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Relative Retention Time Estimation Improves N-Glycopeptide Identifications by LC–MS/MS by Klein, Joshua; Zaia, Joseph is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Relative Retention Time Estimation Improves N-Glycopeptide Identifications by LC–MS/MS about?

Glycopeptides identified by tandem mass spectrometry rely on the identification of the peptide backbone sequence and the attached glycan(s) by the incomplete fragmentation of both moieties. This may lead to ambiguous identifications where multiple structures could explain the same spectrum equally well due to missing information in the mass spectrum or incorrect precursor mass determination. To date, approaches to solving these problems have been limited, and few inroads have been made to address these issues. We present a technique to address some of these challenges and demonstrate it on previously published data sets. We use a linear modeling approach to learn the influence of the glycan composition on the retention time of a glycopeptide and use these models to validate glycopeptides within the same experiment, detecting over 400 incorrect cases during the MS/MS search and correcting 75 cases that could not be identified based on mass alone. We make this technique available as a command line executable program, written in Python and C, freely available at https://github.com/mobiusklein/glycresoft in source form, with precompiled binaries for Windows.

Who reads Relative Retention Time Estimation Improves N-Glycopeptide Identifications by LC–MS/MS?

Author

Klein, Joshua; Zaia, Joseph

Publisher

American Chemical Society; American Chemical Society (ACS) (ISSN 1535-3893)

Published

2020

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)