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Brodbelt JS. Deciphering combinatorial post-translational modifications by top-down mass spectrometry. Curr Opin Chem Biol 2022; 70:102180. [PMID: 35779351 PMCID: PMC9489649 DOI: 10.1016/j.cbpa.2022.102180] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 12/15/2022]
Abstract
Post-translational modifications (PTMs) create vast structural and functional diversity of proteins, ultimately modulating protein function and degradation, influencing cellular signaling, and regulating transcription. The combinatorial patterns of PTMs increase the heterogeneity of proteins and further mediates their interactions. Advances in mass spectrometry-based proteomics have resulted in identification of thousands of proteins and allowed characterization of numerous types and sites of PTMs. Examination of intact proteins, termed the top-down approach, offers the potential to map protein sequences and localize multiple PTMs on each protein, providing the most comprehensive cataloging of proteoforms. This review describes some of the dividends of using mass spectrometry to analyze intact proteins and showcases innovative strategies that have enhanced the promise of top-down proteomics for exploring the impact of combinatorial PTMs in unsurpassed detail.
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Affiliation(s)
- Jennifer S Brodbelt
- Department of Chemistry, University of Texas at Austin, Austin, TX 78712, USA.
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Li Z, He B, Feng W. Evaluation of bottom-up and top-down mass spectrum identifications with different customized protein sequences databases. Bioinformatics 2020; 36:1030-1036. [PMID: 31584612 DOI: 10.1093/bioinformatics/btz733] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 08/12/2019] [Accepted: 09/25/2019] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Generally, bottom-up and top-down are two complementary approaches for proteoforms identification. The inference of proteoforms relies on searching mass spectra against an accurate proteoform sequence database. A customized protein sequence database derived by RNA-Seq data can be used to better identify the proteoform existed in a studied species. However, the quality of sequences in customized databases which constructed by different strategies affect the performances of mass spectrometry (MS) identification. Additionally, performances of identifications between bottom-up and top-down using customized databases are also needed to be evaluated. RESULTS Three customized databases were constructed with different strategies separately. Two of them were based on translating assembled transcripts with or without genomic annotation, and the third one is a variant-extending protein database. By testing with bottom-up and top-down MS data separately, a variant-extending protein database could identify not only the most number of spectra but also the alleles expressed at the same time in diploid cells. An assembled database could identify the spectrum missed in reference database and amino acid (AA) alterations existed in studied species. AVAILABILITY AND IMPLEMENTATION Experimental results demonstrated that the proteoform sequences in an annotated database are more suitable for identifying AA alterations and peptide sequences missed in reference database. An unannotated database instead of a reference proteome database gets an enough high sensitivity of identifying mass spectra. The variant-extending reference database is the most sensitive to identify mass spectra and single AA variants. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziwei Li
- College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
| | - Bo He
- College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
| | - Weixing Feng
- College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
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Dai Y, Buxton KE, Schaffer LV, Miller RM, Millikin RJ, Scalf M, Frey BL, Shortreed MR, Smith LM. Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database. J Proteome Res 2019; 18:3671-3680. [PMID: 31479276 DOI: 10.1021/acs.jproteome.9b00339] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.
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Affiliation(s)
- Yunxiang Dai
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States.,Biophysics Graduate Program , University of Wisconsin , 413 Bock Laboratories, 1525 Linden Drive , Madison , Wisconsin 53706 , United States
| | - Katherine E Buxton
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Leah V Schaffer
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Rachel M Miller
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Robert J Millikin
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Mark Scalf
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Brian L Frey
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Michael R Shortreed
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
| | - Lloyd M Smith
- Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States
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Liu X, Xie L, Wu Z, Wang K, Zhao Z, Ruan J, Zhi D. The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications. BMC Bioinformatics 2018; 19:492. [PMID: 30591012 PMCID: PMC6309051 DOI: 10.1186/s12859-018-2460-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.
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Affiliation(s)
- Xiaoming Liu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Present address: College of Public Health, University of South Florida, Tampa, FL, 33612, USA.
| | - Lei Xie
- Department of Computer Science, Hunter College & The Graduate Center, The City University of New York, New York, NY, 10065, USA
| | - Zhijin Wu
- Department of Biostatistics, Brown University, Providence, RI, 02912, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - Degui Zhi
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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