Elucidating the Fragmentation Mechanism of Protonated Lewis A Trisaccharide using MSn CID

Lewis blood group antigens are a prominent example of isomeric oligosaccharides with biological activity. Understanding the fragmentation mechanism in the gas phase is essential for their identification and assignment by mass spectrometric methods such as ESI-MS. In this work, the [M + H]+ species of Lewis A trisaccharide and Lewis A trisaccharide methyl glycoside were studied by ESI-MS with FT-ICR as mass analyzer with respect to their fragmentation mechanism. The comparison between the underivatized and the methylated species has shown that the reducing end plays a key role in this mechanism. The results of this study question the existence of Z-type fragment ions after activation of the protonated species. The main product of the fragmentation are Y-type fragment ions and a combination of Y-type fragmentation and the loss of water at the reducing end instead of Z-type fragmentation. C-type fragment ions could not be detected. MS3 measurements also reveal that each fragment ion only occurs with the participation of a mobile proton and the possibility of glycosidic bond cleavage after fragmentation has already occurred at the reducing end as B2 fragment ion.

Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

ABSTRACTMotivationIdentification of peptides in data-independent acquisition (DIA) mass spectrometry (MS) typically relies on the scoring for the peak groups upon extracted chromatograms of fragment ions. Expanding fragment scoring features closer to the genuine experimental spectra can improve DIA identification. Deep learning is able to predict fragment presence without understanding the fragmentation mechanism that can enrich the scoring features in DIA identification.ResultsIn this work, we developed a deep neural network-based model, Alpha-Frag, to predict the fragment ions that should be present for a given peptide by reporting their probabilities of existence. The prediction performance was evaluated in terms of intersection over union (IoU), and Alpha-Frag achieved an average of >0.7 and outperformed substantially the benchmarks across the validation datasets. Furthermore, qualitative scores based on Alpha-Frag were designed and incorporated into the peptide statistical validation tools as auxiliary scores. Our preliminary experiments show that the qualitative scores by Alpha-Frag are profitable for DIA identification, especially in the case of short gradient, and yielded an increase of 10.1%-29.3% improvements for the test dataset compared to the same scoring strategy but using Prosit.Availability and ImplementationSource code and the trained model are available at www.github.com/YuAirLab/Alpha-Frag.

The fragmentation mechanism of gold nanoparticles in water under femtosecond laser irradiation

Plasmonic nanoparticles in aqueous solution have long been known to fragment under irradiation with intense ultrafast laser pulses, creating progeny particles with diameters of a few nanometers. However, the mechanism...