Proteomics Workflow Developed for Validating Plant Fusion Peptides from Public Datasets

Fusion transcripts and their translated protein products are emerging as significant entities in molecular biology, holding promise as unique biomarkers and therapeutic targets. While numerous algorithms exist to identify fusion RNAs, the detection and robust validation of their protein counterparts through proteomics remains a growing challenge, particularly within plant biology. In plants, fusion events can profoundly influence stress responses, development, and adaptation, necessitating reliable validation methods to confirm their existence and function at the protein level. This gap in protein-level validation hinders full understanding and application.

This chapter provides an accessible and practical workflow specifically designed for validating plant fusion peptides. The methodology guides researchers through the systematic process of utilizing publicly available proteomics datasets. It details essential steps for data acquisition from various repositories, performing peptide-spectrum matching, and the subsequent systematic identification of fusion peptides. The workflow emphasizes leveraging existing data to confirm the presence of these novel protein products, offering a structured and reproducible approach to overcome the current limitations of solely RNA-based detection methods.

The presented workflow enables the robust and systematic validation of plant fusion peptides, providing a critical and practical tool for molecular biologists and plant scientists. It facilitates the confident detection of these unique protein products directly from complex proteomics datasets, which is essential for confirming their biological relevance and functional implications. > This practical guide empowers researchers to bridge the gap between computationally predicted fusion RNAs and their actual protein expression, significantly enhancing the reliability of findings related to plant stress responses, development, and adaptation. The workflow offers a clear, step-by-step pathway to identify novel fusion events at the protein level, an area often under-explored despite the increasing prevalence of RNA fusion detection. This systematic approach improves the accuracy and confidence in identifying true fusion proteins, moving beyond theoretical predictions.