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Babovic, M.

Publications and source records attributed to Babovic, M..

2 recordsLinked to original sources

How to deal with internal fragment ions?

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.

bioinformatics↗

FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count

Top-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in [1/3] of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

bioinformatics↗