with some simple modifications, label-free DIA TPCA workflow can yield performance comparable to, and in some cases superior to, the traditional tandem mass tag (TMT) data dependent acquisition (DDA) TPCA workflow. This work provides critical insights into denaturation-based assays, highlights the value of insoluble fractions, and offers practical improvements for enhancing global PPI network mapping.
HostingRepository	PRIDE
AnnounceDate	2026-02-02
AnnouncementXML	Submission_2026-02-01_22:41:19.453.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Tavis Reed
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606; scientific name: Influenza A virus; NCBI TaxID: NCBITaxon:11320;
ModificationList	No PTMs are included in the dataset
Instrument	timsTOF Ultra

Revision	Datetime	Status	ChangeLog Entry
0	2025-09-21 19:52:10	ID requested
⏵ 1	2026-02-01 22:41:20	announced

10.1016/j.mcpro.2025.101479;

Reed TJ, Haubold LM, Hutton JE, Troyanskaya OG, Cristea IM, Exploring How Workflow Variations in Denaturation-Based Assays Impact Global Protein-Protein Interaction Predictions. Mol Cell Proteomics, 25(2):101479(2025) [pubmed]

submitter keyword: TPCA,Infuneza A

Ileana M. Cristea
contact affiliation	Molecular Biology Department, Princeton University, USA
contact email	icristea@princeton.edu
lab head
Tavis Reed
contact affiliation	Princeton University
contact email	tjreed@princeton.edu
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD068620
     1. Label: PRIDE project
     2. Name: Exploring How Workflow Variations in Denaturation-Based Assays Impact Global Protein-Protein Interaction Predictions (Influenza A Data)