Back Cover: Identification and Quantification of Proteoforms by Mass Spectrometry

Schaffer, Leah V.; Millikin, Robert; Miller, Rachel M; Anderson, Lissa C.; Fellers, Ryan T.; Ge, Ying; Kelleher, Neil L.; LeDuc, Richard D.; Liu, Xiaowen; Payne, Samuel H.; Sun, Liang-Liang; Thomas, Paul M.; Tucholski, Trisha; Wang, Zhe; Wu, Si; Wu, Zhijie; Yu, Dahang; Shortreed, Michael R.; Smith, Lloyd M.

doi:10.1002/pmic.201970085

Cited by 9 publications

(7 citation statements)

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“…Mass spectrometry (MS)-based top-down proteomics aims to delineate proteoforms in cells comprehensively with high confidence and throughput. − Proteoforms extracted from biological samples are typically separated by reversed-phase liquid chromatography (RPLC) or capillary zone electrophoresis (CZE), followed by electrospray ionization (ESI)-tandem mass spectrometry (MS/MS). A database search is then performed for the identification (ID) of proteoform spectrum matches (PrSMs), proteoforms, and proteins through comparing experimental and theoretical masses of proteoforms and their fragments.…”

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confidence: 99%

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

et al. 2020

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Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)–tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)–MS/MS. The false discovery rate (FDR) from the target–decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms’ electrophoretic mobility (μef) using large-scale top-down proteomics data sets from CZE–MS/MS. We achieved a linear correlation between experimental and predicted μef of E. coli proteoforms (R 2 = 0.98) with a simple semiempirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their μef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease the proteoforms’ charge by roughly one charge unit.

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confidence: 99%

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

et al. 2020

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“…No proteoforms larger than 50 kDa were identified in the previous study, despite availability of acquired targeted MS2 data . It is a recognized challenge for online LC-MS/MS top-down proteomics to obtain sufficient fragmentation ions for identification of proteins larger than 50 kDa. , Indeed, a number of issues challenged successful top-down proteomic analysis of larger proteoforms. Though sSEC-RPC separation allowed for the MS1 detection of larger heart proteoforms, proteoforms larger than 50 kDa were not isotopically resolved by the Q-TOF mass analyzer, which prevented the use of monoisotopic mass in top-down search algorithms.…”

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confidence: 89%

“…The identification and quantification of proteoforms are important to understanding biological systems because different proteoforms exhibit distinct biological functions. − Altered proteoforms have been observed in heart disease, the leading cause of death worldwide, suggesting the importance of proteoform analysis to understanding cardiac dysfunction. ,, Mass spectrometry-based top-down proteomics is the most powerful available tool for identification and quantification of proteoforms. In a typical top-down proteomic analysis, precursor mass spectra (MS1) of intact proteoforms are acquired; the most abundant peaks are selected for fragmentation, and tandem mass spectra (MS2) of the fragment ions are acquired . Top-down proteomic analysis of the human proteome is technically challenging due to its wide dynamic range and high complexity.…”

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Intact-Mass Analysis Facilitating the Identification of Large Human Heart Proteoforms

et al. 2019

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Proteoforms, the primary effectors of biological processes, are the different forms of proteins that arise from molecular processing events such as alternative splicing and post-translational modifications. Heart diseases exhibit changes in proteoform levels, motivating the development of a deeper understanding of the heart proteoform landscape. Our recently developed two-dimensional top-down proteomics platform coupling serial size exclusion chromatography (sSEC) to reversed-phase chromatography (RPC) expanded coverage of the human heart proteome and allowed observation of highmolecular weight proteoforms. However, most of these observed proteoforms were not identified due to the difficulty in obtaining quality tandem mass spectrometry (MS2) fragmentation data for large proteoforms from complex biological mixtures on a chromatographic time scale. Herein, we sought to identify human heart proteoforms in this data set using an enhanced version of Proteoform Suite, which identifies proteoforms by intact mass alone. Specifically, we added a new feature to Proteoform Suite to determine candidate identifications for isotopically unresolved proteoforms larger than 50 kDa, enabling subsequent MS2 identification of important high-molecular weight human heart proteoforms such as lamin A (72 kDa) and trifunctional enzyme subunit α (79 kDa). With this new workflow for large proteoform identification, endogenous human cardiac myosin binding protein C (140 kDa) was identified for the first time. This study demonstrates the integration of our sSEC-RPC-MS proteomics platform with intact-mass analysis through Proteoform Suite to create a catalog of human heart proteoforms and facilitate the identification of large proteoforms in complex systems.

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“…There are several welcomed free software and databases. ProSightP TM (40) and TDPortal (41) might be the most widely used for TDP and proteoform identification, and TopPIC, TopMG, and Proteoform Suite might also be worthy of implementation (42). MetaMorpheus is an integrated software program for both BUP and TDP to identify peptides and proteoforms (43).…”

Section: Top-down Strategy For Proteomicsmentioning

confidence: 99%

Putting Proteomics Into Immunotherapy for Glioblastoma

Chen¹,

Qin²,

Guo³

et al. 2021

Front. Immunol.

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In glioblastoma, the most aggressive brain cancer, a complex microenvironment of heterogeneity and immunosuppression, are considerable hurdles to classify the subtypes and promote treatment progression. Treatments for glioblastoma are similar to standard therapies for many other cancers and do not effectively prolong the survival of patients, due to the unique location and heterogeneous characteristics of glioblastoma. Immunotherapy has shown a promising effect for many other tumors, but its application for glioma still has some challenges. The recent breakthrough of high-throughput liquid chromatography–mass spectrometry (LC-MS/MS) systems has allowed researchers to update their strategy for identifying and quantifying thousands of proteins in a much shorter time with lesser effort. The protein maps can contribute to generating a complete map of regulatory systems to elucidate tumor mechanisms. In particular, newly developed unicellular proteomics could be used to determine the microenvironment and heterogeneity. In addition, a large scale of differentiated proteins provides more ways to precisely classify tumor subtypes and construct a larger library for biomarkers and biotargets, especially for immunotherapy. A series of advanced proteomic studies have been devoted to the different aspects of immunotherapy for glioma, including monoclonal antibodies, oncolytic viruses, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) T cells. Thus, the application of proteomics in immunotherapy may accelerate research on the treatment of glioblastoma. In this review, we evaluate the frontline applications of proteomics strategies for immunotherapy in glioblastoma research.

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Back Cover: Identification and Quantification of Proteoforms by Mass Spectrometry

Cited by 9 publications

References 0 publications

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

Intact-Mass Analysis Facilitating the Identification of Large Human Heart Proteoforms

Putting Proteomics Into Immunotherapy for Glioblastoma

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