FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics

Area of Science:

• Proteomics
• Analytical Chemistry
• Biochemistry

Background:

• Shotgun proteomics enables deep proteome characterization but faces sensitivity limitations due to sample complexity and dynamic range.
• Current peptide fractionation methods using uniform fraction collection lead to suboptimal mass spectrometer performance.
• Non-uniform peptide distribution across fractions hinders comprehensive proteome analysis.

Purpose of the Study:

• To develop an optimized peptide fractionation strategy for enhanced shotgun proteomics sensitivity.
• To introduce FractionOptimizer, an open-source software for predicting peptide retention times and optimizing fractionation.
• To improve the uniformity of peptide distribution for more efficient mass spectrometry analysis.

Main Methods:

• Development of an approach based on peptide retention time prediction.
• Creation of the open-source software FractionOptimizer (http://hg.theorchromo.ru/FractionOptimizer).
• Application of the method to human embryonic kidney (HEK293) cell line lysate for performance demonstration.

Main Results:

• Achieved improved uniformity of peptide distribution between fractions.
• Identified 13,492 known peptides and 6,787 new peptides.
• Discovered up to 800 new proteins, representing a 25% increase in protein identification.

Conclusions:

• The FractionOptimizer approach significantly enhances proteome coverage in shotgun proteomics.
• Optimized fractionation based on predicted peptide retention times overcomes limitations of uniform collection.
• This method provides a substantial increase in identified peptides and proteins, advancing deep proteome characterization.