ASMS – 73rd Conference on Mass Spectrometry and Allied Topics

Anne Marie Smith¹; Artyom Petrovskiy¹; Vitaly Lashin¹

¹ACD/Labs, Toronto, ON

Introduction

Peptides are integral to numerous biological processes and have already been applied in medicine, functioning as hormones, neurotransmitters, and signaling molecules. Their selective binding to receptors makes them invaluable for drug development, particularly in targeted therapies. They also serve as biomarkers for disease diagnosis and progression. Research into synthetic peptides is expanding, paving the way for novel therapeutics and enhanced vaccine efficacy, positioning them as a focal point in biochemistry and pharmacology. Here, we introduce a robust data analysis approach for LC-MS spectra of peptide compounds. Our method incorporates in silico fragmentation, including new rules for generating multiply charged species and fragmenting amide and disulfide bonds, with clear spectral highlighting and identification of structures and ion types.

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.