Peptide collision cross sections of 22 post-translational modifications

Abstract

Recent advances have rekindled the interest in ion mobility spectrometry as an additional dimension of separation in mass spectrometry (MS)– based proteomics. It separates ions according to their size and shape in the gas phase. Here, we set out to investigate the effect of 22 different post-translational modifications (PTMs) on the collision cross section (CCS) of peptides. In total, we analyzed about 4700 pairs of matching modified and unmodified peptide ions by trapped ion mobility spectrometry (TIMS). Linear alignment based on spike-in reference peptides resulted in highly reproducible CCS values with a median coefficient of variation of 0.3%. On a global level, we observed a redistribution in the m/z vs. ion mobility space for modified peptides upon changes in their charge state. Pairwise comparison between modified and unmodified peptides of the same charge state revealed median shifts in CCS between −1.1% (lysine formylation) and +4.5% (O-GlcNAcylation). In general, increasing modified peptide masses were correlated with higher CCS values, in particular within homologous PTM series. However, investigating the ion populations in more detail, we found that the change in CCS can vary substantially for a given PTM depending on the gas phase structure of its unmodified counterpart. In conclusion, our study shows PTM– and sequence–specific effects on the cross section of peptides, which could be further leveraged for proteome–wide PTM analysis.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

“…Similar to fragment ion intensity and RT predictions, CCS predictions can be used as features during PSM rescoring 36 . Some CCS prediction models already exist 22,[37][38][39] , including a recent model trained on tryptic peptides, phosphopeptides, and immunopeptides 38 .…”

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

“…Similar to fragment ion intensity and RT predictions, CCS predictions can be used as features during PSM rescoring 38 . Some CCS prediction models already exist 22,[39][40][41] , including a recent model trained on immunopeptides 40 .…”

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

“…The first data set, described by Meier et al (23) consists of 718,917 unique combinations of peptide sequence, charge state and, when applicable, modifications (limited to methionine oxidation, cysteine carbamidomethylation and N-terminal acetylation). The second data set, described in (24), comprises 5,202 unique peptidoform-charge state combinations, and contains a wider variety of modifications. In this data set, a distinction is also made between symmetrical and asymmetrical arginine dimethylation.…”