Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine

Abstract

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of Nglycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

Narrow window data-independent acquisition on the Orbitrap Astral Mass Spectrometer enables fast and deep coverage of the plasma glycoproteome

“…Furthermore, we used four different collision energies here, which each provided important information for compositional and structural annotation of the glycopeptides. Being able to use stepping collision energy without sacrificing speed would enable all complementary information to be acquired in a single scan, as is often used in DDA glycoproteomics 14,15,54 . However, applying stepping HCD in this case meant we had to either reduce the cycle time, the injection time, or the MS1 scan range, or we had to increase the window size which would lead to more chimeric spectra.…”

Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes

“…Glycomic and glycoproteomic analyses have been used complementarily for over a decade and have been refined since into a combined glycomics-assisted glycoproteomics analytical protocol, using the released glycan masses and assigned structures to create a more targeted glycan space for subsequent glycoproteomic data analysis. , Even with this approach, glycoproteomic analysis is limited to informed compositional and structural assignments based on previously obtained information. Adding glycotope assignment to glycoproteomic analysis workflows can provide greater detail to the glycopeptide characterization, particularly with recent instrument and software advances in fragmentation and automated analysis. − …”