Confident Identification of Citrullination and Carbamylation Assisted by Peptide Retention Time Prediction.
Carina VillacrésVictor SpicerOleg V KrokhinPublished in: Journal of proteome research (2021)
The chromatographic behavior of peptides carrying citrulline and homocitrulline residues in proteomic two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS) experiments has been investigated. The primary goal of this study was to determine the chromatographic conditions that allow differentiating between arginine citrullination and deamidation of asparagine based on retention data, improving the confidence of MS-based identifications. Carbamylation was used as a reference point due to a high degree of similarity between modification products and anticipated changes in chromatographic behavior. We applied 2D LC-MS/MS (a high-pH-low-pH reversed phase (RP), hydrophilic interaction liquid chromatography (HILIC)-low-pH RP, and strong cation exchange (SCX)-low-pH RP) to acquire retention data for modified-nonmodified peptide pairs in the four separation modes. Modifications of a standard protein mixture were induced enzymatically (PAD-2) or chemically (urea) for citrullination and carbamylation, respectively. Deamidation occurs spontaneously. Similar retention shifts were observed for all three modifications in a high-pH RP (decrease) and a low-pH RP (increase), thus limiting the applicability of this 2D LC combination. HILIC on bare silica and strong cation exchange separations have been probed to amplify the effect of charge loss upon citrullination, with SCX demonstrating the most differentiating power: the elimination of basic residues upon citrullination/carbamylation results in an ∼58 mM KCl retention decrease, while retention of deamidated products decreases slightly.
Keyphrases
- liquid chromatography
- mass spectrometry
- simultaneous determination
- high resolution mass spectrometry
- tandem mass spectrometry
- capillary electrophoresis
- solid phase extraction
- high performance liquid chromatography
- gas chromatography
- high resolution
- electronic health record
- ionic liquid
- big data
- amino acid
- magnetic resonance imaging
- contrast enhanced
- oxidative stress
- drug induced
- magnetic resonance
- artificial intelligence
- data analysis
- molecular dynamics simulations
- deep learning
- diabetic rats