Sign in
BioinformaticsMolecular & Cellular Proteomics
Peer-reviewed journal article

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Daniel A. PolaskyiD, Daniel GeiszleriD, Fengchao YuiD, Kai LiiD, Guo Ci TeoiD, Alexey I. NesvizhskiiiD

Abstract

Post-translational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry (MS)-based proteomics, largely relying on traditional database search methods to annotate resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem MS experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new Labile Mode in the MSFragger search engine that can tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum annotation rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISGeiszler '22Ramírez '26Buur '26Kohler '22Karlic '26Ming '20
75% semantic matchBioinformatics
Daniel Geiszler, Daniel A. Polasky, Fengchao Yu+1 more·bioRxiv·Sep 12, 2022· 5 citations