Molecular & Cellular ProteomicsBioinformaticsPeer-reviewed journal article
How to deal with internal fragment ions?
Arthur GrimaudiD, Maša BabovićiD, Frederik Haugaard HolckiD, Ole Nørregaard JenseniD, Veit SchwämmleiD
Abstract
1Tandem mass spectrometry of peptides and proteins generates mass spectra of their gas-phase fragmentation product ions, including N-terminal, C-terminal, and internal fragment ions. Whereas N- and C-terminal ions are routinely assigned and identified using computational methods, internal fragment ions are often difficult to annotate correctly. They become particularly relevant for long peptides and full proteoforms where the peptide backbone is more likely to be fragmented multiple times. Internal fragment ions potentially offer tremendous information regarding amino acid sequences and positions of post-translational modifications of peptides and intact proteins. However, their practical application is challenged by the vast number of theoretical internal fragments that exist for long amino acid sequences, leading to a high risk of false-positive annotations. We analyze the mass spectral contributions of internal fragment ions in spectra from middle-down and top-down experiments and introduce a novel graph-based annotation approach designed to manage the complexity of internal fragments. Our graph-based representation allows us to compare multiple candidate proteoforms in a single graph, and to assess different candidate annotations in a fragment ion spectrum. We demonstrate cases from middle-down and top-down data where internal ions enhance amino acid sequence coverage of polypeptides and proteins and accurate localization of post-translational modifications. We conclude that our graph-based method provides a general approach to process complex tandem mass spectra, enhance annotation of internal fragment ions, and improve proteoform sequencing and characterization by mass spectrometry.
- assay: proteomics
- research focus: protein structure prediction
- research focus: protein design
- research focus: benchmarking
- approach: graph neural network
- Citations
- 5
- FWCI
- 1.26
- Field percentile
- top 26%
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