1
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Kang C, Huh S, Nam D, Kim H, Hong J, Hwang D, Lee SW. Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome. Anal Chem 2022; 94:12185-12195. [PMID: 35994246 DOI: 10.1021/acs.analchem.2c02641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.
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Affiliation(s)
- Chaewon Kang
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 02841, Republic of Korea
| | - Sunghyun Huh
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.,Bertis R&D Division, Bertis Inc., Seongnam-si, Gyeonggi-do 13605, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 02841, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 02841, Republic of Korea
| | - Jiwon Hong
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 02841, Republic of Korea
| | - Daehee Hwang
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.,Bioinformatics Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 02841, Republic of Korea
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2
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Huh S, Kang C, Park JE, Nam D, Kim SI, Seol A, Choi K, Hwang D, Yu MH, Chung HH, Lee SW, Kang UB. Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry. J Proteome Res 2022; 21:2146-2159. [PMID: 35939567 DOI: 10.1021/acs.jproteome.2c00218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor β signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).
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Affiliation(s)
- Sunghyun Huh
- Bertis R&D Division, Bertis Inc., Seongnam-si, Gyeonggi-do 13605, Republic of Korea
| | - Chaewon Kang
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Ji Eun Park
- Bertis R&D Division, Bertis Inc., Seongnam-si, Gyeonggi-do 13605, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Se Ik Kim
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Aeran Seol
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Obstetrics and Gynecology, Korea University Medical Center, Seoul 02843, Republic of Korea
| | - Kyerim Choi
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Daehee Hwang
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.,Bioinformatics Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Myeong-Hee Yu
- Bertis R&D Division, Bertis Inc., Seongnam-si, Gyeonggi-do 13605, Republic of Korea
| | - Hyun Hoon Chung
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Un-Beom Kang
- Bertis R&D Division, Bertis Inc., Seongnam-si, Gyeonggi-do 13605, Republic of Korea
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3
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Bae JW, Kim S, Kim VN, Kim JS. Photoactivatable ribonucleosides mark base-specific RNA-binding sites. Nat Commun 2021; 12:6026. [PMID: 34654832 PMCID: PMC8519950 DOI: 10.1038/s41467-021-26317-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
RNA-protein interaction can be captured by crosslinking and enrichment followed by tandem mass spectrometry, but it remains challenging to pinpoint RNA-binding sites (RBSs) or provide direct evidence for RNA-binding. To overcome these limitations, we here developed pRBS-ID, by incorporating the benefits of UVA-based photoactivatable ribonucleoside (PAR; 4-thiouridine and 6-thioguanosine) crosslinking and chemical RNA cleavage. pRBS-ID robustly detects peptides crosslinked to PAR adducts, offering direct RNA-binding evidence and identifying RBSs at single amino acid-resolution with base-specificity (U or G). Using pRBS-ID, we could profile uridine-contacting RBSs globally and discover guanosine-contacting RBSs, which allowed us to characterize the base-specific interactions. We also applied the search pipeline to analyze the datasets from UVC-based RBS-ID experiments, altogether offering a comprehensive list of human RBSs with high coverage (3,077 RBSs in 532 proteins in total). pRBS-ID is a widely applicable platform to investigate the molecular basis of posttranscriptional regulation.
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Affiliation(s)
- Jong Woo Bae
- Center for RNA Research, Institute for Basic Science, Seoul, 08826, Korea
- School of Biological Sciences, Seoul National University, Seoul, 08826, Korea
| | | | - V Narry Kim
- Center for RNA Research, Institute for Basic Science, Seoul, 08826, Korea.
- School of Biological Sciences, Seoul National University, Seoul, 08826, Korea.
| | - Jong-Seo Kim
- Center for RNA Research, Institute for Basic Science, Seoul, 08826, Korea.
- School of Biological Sciences, Seoul National University, Seoul, 08826, Korea.
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4
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Solovyeva EM, Moshkovskii SA, Gorshkov MV. Identification-Free Control over the Precursor Isotopic Mass Misassignment in Orbitrap-Based Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:218-224. [PMID: 33119294 DOI: 10.1021/jasms.0c00281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Selection of a precursor ion from a peptide isotopic cluster to obtain a fragmentation mass spectrum is a crucial step in data-dependent proteome analysis. However, the monoisotopic mass assignment performed in this step is often an issue confronted by the data acquisition software of hybrid Orbitrap FTMS that is most widely used in proteomics. To address the problem, many data processing tools, such as raw data converters and search engines, have optional accounting for the precursor mass shift due to the isotopic error. These solutions require additional data preprocessing steps and lead to an increase in the search space, thus making the analysis longer and/or less reliable. In this work, we processed 100 Orbitrap-based LC-MS/MS runs from 10 publicly available data sets to examine the rate of precursor isotope misassignment. The effect from taking the isotope error into account during the search on the number of identified peptides varied in a wide range from 0 to 33%. Thus, it may be tempting to spend extra time before or during a search to account for the mass assignment issue. Alternatively, this effect can be predicted a priori using an identification-free metric, which can be a part of data quality control software. Based on the results obtained in this work, we propose such a metric be further added into the visual and intuitive quality control software, viQC, developed previously and available at https://github.com/lisavetasol/viQC. It takes about a minute to calculate and plot nine quality metrics, including the proposed one for typical proteome analysis.
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Affiliation(s)
- Elizaveta M Solovyeva
- Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141701, Russia
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Sergei A Moshkovskii
- Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Federal Research and Clinical Center of Physical-Chemical Medicine, Moscow 119435, Russia
| | - Mikhail V Gorshkov
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
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5
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Mun DG, Renuse S, Saraswat M, Madugundu A, Udainiya S, Kim H, Park SKR, Zhao H, Nirujogi RS, Na CH, Kannan N, Yates JR, Lee SW, Pandey A. PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies. Anal Chem 2020; 92:14466-14475. [PMID: 33079518 DOI: 10.1021/acs.analchem.0c02513] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.
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Affiliation(s)
- Dong-Gi Mun
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Santosh Renuse
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Mayank Saraswat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Anil Madugundu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Savita Udainiya
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Sung-Kyu Robin Park
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Hui Zhao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Raja Sekhar Nirujogi
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 5EH, United Kingdom
| | - Chan Hyun Na
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.,Neurology, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - John R Yates
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
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6
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Schweppe DK, Eng JK, Yu Q, Bailey D, Rad R, Navarrete-Perea J, Huttlin EL, Erickson BK, Paulo JA, Gygi SP. Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics. J Proteome Res 2020; 19:2026-2034. [PMID: 32126768 DOI: 10.1021/acs.jproteome.9b00860] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method's longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter's online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 ms. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiter's RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 h for RTS, 36 h for SPS-MS3).
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Affiliation(s)
- Devin K Schweppe
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Jimmy K Eng
- University of Washington Proteomics Resource, Seattle, Washington 98109, United States
| | - Qing Yu
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Derek Bailey
- Thermo Scientific LSMS, San Jose, California 95134, United States
| | - Ramin Rad
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Jose Navarrete-Perea
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Edward L Huttlin
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Brian K Erickson
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Joao A Paulo
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
| | - Steven P Gygi
- Harvard Medical School, Department of Cell Biology, Cambridge, Massachusetts 02155, United States
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7
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Sun J, Shi J, Wang Y, Wu S, Zhao L, Li Y, Wang H, Chang L, Lyu Z, Wu J, Liu F, Li W, He F, Zhang Y, Xu P. Open-pFind Enhances the Identification of Missing Proteins from Human Testis Tissue. J Proteome Res 2019; 18:4189-4196. [DOI: 10.1021/acs.jproteome.9b00376] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Jinshuai Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Jiahui Shi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Yihao Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shujia Wu
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
| | - Liping Zhao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Guizhou University School of Medicine, Guiyang 550025, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Hong Wang
- School of Public Health, North China University Science and Technology, Tangshan 063210, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zhitang Lyu
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Junzhu Wu
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
| | - Fengsong Liu
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Wenjun Li
- State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yao Zhang
- State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
- Guizhou University School of Medicine, Guiyang 550025, China
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8
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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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9
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Mun DG, Nam D, Kim H, Pandey A, Lee SW. Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry. Anal Chem 2019; 91:8453-8460. [DOI: 10.1021/acs.analchem.9b01474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Dong-Gi Mun
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55902, United States
- Manipal Academy of Higher Education (MAHE), Manipal, 576104 Karnataka, India
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
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10
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Chi H, Liu C, Yang H, Zeng WF, Wu L, Zhou WJ, Wang RM, Niu XN, Ding YH, Zhang Y, Wang ZW, Chen ZL, Sun RX, Liu T, Tan GM, Dong MQ, Xu P, Zhang PH, He SM. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat Biotechnol 2018; 36:nbt.4236. [PMID: 30295672 DOI: 10.1038/nbt.4236] [Citation(s) in RCA: 193] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 08/03/2018] [Indexed: 12/27/2022]
Abstract
We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.
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Affiliation(s)
- Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chao Liu
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hao Yang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wen-Feng Zeng
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Long Wu
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wen-Jing Zhou
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Rui-Min Wang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiu-Nan Niu
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yue-He Ding
- National Institute of Biological Sciences, Beijing, Beijing, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
- State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, College of Ecology and Evolution, Sun Yat-Sen University, Guangzhou, China
| | - Zhao-Wei Wang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhen-Lin Chen
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Rui-Xiang Sun
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tao Liu
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Guang-Ming Tan
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Meng-Qiu Dong
- National Institute of Biological Sciences, Beijing, Beijing, China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Pei-Heng Zhang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Si-Min He
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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