1
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Joyce AW, Searle BC. Computational approaches to identify sites of phosphorylation. Proteomics 2024; 24:e2300088. [PMID: 37897210 DOI: 10.1002/pmic.202300088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.
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Affiliation(s)
- Alex W Joyce
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, USA
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2
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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3
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Zong Y, Wang Y, Yang Y, Zhao D, Wang X, Shen C, Qiao L. DeepFLR facilitates false localization rate control in phosphoproteomics. Nat Commun 2023; 14:2269. [PMID: 37080984 PMCID: PMC10119288 DOI: 10.1038/s41467-023-38035-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.
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Affiliation(s)
- Yu Zong
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Yuxin Wang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
- Department of Computer Science, and Institute of Modern Languages and Linguistics, Fudan University, Shanghai, China
| | - Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | | | | | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
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4
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Yang Y, Horvatovich P, Qiao L. Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation. J Proteome Res 2020; 20:634-644. [PMID: 32985198 DOI: 10.1021/acs.jproteome.0c00580] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been the most widely used technology for phosphoproteomics studies. As an alternative to database searching and probability-based phosphorylation site localization approaches, spectral library searching has been proved to be effective in the identification of phosphopeptides. However, incompletion of experimental spectral libraries limits the identification capability. Herein, we utilize MS/MS spectrum prediction coupled with spectral matching for site localization of phosphopeptides. In silico MS/MS spectra are generated from peptide sequences by deep learning/machine learning models trained with nonphosphopeptides. Then, mass shift according to phosphorylation sites, phosphoric acid neutral loss, and a "budding" strategy are adopted to adjust the in silico mass spectra. In silico MS/MS spectra can also be generated in one step for phosphopeptides using models trained with phosphopeptides. The method is benchmarked on data sets of synthetic phosphopeptides and is used to process real biological samples. It is demonstrated to be a method requiring only computational resources that supplements the probability-based approaches for phosphorylation site localization of singly and multiply phosphorylated peptides.
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Affiliation(s)
- Yi Yang
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Handan Road 220, Shanghai 200000, China
| | - Peter Horvatovich
- Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, Groningen 9700 AD, The Netherlands
| | - Liang Qiao
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Handan Road 220, Shanghai 200000, China
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5
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Hentschker C, Maaß S, Junker S, Hecker M, Hammerschmidt S, Otto A, Becher D. Comprehensive Spectral Library from the Pathogenic Bacterium Streptococcus pneumoniae with Focus on Phosphoproteins. J Proteome Res 2020; 19:1435-1446. [DOI: 10.1021/acs.jproteome.9b00615] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Christian Hentschker
- Department of Microbial Proteomics, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Sandra Maaß
- Department of Microbial Proteomics, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Sabryna Junker
- Department of Microbial Proteomics, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Michael Hecker
- Department of Microbial Physiology and Molecular Biology, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Sven Hammerschmidt
- Department of Molecular Genetics and Infection Biology, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Andreas Otto
- Department of Microbial Proteomics, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
| | - Dörte Becher
- Department of Microbial Proteomics, Institute of Microbiology; University of Greifswald, Felix-Hausdorff-Str. 8, 17489 Greifswald, Germany
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6
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Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites. PLoS One 2019; 14:e0225885. [PMID: 31790495 PMCID: PMC6886777 DOI: 10.1371/journal.pone.0225885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 11/14/2019] [Indexed: 12/31/2022] Open
Abstract
Tandem mass spectrometry (MS/MS) has been used in analysis of proteins and their post-translational modifications. A recently developed data analysis method, which simulates MS/MS spectra of phosphopeptides and performs spectral library searching using SpectraST, facilitates confident localization of phosphorylation sites. However, its performance has been evaluated only on MS/MS spectra acquired using Orbitrap HCD mass spectrometers so far. In this study, we have investigated whether this approach would be applicable to another type of mass spectrometers, and optimized the simulation and search conditions to achieve sensitive and confident site localization. Synthetic phosphopeptides and enriched K562 cell phosphopeptides were analyzed using a TripleTOF 6600 mass spectrometer before and after enzymatic dephosphorylation. Dephosphorylated peptides identified by X!Tandem database searching were subjected to spectral simulation of all possible single phosphorylations using SimPhospho software. Phosphopeptides were identified and localized by SpectraST searching against a library of the simulated spectra. Although no synthetic phosphopeptide was localized at 1% false localization rate under the previous conditions, optimization of the spectral simulation and search conditions for the TripleTOF datasets achieved the localization and improved the sensitivity. Furthermore, the optimized conditions enabled sensitive localization of K562 phosphopeptides at 1% false discovery and localization rates. These results suggest that accurate phosphopeptide simulation of TripleTOF MS/MS spectra is possible and the simulated spectral libraries can be used in SpectraST searching for confident localization of phosphorylation sites.
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7
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Buchowiecka AK. Modified cysteine S-phosphopeptide standards for mass spectrometry-based proteomics. Amino Acids 2019; 51:1365-1375. [DOI: 10.1007/s00726-019-02773-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 08/18/2019] [Indexed: 02/06/2023]
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8
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Junker S, Maaβ S, Otto A, Michalik S, Morgenroth F, Gerth U, Hecker M, Becher D. Spectral Library Based Analysis of Arginine Phosphorylations in Staphylococcus aureus. Mol Cell Proteomics 2018; 17:335-348. [PMID: 29183913 PMCID: PMC5795395 DOI: 10.1074/mcp.ra117.000378] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Indexed: 12/19/2022] Open
Abstract
Reversible protein phosphorylation is one of the major mechanisms in the regulation of protein expression and protein activity, controlling physiological functions of the important human pathogen Staphylococcus aureus Phosphorylations at serine, threonine and tyrosine are known to influence for example protein activity in central metabolic pathways and the more energy-rich phosphorylations at histidine, aspartate or cysteine can be found as part of two component system sensor domains or mediating bacterial virulence. In addition to these well-known phosphorylations, the phosphorylation at arginine residues plays an essential role. Hence, the deletion mutant S. aureus COL ΔptpB (protein tyrosine phosphatase B) was studied because the protein PtpB is assumed to be an arginine phosphatase. A gel-free approach was applied to analyze the changes in the phosphoproteome of the deletion mutant ΔptpB and the wild type in growing cells, thereby focusing on the occurrence of phosphorylation on arginine residues. In order to enhance the reliability of identified phosphorylation sites at arginine residues, a subset of arginine phosphorylated peptides was chemically synthesized. Combined spectral libraries based on phosphoenriched samples, synthetic arginine phosphorylated peptides and classical proteome samples provide a sophisticated tool for the analysis of arginine phosphorylations. This way, 212 proteins phosphorylated on serine, threonine, tyrosine or arginine residues were identified within the mutant ΔptpB and 102 in wild type samples. Among them, 207 arginine phosphosites were identified exclusively within the mutant ΔptpB, widely distributed along the whole bacterial metabolism. This identification of putative targets of PtpB allows further investigation of the physiological relevance of arginine phosphorylations and provides the basis for reliable quantification of arginine phosphorylations in bacteria.
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Affiliation(s)
- Sabryna Junker
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Sandra Maaβ
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Andreas Otto
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Stephan Michalik
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | | | - Ulf Gerth
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Michael Hecker
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Dörte Becher
- From the ‡Institute for Microbiology, University of Greifswald, Germany
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9
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Junker S, Maaβ S, Otto A, Michalik S, Morgenroth F, Gerth U, Hecker M, Becher D. Spectral Library Based Analysis of Arginine Phosphorylations in Staphylococcus aureus. MOLECULAR & CELLULAR PROTEOMICS : MCP 2017. [PMID: 29183913 DOI: 10.1074/mcp.ra117.000378.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Reversible protein phosphorylation is one of the major mechanisms in the regulation of protein expression and protein activity, controlling physiological functions of the important human pathogen Staphylococcus aureus Phosphorylations at serine, threonine and tyrosine are known to influence for example protein activity in central metabolic pathways and the more energy-rich phosphorylations at histidine, aspartate or cysteine can be found as part of two component system sensor domains or mediating bacterial virulence. In addition to these well-known phosphorylations, the phosphorylation at arginine residues plays an essential role. Hence, the deletion mutant S. aureus COL ΔptpB (protein tyrosine phosphatase B) was studied because the protein PtpB is assumed to be an arginine phosphatase. A gel-free approach was applied to analyze the changes in the phosphoproteome of the deletion mutant ΔptpB and the wild type in growing cells, thereby focusing on the occurrence of phosphorylation on arginine residues. In order to enhance the reliability of identified phosphorylation sites at arginine residues, a subset of arginine phosphorylated peptides was chemically synthesized. Combined spectral libraries based on phosphoenriched samples, synthetic arginine phosphorylated peptides and classical proteome samples provide a sophisticated tool for the analysis of arginine phosphorylations. This way, 212 proteins phosphorylated on serine, threonine, tyrosine or arginine residues were identified within the mutant ΔptpB and 102 in wild type samples. Among them, 207 arginine phosphosites were identified exclusively within the mutant ΔptpB, widely distributed along the whole bacterial metabolism. This identification of putative targets of PtpB allows further investigation of the physiological relevance of arginine phosphorylations and provides the basis for reliable quantification of arginine phosphorylations in bacteria.
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Affiliation(s)
- Sabryna Junker
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Sandra Maaβ
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Andreas Otto
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Stephan Michalik
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | | | - Ulf Gerth
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Michael Hecker
- From the ‡Institute for Microbiology, University of Greifswald, Germany
| | - Dörte Becher
- From the ‡Institute for Microbiology, University of Greifswald, Germany
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10
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Shao W, Lam H. Tandem mass spectral libraries of peptides and their roles in proteomics research. MASS SPECTROMETRY REVIEWS 2017; 36:634-648. [PMID: 27403644 DOI: 10.1002/mas.21512] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 05/21/2016] [Indexed: 05/15/2023]
Abstract
Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.
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Affiliation(s)
- Wenguang Shao
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Henry Lam
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
- Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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11
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Hu H, Khatri K, Zaia J. Algorithms and design strategies towards automated glycoproteomics analysis. MASS SPECTROMETRY REVIEWS 2017; 36:475-498. [PMID: 26728195 PMCID: PMC4931994 DOI: 10.1002/mas.21487] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 11/30/2015] [Indexed: 05/09/2023]
Abstract
Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.
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Affiliation(s)
- Han Hu
- Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Kshitij Khatri
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Joseph Zaia
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, Massachusetts 02118, USA
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12
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Lam MPY, Lau E, Ng DCM, Wang D, Ping P. Cardiovascular proteomics in the era of big data: experimental and computational advances. Clin Proteomics 2016; 13:23. [PMID: 27980500 PMCID: PMC5137214 DOI: 10.1186/s12014-016-9124-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Accepted: 08/24/2016] [Indexed: 01/14/2023] Open
Abstract
Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted.
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Affiliation(s)
- Maggie P Y Lam
- NIH BD2K Center of Excellence at UCLA; Department of Physiology, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA
| | - Edward Lau
- NIH BD2K Center of Excellence at UCLA; Department of Physiology, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA
| | - Dominic C M Ng
- NIH BD2K Center of Excellence at UCLA; Department of Physiology, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA
| | - Ding Wang
- NIH BD2K Center of Excellence at UCLA; Department of Physiology, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA
| | - Peipei Ping
- NIH BD2K Center of Excellence at UCLA; Department of Physiology, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA ; Department of Medicine, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA ; Department of Bioinformatics, University of California at Los Angeles, 675 Charles E. Young Drive, Los Angeles, CA 90095 USA
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13
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Griss J, Perez-Riverol Y, Lewis S, Tabb DL, Dianes JA, del-Toro N, Rurik M, Walzer MW, Kohlbacher O, Hermjakob H, Wang R, Vizcaíno JA. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat Methods 2016; 13:651-656. [PMID: 27493588 PMCID: PMC4968634 DOI: 10.1038/nmeth.3902] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 05/24/2016] [Indexed: 12/13/2022]
Abstract
Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.
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Affiliation(s)
- Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Steve Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - David L. Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville
| | - José A. Dianes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Noemi del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Marc Rurik
- Dept. of Computer Science, University of Tübingen, Germany
- Center for Bioinformatics, University of Tübingen, Germany
| | - Mathias W. Walzer
- Dept. of Computer Science, University of Tübingen, Germany
- Center for Bioinformatics, University of Tübingen, Germany
| | - Oliver Kohlbacher
- Dept. of Computer Science, University of Tübingen, Germany
- Center for Bioinformatics, University of Tübingen, Germany
- Quantitative Biology Center, University of Tübingen, Germany
- Max Planck Institute for Developmental Biology, Germany
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- National Center for Protein Sciences, Beijing, China
| | - Rui Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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14
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Pai PJ, Hu Y, Lam H. Direct glycan structure determination of intact N-linked glycopeptides by low-energy collision-induced dissociation tandem mass spectrometry and predicted spectral library searching. Anal Chim Acta 2016; 934:152-62. [DOI: 10.1016/j.aca.2016.05.049] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 05/24/2016] [Accepted: 05/30/2016] [Indexed: 11/24/2022]
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15
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Griss J. Spectral library searching in proteomics. Proteomics 2016; 16:729-40. [PMID: 26616598 DOI: 10.1002/pmic.201500296] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 10/15/2015] [Accepted: 10/29/2015] [Indexed: 12/12/2022]
Abstract
Spectral library searching has become a mature method to identify tandem mass spectra in proteomics data analysis. This review provides a comprehensive overview of available spectral library search engines and highlights their distinct features. Additionally, resources providing spectral libraries are summarized and tools presented that extend experimental spectral libraries by simulating spectra. Finally, spectrum clustering algorithms are discussed that utilize the same spectrum-to-spectrum matching algorithms as spectral library search engines and allow novel methods to analyse proteomics data.
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Affiliation(s)
- Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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16
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Affiliation(s)
- Nicholas M. Riley
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Joshua J. Coon
- Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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17
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Cho JY, Lee HJ, Jeong SK, Kim KY, Kwon KH, Yoo JS, Omenn GS, Baker MS, Hancock WS, Paik YK. Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:4959-66. [DOI: 10.1021/acs.jproteome.5b00578] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jin-Young Cho
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Hyoung-Joo Lee
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Seul-Ki Jeong
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Kwang-Youl Kim
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | | | | | - Gilbert S. Omenn
- Center
for Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor 48109, Michigan United States
| | - Mark S. Baker
- Department
of Biomedical Science, Faculty of Medicine and Health Science, Macquarie University, New South Wales 2109, Australia
| | | | - Young-Ki Paik
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
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18
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Large-scale label-free phosphoproteomics: from technology to data interpretation. Bioanalysis 2015; 6:2403-20. [PMID: 25384593 DOI: 10.4155/bio.14.188] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Protein phosphorylation plays a central role in the dynamic intracellular signaling and the control of biochemical pathways in all living cells. Recent advances in high-performance MS/MS-based technology make the large-scale identification and quantification of phosphorylation sites possible. Here, we review the full data generation pipeline, starting from sample preparation methods and LC-MS detection procedures, through to data processing and analysis software tools that facilitate the systematic comparative profiling of thousands of phosphoproteins in different biological specimens in a single experiment. We emphasize current challenges and promising avenues for the mechanistic interpretation and visualization of global phosphorylation networks and their relevance to human health and disease.
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19
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Suni V, Imanishi SY, Maiolica A, Aebersold R, Corthals GL. Confident Site Localization Using a Simulated Phosphopeptide Spectral Library. J Proteome Res 2015; 14:2348-59. [DOI: 10.1021/acs.jproteome.5b00050] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Veronika Suni
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistokatu 6, FI-20520 Turku, Finland
- Turku Centre for Computer Science, Joukahaisenkatu 3-5 B, FI-20520 Turku, Finland
| | - Susumu Y. Imanishi
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistokatu 6, FI-20520 Turku, Finland
| | - Alessio Maiolica
- Department
of Biology, Institute of Molecular Systems Biology, ETH Zurich, HPT E 51,
Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland
| | - Ruedi Aebersold
- Department
of Biology, Institute of Molecular Systems Biology, ETH Zurich, HPT E 51,
Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland
- Faculty
of Science, University of Zurich, 8057 Zurich, Switzerland
| | - Garry L. Corthals
- Turku
Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistokatu 6, FI-20520 Turku, Finland
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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20
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Lee DCH, Jones AR, Hubbard SJ. Computational phosphoproteomics: from identification to localization. Proteomics 2015; 15:950-63. [PMID: 25475148 PMCID: PMC4384807 DOI: 10.1002/pmic.201400372] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 10/31/2014] [Accepted: 11/26/2014] [Indexed: 01/08/2023]
Abstract
Analysis of the phosphoproteome by MS has become a key technology for the characterization of dynamic regulatory processes in the cell, since kinase and phosphatase action underlie many major biological functions. However, the addition of a phosphate group to a suitable side chain often confounds informatic analysis by generating product ion spectra that are more difficult to interpret (and consequently identify) relative to unmodified peptides. Collectively, these challenges have motivated bioinformaticians to create novel software tools and pipelines to assist in the identification of phosphopeptides in proteomic mixtures, and help pinpoint or "localize" the most likely site of modification in cases where there is ambiguity. Here we review the challenges to be met and the informatics solutions available to address them for phosphoproteomic analysis, as well as highlighting the difficulties associated with using them and the implications for data standards.
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Affiliation(s)
- Dave C H Lee
- Faculty of Life Sciences, University of ManchesterManchester, UK
| | - Andrew R Jones
- Institute of Integrative Biology, University of LiverpoolLiverpool, UK
| | - Simon J Hubbard
- Faculty of Life Sciences, University of ManchesterManchester, UK
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21
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Buchowiecka AK. Puzzling over protein cysteine phosphorylation – assessment of proteomic tools for S-phosphorylation profiling. Analyst 2014; 139:4118-23. [DOI: 10.1039/c4an00724g] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The article provides useful information necessary for designing experiments in the emerging cysteine phosphoproteomics.
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Affiliation(s)
- A. K. Buchowiecka
- Institute of Technical Biochemistry
- Lodz University of Technology
- 90-924 Lodz, Poland
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