Browsing by Subject "Proteoform"

Now showing 1 - 3 of 3
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    Complex Proteoform Identification Using Top-Down Mass Spectrometry
    (2018-12) Kou, Qiang; Wu, Huanmei; Liu, Xiaowen; Liu, Yunlong; Al Hasan, Mohammad
    Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.
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    Computational Methods for Proteoform Identification and Characterization Using Top-Down Mass Spectrometry
    (2023-12) Chen, Wenrong; Yan, Jingwen; Wang, Juexin; Wan, Jun; Zang, Yong; Luo, Xiao; Liu, Xiaowen
    Proteoforms, distinct molecular forms of proteins, arise due to numerous factors such as genetic mutations, differential gene expression, alternative splicing, and a range of biological processes. These proteoforms are often characterized by primary structural variances such as amino acid substitutions, terminal truncations, and post-translational modifications (PTMs). Proteoforms from the same proteins can manifest varied functional behaviors based on the specific alterations. The complexity inherent to proteoforms has elevated the significance of top-down mass spectrometry (MS) due to its proficiency in providing intricate sequence information for these intact proteoforms. During a typical top-down MS experiment, intact proteoforms are separated through platforms like liquid chromatography (LC) or capillary zone electrophoresis (CZE) prior to tandem mass spectrometry (MS/MS) analysis. Despite advancements in instruments and protocols for top-down MS, computational challenges persist, with software tool development still in its early stage. In this dissertation, our research revolves around three primary goals, all aimed at refining proteoform characterization. First, we bridge RNA-Seq with top-down MS for a better proteoform identification. We propose TopPG, an innovative proteogenomic tool which is tailored to generate proteoform sequence databases from genetic and splicing variations explicitly for top-down MS in contrast to traditional approaches. Second, to boost the accuracy of proteoform detection, we utilize machine learning methods to predict proteoform retention and migration times in top-down MS, an area previously overshadowed by bottom-up MS paradigms. critically evaluating models in a realm traditionally dominated by bottom-up MS methodologies. Lastly, recognizing the indispensable role of post-translational modifications (PTMs) on cellular functions, we introduce PTM-TBA. This tool integrates the complementary strengths of both top-down and bottom-up MS, augmented with annotations, building a comprehensive strategy for precise PTM identification and localization.
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    Identification and Quantification of Proteoforms by Mass Spectrometry
    (Wiley, 2019-05) Schaffer, Leah V.; Millikin, Robert J.; Miller, Rachel M.; Anderson, Lissa C.; Fellers, Ryan T.; Ge, Ying; Kelleher, Neil L.; LeDuc, Richard D.; Liu, Xiaowen; Payne, Samuel H.; Sun, Liangliang; Thomas, Paul M.; Tucholski, Trisha; Wang, Zhe; Wu, Si; Wu, Zhijie; Yu, Dahang; Shortreed, Michael R.; Smith, Lloyd M.; BioHealth Informatics, School of Informatics and Computing
    A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, we outline some recent advances and discuss current challenges and future directions for the field.