Predicting Electrophoretic Mobility of Tryptic Peptides for High-Throughput CZE-MS Analysis

Area of Science:

• Proteomics
• Analytical Chemistry
• Biophysics

Background:

• Peptide electrophoretic mobility is primarily determined by charge and size.
• Existing models for peptide separation lack sequence-specific predictive power.
• Capillary Zone Electrophoresis-Mass Spectrometry (CE-MS) is a powerful separation technique.

Purpose of the Study:

• To develop a multiparametric, sequence-specific model for predicting peptide electrophoretic mobility.
• To leverage large-scale bottom-up proteomic CE-MS data for enhanced model accuracy.
• To identify sequence-specific factors influencing peptide mobility in CE-MS.

Main Methods:

• Utilized a large dataset (>4000 peptides) from bottom-up proteomic CE-MS.
• Developed a sequence-specific model incorporating peptide charge (Z) and molecular mass (M).
• Analyzed the impact of N-terminal acidic residues (Asp, Glu) on peptide charge and mobility.

Main Results:

• Achieved high model correlation (R² ≈ 0.995), indicating precise prediction of peptide mobility.
• Identified N-terminal acidic residues as significant factors reducing peptide charge and mobility.
• Demonstrated that CE-MS peptide separation is simpler and more predictable than RP-HPLC.

Conclusions:

• The developed sequence-specific model significantly enhances the prediction of peptide electrophoretic mobility.
• Future work will focus on larger datasets, migration standards, and post-translational modifications for improved CE-MS analysis.
• Investigating peptide secondary structure effects on mobility requires further data expansion.