For many years, CID has been the method of choice for peptide sequencing in bottom-up proteomics. However, as the focus of the proteomics community has been shifting in recent years to more challenging tasks such as identification and localization of complex PTMs and characterization of proteoforms by means of (native) top-down MS, a need for more advanced fragmentation techniques has emerged. Here, we report the results of protein and peptide characterization by various electron based (ECD, EID) and laser based (IRMPD, UVPD) fragmentation techniques and combinations thereof integrated in a novel omnitrap instrument as well as the results of the implementation of these techniques on the LCMS scale for the analysis of complex peptide and glycopeptide mixtures.

All spectra were obtained in an omnitrap MS platform coupled with an Orbitrap Exploris 480 mass spectrometer. The omnitrap is equipped with an electron source for ECD and EID experiments, a UV 193 nm laser for UVPD, and an IR 10um laser for IRMPD and activated-ion fragmentation.

We first performed an initial characterization of UVPD, IRMPD, EID, ECD, and activated-ion ECD (AI-ECD) available in the Omnitrap using several standard polypeptides in direct infusion experiments. The aptitude of each of the dissociation techniques listed above for conventional bottom-up proteomics were probed in a series of large-scale LCMS experiments on cell lysates. Cell lysates were digested using various proteases (trypsin, LysC, GluC, LysN, chymotrypsin) and the digests were offline fractionated prior to LCMS analysis. The identified peptides allowed us to compare the fragmentation techniques in terms of their suitability for peptide sequencing and localisation of PTMs. This experiment yields data which is orthogonal/complementary to conventional CID data. In a separate series of experiments, we examined the utility of Omnitrap UVPD, EID, ECD, and AI-ECD for the analysis of complex glycopeptide mixtures. We focused on the efficiency of each technique for sequencing of peptide and glycan parts of a glycopeptide and its ability to localise glycosites. We demonstrated that the Orbitrap-omnitrap hybrid is capable of generating high-quality spectra with reasonable (up to 20 Hz) scan rate suitable for LCMS experiments.

In this work, we compare alternative fragmentation techniques from the perspective of sequence coverage, fragmentation efficiency, and the ability to localise PTMs and discuss their utility for the top-down and bottom-up analysis of (modified) proteins. In addition, we generated a large peptide dataset which provides a great material for training deep learning models for spectra prediction and thus further enhances the quality of deep proteome sequencing and PTM mapping.