TRANSPIRE: A Computational Pipeline to Elucidate Intracellular Protein Movements from Spatial Proteomics Data Sets

Area of Science:

• Proteomics
• Cellular Biology
• Bioinformatics

Background:

• Protein localization is crucial for protein function and cellular processes.
• Advances in spatial proteomics enable large-scale investigation of protein localization.
• There is a need for analytical frameworks to discover global intracellular protein movement events.

Purpose of the Study:

• To introduce TRANSPIRE, a computational pipeline for translocation analysis of spatial proteomics data.
• To leverage TRANSPIRE to identify and understand protein movement during viral infections.

Main Methods:

• TRANSPIRE uses synthetic translocation profiles from organelle markers to train a Gaussian process classifier.
• It predicts protein distribution changes and integrates co-translocation and gene ontology data.
• Validation performed using nuclear-cytoplasmic shuttling events and viral infection models (KSHV, HCMV).

Main Results:

• TRANSPIRE successfully predicted expected translocations of RNA binding proteins during KSHV infection.
• HCMV infection induced widespread protein localization changes, with over 800 proteins predicted to translocate.
• Specific translocations, like LDLR to the lysosome and DAPK3, were validated and linked to HCMV-mediated processes.

Conclusions:

• TRANSPIRE is an effective tool for analyzing spatial proteomics data to uncover protein translocation events.
• HCMV infection significantly remodels host cell proteome localization, affecting host defense, metabolism, trafficking, and Wnt signaling.
• Identified protein movements provide insights into viral mechanisms and host-pathogen interactions.