Peptide retention time prediction for electrostatic repulsion-hydrophilic interaction chromatography

Abstract

Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) is one of the legacy separation tools developed by Dr. Andrew Alpert and has been used for developing unique separation methods of hydrophilic compounds, including peptides. In the past it has been studied using designed peptide libraries to elucidate major features of its separation mechanism, while comprehensive peptide retention modeling for ERLIC is still lacking. In this work we employed a proteomics-derived ∼170,000 peptide retention datasets to evaluate major ERLIC retention features using the framework of our Sequence-Specific Retention Calculator model. The separation conditions were adjusted to obtain a wider proteome coverage, particularly for non-modified peptides, resulting in a superior separation orthogonality for a 2D LC combination with reversed-phase C18 LC-MS in the second dimension. The SSRCalc ERLIC model presents a consistent theme with the existing ERLIC retention mechanism, reflecting a dependence on peptide orientation and the position of charged and hydrophilic residues across the peptide backbone. R² values of 0.935 and 0.955 accuracy were demonstrated for the standard interpretable SSRCalc model and machine learning algorithm, respectively. The effects of various PTMs on peptide retention were evaluated in this study, covering spontaneous (oxidation, deamidation) and enzymatic (N-terminal acetylation, phosphorylation, glycosylation) modifications.