Browsing by Author "Pasa-Tolić, Ljiljana"

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    Identification of ultramodified proteins using top-down tandem mass spectra
    (American Chemical Society, 2013-12-06) Liu, Xiaowen; Hengel, Shawna; Wu, Si; Tolić, Nikola; Pasa-Tolić, Ljiljana; Pevzner, Pavel A.; Department of BioHealth Informatics, IU School of Informatics and Computing
    Post-translational modifications (PTMs) play an important role in various biological processes through changing protein structure and function. Some ultramodified proteins (like histones) have multiple PTMs forming PTM patterns that define the functionality of a protein. While bottom-up mass spectrometry (MS) has been successful in identifying individual PTMs within short peptides, it is unable to identify PTM patterns spreading along entire proteins in a coordinated fashion. In contrast, top-down MS analyzes intact proteins and reveals PTM patterns along the entire proteins. However, while recent advances in instrumentation have made top-down MS accessible to many laboratories, most computational tools for top-down MS focus on proteins with few PTMs and are unable to identify complex PTM patterns. We propose a new algorithm, MS-Align-E, that identifies both expected and unexpected PTMs in ultramodified proteins. We demonstrate that MS-Align-E identifies many proteoforms of histone H4 and benchmark it against the currently accepted software tools.
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    Mass graphs and their applications in top-down proteomics
    (2015) Kou, Qiang; Wu, Si; Tolić, Nikola; Pasa-Tolić, Ljiljana; Liu, Xiaowen; Department of Biohealth Informatics, School of Informatics and Computing
    Although proteomics has made rapid progress in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a "bird view" of intact proteoforms. The combinatorial explosion of possible proteoforms, which may result in billions of possible proteoforms for one protein, makes proteoform identification a challenging computational problem. Here we propose a new data structure, called the mass graph, for efficiently representing proteoforms. In addition, we design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on a histone H4 mass spectrometry data set showed that the proposed methods outperformed MS-Align-E in identifying complex proteoforms.