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Na S, Paek E. Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls. Methods Mol Biol 2024; 2836:37-55. [PMID: 38995534 DOI: 10.1007/978-1-0716-4007-4_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.
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Affiliation(s)
- Seungjin Na
- Digital Omics Research Center, Korea Basic Science Institute, Cheongju, South Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul, South Korea.
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea.
- Institute for Artificial Intelligence Research, Hanyang University, Seoul, South Korea.
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Na S, Paek E. Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions. Comput Struct Biotechnol J 2020; 18:1391-1402. [PMID: 32637038 PMCID: PMC7322682 DOI: 10.1016/j.csbj.2020.06.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/01/2020] [Accepted: 06/01/2020] [Indexed: 12/28/2022] Open
Abstract
Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.
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Affiliation(s)
- Seungjin Na
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Eunok Paek
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
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3
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Na S, Kim J, Paek E. MODplus: Robust and Unrestrictive Identification of Post-Translational Modifications Using Mass Spectrometry. Anal Chem 2019; 91:11324-11333. [PMID: 31365238 DOI: 10.1021/acs.analchem.9b02445] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Post-translational modifications regulate various cellular processes and are of great biological interest. Unrestrictive searches of mass spectrometry data enable the detection of any type of modification. Here we propose MODplus, which makes practical unrestrictive searches possible by allowing (1) hundreds of modifications, (2) multiple modifications per peptide, (3) the whole proteome database, and (4) any tolerant values in search parameters. The utility of MODplus was demonstrated in large human data sets of HEK293 cells and TMT-labeled phosphorylation enrichment. Notably, MODplus supports identifying different modification types at multiple sites and reports real chemical and biological modifications, as it has been very labor intensive to link unrestrictive search results to real modifications. We also confirmed the presence of Missing Precursor (MP) spectra that were not identifiable using targeted precursor masses. The MP spectra mostly resulted in identifications of wrong modifications and negatively affected the overall performance, often by as much as 10%. MODplus can rapidly recognize MP spectra and correct their identifications, resulting in increased identification rate up to 70% in the HEK293 data set as well as improved reliability.
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Affiliation(s)
- Seungjin Na
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
| | - Jihyung Kim
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
| | - Eunok Paek
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
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Rieder V, Schork KU, Kerschke L, Blank-Landeshammer B, Sickmann A, Rahnenführer J. Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra. J Proteome Res 2017; 16:4035-4044. [DOI: 10.1021/acs.jproteome.7b00427] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Vera Rieder
- Department
of Statistics, TU Dortmund University, 44221 Dortmund, Germany
| | - Karin U. Schork
- Department
of Statistics, TU Dortmund University, 44221 Dortmund, Germany
- Medizinische
Fakultät, Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
| | - Laura Kerschke
- Department
of Statistics, TU Dortmund University, 44221 Dortmund, Germany
- Institut für Biometrie und Klinische Forschung (IBKF) der Westfälischen Wilhelms-Universität und des Universitätsklinikums Münster, 48149 Münster, Germany
| | | | - Albert Sickmann
- Medizinische
Fakultät, Medizinisches Proteom-Center, Ruhr-University Bochum, 44801 Bochum, Germany
- Leibniz-Institut für Analytische Wissenschaften-ISAS - e.V., 44139 Dortmund, Germany
- Department
of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, Scotland, United Kingdom
| | - Jörg Rahnenführer
- Department
of Statistics, TU Dortmund University, 44221 Dortmund, Germany
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Ruggles KV, Krug K, Wang X, Clauser KR, Wang J, Payne SH, Fenyö D, Zhang B, Mani DR. Methods, Tools and Current Perspectives in Proteogenomics. Mol Cell Proteomics 2017; 16:959-981. [PMID: 28456751 DOI: 10.1074/mcp.mr117.000024] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Indexed: 12/20/2022] Open
Abstract
With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.
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Affiliation(s)
- Kelly V Ruggles
- From the ‡Department of Medicine, New York University School of Medicine, New York, New York 10016
| | - Karsten Krug
- §The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
| | - Xiaojing Wang
- ¶Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030.,‖Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
| | - Karl R Clauser
- §The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
| | - Jing Wang
- ¶Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030.,‖Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
| | - Samuel H Payne
- **Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354
| | - David Fenyö
- ‡‡Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York 10016; .,§§Institute for Systems Genetics, New York University School of Medicine, New York, New York 10016
| | - Bing Zhang
- ¶Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030; .,‖Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030
| | - D R Mani
- §The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142;
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Deutsch EW, Csordas A, Sun Z, Jarnuczak A, Perez-Riverol Y, Ternent T, Campbell DS, Bernal-Llinares M, Okuda S, Kawano S, Moritz RL, Carver JJ, Wang M, Ishihama Y, Bandeira N, Hermjakob H, Vizcaíno JA. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res 2016; 45:D1100-D1106. [PMID: 27924013 PMCID: PMC5210636 DOI: 10.1093/nar/gkw936] [Citation(s) in RCA: 670] [Impact Index Per Article: 83.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 10/07/2016] [Indexed: 11/13/2022] Open
Abstract
The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components. We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.
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Affiliation(s)
| | - Attila Csordas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Zhi Sun
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Andrew Jarnuczak
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Tobias Ternent
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Manuel Bernal-Llinares
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Shin Kawano
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa 277-0871, Japan
| | | | - Jeremy J Carver
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Mingxun Wang
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.,National Center for Protein Sciences, Beijing, China
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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