Benchmarking and Automating the Biotinylation Proteomics Workflow.

Area of Science:

• Biochemistry
• Proteomics
• Molecular Biology

Background:

• Protein biotinylation is crucial in biotechnology for labeling and enrichment.
• Existing methods lack systematic evaluation and standardized benchmarking models.
• Current workflows are often lengthy, prone to non-specific binding, and have limited throughput.

Purpose of the Study:

• To establish a robust benchmarking model for evaluating biotinylation proteomics strategies.
• To optimize and automate biotinylation workflows for increased efficiency and reduced variability.
• To investigate organelle dynamics, specifically mitochondria and lysosomes, using proximity labeling.

Main Methods:

• Developed a two-proteome model (yeast spiked into human proteins) to differentiate true enrichment from non-specific binding.
• Systematically compared common biotinylation proteomics methods using the developed model.
• Optimized and automated sample preparation, reducing processing time from 3 days to 9 hours for 96-well plates.

Main Results:

• Established a reliable benchmarking model for assessing biotinylation enrichment strategies.
• Achieved a significant reduction in sample preparation time to 9 hours with full automation.
• Identified dynamic proteome remodeling and protein translocation between mitochondria and lysosomes upon cellular stress.

Conclusions:

• The developed two-proteome model provides a fair comparison of biotinylation methods.
• The optimized 9-hour automated workflow enhances throughput and reduces experimental variability.
• This approach facilitates the study of protein interactions and organelle dynamics in various biological contexts.