BMSS Annual Meeting 2025

Authors: Anne Marie Smith, Michael Sutherland, Nafimal Haque, Artyom Petrovskiy, Vitaly Lashin

Introduction:
Peptides play vital roles in biological systems and are key targets in drug development and diagnostics. Their selective receptor binding makes them ideal for therapeutics and biomarkers. We present a streamlined LC-MS data analysis method using in silico fragmentation—including new rules for multiply charged species and bond breakage—to enhance peptide structure identification and spectral interpretation.

Methods:

MSn spectra of peptide samples were analyzed using various mass accuracies. Structures were assigned to spectra based on optimized in silico fragmentation rules, aiming for the highest assignment scores. The mass differences between the experimental spectra and theoretical fragment formulas were evaluated to assess assignment precision. To evaluate the impact of these enhancements on overall results, comparisons were made between the previously available settings and the newly developed peptide-specific settings for multiply charged species, as well as for amide and disulfide bond fragmentation.

Preliminary Data:
In silico fragmentation was applied to select LC-MS and extracted MSn spectra. Preliminary data indicated improved assignment scores due to the introduction of new fragmentation rules. The visualization of fragments on the spectra, along with the provided mass differences, facilitated the evaluation of assignment accuracy. This method effectively managed both multiply charged parent species and fragment ions. Additionally, it was successfully applied to various isomeric structures within single mass spectra in a semi-automated manner, enhancing our ability to distinguish closely related isomers. Fragmentation schemes, along with spectra annotated with labeled a, b, c, x, y, and z ions, were readily generated from the configurable reporting engine. This capability not only streamlines complex peptide analysis but also allows scientists to focus more on their research and less on the intricacies of fragmentation rules, ultimately enhancing peptide data analysis.

Novel Aspect:

Integration of peptide fragmentation rules with automated labeling of a, b, c, x, y, and z ions for enhanced analysis.