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

Abstract

A multi-parametric sequence-specific model for predicting peptide electrophoretic mobility has been developed using large-scale bottom-up proteomic CE-MS data (5% acetic acid as background electrolyte). Peptide charge (Z) and size (molecular weight, M) are the two major factors determining electrophoretic mobility-- in complete agreement with previous studies. The extended size of the dataset (>4000 peptides) permits access to many sequence-specific factors that impact peptide mobility. The presence of acidic residues Asp and Glu near the peptide N-terminus is by far the most the prominent among them. The induction effect of the side chain of N-terminal Asp reduces the basicity of the N-terminal amino group- and as hence its charge- by ~0.27 units, lowering mobility. The correlation of the model (R2~0.995) indicates that the peptide separation process in CZE is relatively simple and can be predicted to a much higher precision than current RP-HPLC models. Similar to RP-HPLC prediction studies, we anticipate future studies that introduce peptide migration standards, collect larger datasets for modeling through the alignment of multiple CZE-MS acquisitions, and study of the behaviour of peptides carrying post-translational modifications. The increased size of datasets will also permit investigation of the fine-scale effects of peptide secondary structure on peptide mobility. We observed that peptides with higher helical propensity tend to have higher than predicted electrophoretic mobility; the incorporation of these features into CZE migration models will require significantly larger data sets.

Recent advances in capillary electrophoresis‐mass spectrometry: Instrumentation, methodology and applications

“…Therefore, Krokhin et al. used large‐scale bottom‐up CE‐MS datasets to develop a prediction model for the electrophoretic mobility of peptides based on sequence‐specific parameters . The model reached a correlation of R 2 ∼0.995 for predicted versus experimental migration times, indicating a more precise prediction than current RPLC prediction models, which, according to the authors, can be explained by a relative simple separation process of CZE compared to RPLC.…”

Multisegment injections improve peptide identification rates in capillary zone electrophoresis-based bottom-up proteomics

“…However, this situation is unimportant when performing bottom-up analysis of fractions from the same proteome because it is not necessary to determine the fraction from which a peptide was identified. If it is necessary to identify peptides with a particular injection, a high-accuracy migration time model can be used to estimate the injection time for the peptides (19). We observe fewer total numbers of identifications for shorter times between injections.…”

“…As a result of these studies, it is clear that CZE offers several advantages compared to RPLC for this analysis: CZE produces many more peptide and protein identifications from mass limited samples (17), a simple model has been developed for the migration time of peptides to increase confidence of identifications (19), and on-column digestion has been developed to simplify sample manipulations (20–21). …”

Recent developments and applications of capillary and microchip electrophoresis in proteomics and peptidomics (2015–mid 2018)

“…The sequence specific model was developed to predict peptide electrophoretic mobility for an extended set of tryptic peptides (4000) identified by CZE‐MS/MS . Peptide charge and size (molecular mass) were found to be the main factors influencing peptide electrophoretic mobility.…”