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Peptide retention time prediction for electrostatic repulsion-hydrophilic interaction chromatography
Quinn Neale1, Darien Yeung2, Victor Spicer3
1Department of Chemistry, University of Manitoba, Winnipeg, MB R3T 2N2, Canada; Manitoba Centre for Proteomics and Systems Biology, Health Science Centre, Winnipeg, MB R3E 3P4, Canada.
Journal of Chromatography. A
|October 8, 2024
Summary
Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) peptide retention was modeled using a large dataset, revealing key separation features and modification effects. This provides a robust tool for hydrophilic compound separation and proteomics.
Area of Science:
- Analytical Chemistry
- Chromatography
- Proteomics
Background:
- Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) is a valuable technique for separating hydrophilic compounds, especially peptides.
- Comprehensive modeling of peptide retention in ERLIC has been limited, hindering a full understanding of its separation mechanism.
Purpose of the Study:
- To evaluate major ERLIC retention features using a large proteomics-derived peptide dataset.
- To develop and validate a Sequence-Specific Retention Calculator (SSRCalc) model for ERLIC peptide retention.
- To assess the impact of various post-translational modifications (PTMs) on peptide retention in ERLIC.
Main Methods:
- Utilized a dataset of approximately 170,000 peptide retention times from proteomics experiments.
- Applied the Sequence-Specific Retention Calculator (SSRCalc) model framework to analyze ERLIC retention.
- Optimized separation conditions for enhanced proteome coverage and orthogonality with 2D LC-MS.
- Evaluated the influence of spontaneous and enzymatic PTMs on peptide retention.
Main Results:
- The SSRCalc ERLIC model accurately reflects known retention mechanisms, emphasizing peptide orientation and residue positioning.
- High accuracy was achieved with R² values of 0.935 for the interpretable model and 0.955 for a machine learning algorithm.
- Demonstrated superior separation orthogonality for 2D LC-MS, improving proteome coverage.
- Quantified the retention effects of diverse PTMs, including oxidation, deamidation, N-terminal acetylation, phosphorylation, and glycosylation.
Conclusions:
- The developed SSRCalc ERLIC model provides a reliable method for predicting peptide retention, enhancing hydrophilic compound separation.
- This work significantly advances the understanding of ERLIC separation mechanisms and its application in proteomics.
- The model's ability to account for PTMs offers valuable insights for complex biological sample analysis.
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