A Method to Estimate the Distribution of Proteins across Multiple Compartments Using Data from Quantitative...

Area of Science:

• Proteomics
• Cell Biology
• Bioinformatics

Background:

• Protein cellular localization is crucial for understanding biological function.
• Subcellular fractionation coupled with mass spectrometry is a common proteomics technique.
• Current methods often assign proteins to single locations, failing to account for multi-compartment residence.

Purpose of the Study:

• To describe the principles of constrained proportional assignment (CPA).
• To present data transformations for enhanced accuracy in protein and protein isoform localization.
• To introduce a suite of R-based programs for analyzing subcellular proteomics data.

Main Methods:

• Constrained Proportional Assignment (CPA) for fractional localization.
• Data transformations for improved accuracy.
• Development of R-based software suite for CPA implementation.
• Analysis of rat liver fractions using isobaric-labeling mass spectrometry.

Main Results:

• CPA enables accurate fractional residence assignment of proteins across cellular compartments.
• Data transformations enhance the precision of localization for proteins and their isoforms.
• The R-based programs facilitate flexible analysis of diverse experimental designs.

Conclusions:

• CPA provides a more accurate method for determining protein localization, especially for proteins in multiple cellular compartments.
• The developed R package offers a versatile tool for the analysis of subcellular proteomics data.
• The methodology is adaptable to various experimental setups and quantitation methods.