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Assessing Metabolite Interactions With Chloroplastic Proteins via the PISA Assay
Anna Karlsson1, Emil Sporre1, Linnéa Strandberg2
1School of Engineering Science in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
This study adapts the proteome integral solubility alteration (PISA) assay for plant chloroplasts, enabling unbiased identification of protein-ligand interactions. The method screens metabolite regulation of plant proteins without prior knowledge, advancing plant science research.
Area of Science:
- Plant molecular biology
- Proteomics
- Biochemistry
Background:
- Metabolite regulation of proteins is crucial for plant metabolism and adaptation.
- Studying these complex plant protein-interaction networks is challenging.
- The proteome integral solubility alteration (PISA) assay, a chemoproteomic technique, measures protein stability changes upon small molecule interaction.
Purpose of the Study:
- To adapt the PISA assay for use in *Arabidopsis thaliana* chloroplasts.
- To identify potential protein interactions with ascorbate in plant chloroplasts.
- To provide a protocol for unbiased screening of protein regulation by small molecules in plants.
Main Methods:
- Chloroplasts were isolated from *Arabidopsis thaliana* using a Percoll gradient.
- Isolated chloroplasts were treated with varying ascorbate concentrations.
- Protein stability changes were quantified using heat-induced denaturation, ultracentrifugation, and multiplexed LC-MS.
Main Results:
- The adapted PISA protocol allows for proteome-wide screening of protein-ligand interactions in plant chloroplasts.
- The method does not require prior knowledge of interaction partners, chemical probes, or genetic modifications.
- The protocol includes instructions for LC-MS data analysis and statistical interpretation.
Conclusions:
- This adapted PISA assay provides a powerful tool for studying metabolite regulation of proteins in plants.
- The protocol is adaptable to other organisms like bacteria with minor modifications.
- It enables high proteome coverage with low sample requirements and reduced costs.
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