Dataset Information

Total Retention Liquid Chromatography-Mass Spectrometry to Achieve Maximum Protein Sequence Coverage.

ABSTRACT: Peptide identification by liquid chromatography-mass spectrometry (LC-MS) requires retention and elution of peptides from the LC column. Although medium and hydrophobic peptides are readily retained by the C18 columns that are commonly used in proteomics, short and hydrophilic peptides are not retained nor measured by MS due to their elution in the void volume after sample injection. These nonretained peptides can possess important post-translational modifications, such as glycosylation or phosphorylation. We describe a total retention LC-MS method that employs a reverse phase C18 column and porous graphitic carbon (PGC) column to retain both hydrophobic and hydrophilic peptides for LC-MS analysis. Our setup uses a single valve with a trapping column and two LC pumps run at low microliter/minute flow rates to deliver separate gradients to parallel capillary C18 and PGC columns. Our capillary LC system balances the need for high sensitivity with ease of implementation as compared to other 2D LC systems that use nanocolumns with multiple trapping columns and multiport valves. We demonstrate the utility of the method identifying hydrophilic peptides that went undetected when only a C18 nanocolumn was used. These missed hydrophilic peptides include tripeptides and N-glycosylated species.

SUBMITTER: Jennings ME

PROVIDER: S-EPMC8655629 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

Dataset Information

Total Retention Liquid Chromatography-Mass Spectrometry to Achieve Maximum Protein Sequence Coverage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets