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Dataset for TAggiXL: A Fluorescence-Traceable Cross-Linking Strategy for Unbiased Profiling of Protein Aggregation and Interactome Dynamics

ABSTRACT: Protein aggregation is a hallmark of numerous degenerative diseases, yet its underlying mechanisms remain poorly understood due to the challenges in identifying the composition and interaction networks of these aggregates. To address this issue, we developed TAggiXL, a novel method that combines fluorescence-traceable aggregate isolation with cross-linking proteomics, significantly enhancing the efficiency and precision of isolating protein aggregates. This method facilitates unbiased profiling of aggregated proteomes and their interactomes in live cells. The TAggiXL approach leverages advanced cross-linking proteomics, density gradient centrifugation, and fluorescence tracking to provide detailed characterization of protein aggregation under various stress conditions, including HSP90 and proteasome inhibition. Using TAggiXL, we identified key components and interactions within the aggregates, particularly highlighting the E3 ubiquitin ligase TRIM26, which plays a crucial role in aggregate formation and autophagic clearance under stress and pathogenic conditions. Moreover, TAggiXL revealed that HSPA1B functions as a central interaction hub within the aggregated proteome. It preferentially interacts with intrinsically disordered regions (IDRs) of aggregate components and demonstrates dynamic behavior within the aggregate. In summary, TAggiXL offers a powerful tool for dissecting the complex composition and interaction networks of protein aggregates, with significant potential to advance our understanding of protein aggregation in degenerative diseases. It also holds promise for informing the development of future therapeutic interventions.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Lihua Zhang

PROVIDER: PXD056352 | JPOST Repository | Sun Sep 29 00:00:00 BST 2024

REPOSITORIES: jPOST

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			Action	DRS
	AGG-DGC_17AAG_CXMS_BioDuplicate1-F3-1.raw	Raw
	AGG-DGC_17AAG_CXMS_BioDuplicate1-F3-2.raw	Raw
	AGG-DGC_17AAG_CXMS_BioDuplicate2-F1-1.raw	Raw
	AGG-DGC_17AAG_CXMS_BioDuplicate2-F1-2.raw	Raw
	AGG-DGC_17AAG_CXMS_BioDuplicate2-F2-1.raw	Raw

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Project description:Auer2010 - Correlation between lag time and aggregation rate in protein aggregation This model is described in the article: Insight into the correlation between lag time and aggregation rate in the kinetics of protein aggregation. Auer S, Kashchiev D. Proteins 2010 Aug; 78(11): 2412-2416 Abstract: Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation. This model is hosted on BioModels Database and identified by: BIOMD0000000555. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dataset for TAggiXL: A Fluorescence-Traceable Cross-Linking Strategy for Unbiased Profiling of Protein Aggregation and Interactome Dynamics

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