1
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Zahedipour F, Jamialahmadi K, Zamani P, Reza Jaafari M. Improving the efficacy of peptide vaccines in cancer immunotherapy. Int Immunopharmacol 2023; 123:110721. [PMID: 37543011 DOI: 10.1016/j.intimp.2023.110721] [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] [Received: 05/24/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
Peptide vaccines have shown great potential in cancer immunotherapy by targeting tumor antigens and activating the patient's immune system to mount a specific response against cancer cells. However, the efficacy of peptide vaccines in inducing a sustained immune response and achieving clinical benefit remains a major challenge. In this review, we discuss the current status of peptide vaccines in cancer immunotherapy and strategies to improve their efficacy. We summarize the recent advancements in the development of peptide vaccines in pre-clinical and clinical settings, including the use of novel adjuvants, neoantigens, nano-delivery systems, and combination therapies. We also highlight the importance of personalized cancer vaccines, which consider the unique genetic and immunological profiles of individual patients. We also discuss the strategies to enhance the immunogenicity of peptide vaccines such as multivalent peptides, conjugated peptides, fusion proteins, and self-assembled peptides. Although, peptide vaccines alone are weak immunogens, combining peptide vaccines with other immunotherapeutic approaches and developing novel approaches such as personalized vaccines can be promising methods to significantly enhance their efficacy and improve the clinical outcomes for cancer patients.
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Affiliation(s)
- Fatemeh Zahedipour
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Khadijeh Jamialahmadi
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Parvin Zamani
- Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahmoud Reza Jaafari
- Nanotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Pharmaceutical Nanotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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2
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Jalaludin I, Lubman DM, Kim J. A guide to mass spectrometric analysis of extracellular vesicle proteins for biomarker discovery. MASS SPECTROMETRY REVIEWS 2023; 42:844-872. [PMID: 34747512 DOI: 10.1002/mas.21749] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 06/13/2023]
Abstract
Exosomes (small extracellular vesicles) in living organisms play an important role in processes such as cell proliferation or intercellular communication. Recently, exosomes have been extensively investigated for biomarker discoveries for various diseases. An important aspect of exosome analysis involves the development of enrichment methods that have been introduced for successful isolation of exosomes. These methods include ultracentrifugation, size exclusion chromatography, polyethylene glycol-based precipitation, immunoaffinity-based enrichment, ultrafiltration, and asymmetric flow field-flow fractionation among others. To confirm the presence of exosomes, various characterization methods have been utilized such as Western blot analysis, atomic force microscopy, electron microscopy, optical methods, zeta potential, visual inspection, and mass spectrometry. Recent advances in high-resolution separations, high-performance mass spectrometry and comprehensive proteome databases have all contributed to the successful analysis of exosomes from patient samples. Herein we review various exosome enrichment methods, characterization methods, and recent trends of exosome investigations using mass spectrometry-based approaches for biomarker discovery.
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Affiliation(s)
- Iqbal Jalaludin
- Department of Chemistry, Chungnam National University, Daejeon, Republic of Korea
| | - David M Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan, USA
| | - Jeongkwon Kim
- Department of Chemistry, Chungnam National University, Daejeon, Republic of Korea
- Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon, Republic of Korea
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3
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Abstract
Bioactive peptides with high potency against numerous human disorders have been regarded as a promising therapy in disease control. These peptides could be released from various dietary protein sources through hydrolysis processing using physical conditions, chemical agents, microbial fermentation, or enzymatic digestions. Considering the diversity of the original proteins and the complexity of the multiple structural peptides that existed in the hydrolysis mixture, the screening of bioactive peptides will be a challenge task. Well-organized and well-designed methods are necessarily required to enhance the efficiency of studying the potential peptides. This article, hence, provides an overview of bioactive peptides with an emphasis on the current strategy used for screening and characterization methods. Moreover, the understanding of the biological activities of peptides, mechanism inhibitions, and the interaction of the complex of peptide–enzyme is commonly evaluated using specific in vitro assays and molecular docking analysis.
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4
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Identification of tumor antigens with immunopeptidomics. Nat Biotechnol 2021; 40:175-188. [PMID: 34635837 DOI: 10.1038/s41587-021-01038-8] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 07/29/2021] [Indexed: 12/18/2022]
Abstract
The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.
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5
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Characteristics of Food Protein-Derived Antidiabetic Bioactive Peptides: A Literature Update. Int J Mol Sci 2021; 22:ijms22179508. [PMID: 34502417 PMCID: PMC8431147 DOI: 10.3390/ijms22179508] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/29/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Diabetes, a glucose metabolic disorder, is considered one of the biggest challenges associated with a complex complication of health crises in the modern lifestyle. Inhibition or reduction of the dipeptidyl peptidase IV (DPP-IV), alpha-glucosidase, and protein-tyrosine phosphatase 1B (PTP-1B) enzyme activities or expressions are notably considered as the promising therapeutic strategies for the management of type 2 diabetes (T2D). Various food protein-derived antidiabetic bioactive peptides have been isolated and verified. This review provides an overview of the DPP-IV, PTP-1B, and α-glucosidase inhibitors, and updates on the methods for the discovery of DPP-IV inhibitory peptides released from food-protein hydrolysate. The finding of novel bioactive peptides involves studies about the strategy of separation fractionation, the identification of peptide sequences, and the evaluation of peptide characteristics in vitro, in silico, in situ, and in vivo. The potential of bioactive peptides suggests useful applications in the prevention and management of diabetes. Furthermore, evidence of clinical studies is necessary for the validation of these peptides’ efficiencies before commercial applications.
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6
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Willems P, Fels U, Staes A, Gevaert K, Van Damme P. Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling. J Proteome Res 2021; 20:1165-1177. [PMID: 33467856 PMCID: PMC7871992 DOI: 10.1021/acs.jproteome.0c00350] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Indexed: 01/01/2023]
Abstract
In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.
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Affiliation(s)
- Patrick Willems
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
- Department
of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9000, Belgium
- VIB-UGent
Center for Plant Systems Biology, Ghent 9052, Belgium
| | - Ursula Fels
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
| | - An Staes
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
- Department
of Biomolecular Medicine, Ghent University, Ghent 9000, Belgium
| | - Kris Gevaert
- VIB-UGent
Center for Medical Biotechnology, Ghent 9052, Belgium
- Department
of Biomolecular Medicine, Ghent University, Ghent 9000, Belgium
| | - Petra Van Damme
- Department
of Biochemistry and Microbiology, Ghent
University, Ghent 9000, Belgium
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7
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Agten A, Van Houtven J, Askenazi M, Burzykowski T, Laukens K, Valkenborg D. Visualizing the agreement of peptide assignments between different search engines. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4471. [PMID: 31713933 DOI: 10.1002/jms.4471] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 06/10/2023]
Abstract
There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit-for-purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide-spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in-house data set of our benchmark organism, Caenorhabditis elegans, and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.
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Affiliation(s)
- Annelies Agten
- Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
| | - Joris Van Houtven
- Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
- UA-VITO Center for Proteomics, University of Antwerp, Antwerp, Belgium
- Applied Bio and Molecular Systems, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | | | - Tomasz Burzykowski
- Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Network Antwerp (biomina), University of Antwerp, Antwerp, Belgium
| | - Dirk Valkenborg
- Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
- UA-VITO Center for Proteomics, University of Antwerp, Antwerp, Belgium
- Applied Bio and Molecular Systems, Flemish Institute for Technological Research (VITO), Mol, Belgium
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8
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Yee WLS, Drum CL. Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring. ADVANCED THERAPEUTICS 2020. [DOI: 10.1002/adtp.201900163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Wei Loong Sherman Yee
- Yong Loo Lin School of MedicineDepartment of MedicineNational University of Singapore Singapore 119077 Singapore
- Cardiovascular Research Institute (CVRI)National University Health System Singapore 119228 Singapore
| | - Chester Lee Drum
- Yong Loo Lin School of MedicineDepartment of MedicineNational University of Singapore Singapore 119077 Singapore
- Cardiovascular Research Institute (CVRI)National University Health System Singapore 119228 Singapore
- Yong Loo Lin School of MedicineDepartment of BiochemistryNational University of Singapore Singapore 119077 Singapore
- The N.1 Institute for Health (N.1)National University of Singapore Singapore 119077 Singapore
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9
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Kind T, Tsugawa H, Cajka T, Ma Y, Lai Z, Mehta SS, Wohlgemuth G, Barupal DK, Showalter MR, Arita M, Fiehn O. Identification of small molecules using accurate mass MS/MS search. MASS SPECTROMETRY REVIEWS 2018; 37:513-532. [PMID: 28436590 PMCID: PMC8106966 DOI: 10.1002/mas.21535] [Citation(s) in RCA: 248] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 03/17/2017] [Accepted: 03/18/2017] [Indexed: 05/03/2023]
Abstract
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.
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Affiliation(s)
- Tobias Kind
- Genome Center, Metabolomics, UC Davis, Davis, California
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Tomas Cajka
- Genome Center, Metabolomics, UC Davis, Davis, California
| | - Yan Ma
- National Institute of Biological Sciences, Beijing, People’s Republic of China
| | - Zijuan Lai
- Genome Center, Metabolomics, UC Davis, Davis, California
| | | | | | | | | | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Oliver Fiehn
- Genome Center, Metabolomics, UC Davis, Davis, California
- Faculty of Sciences, Department of Biochemistry, King Abdulaziz University, Jeddah, Saudi Arabia
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10
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Rodriguez-Furlan C, Zhang C, Raikhel N, Hicks GR. Drug Affinity Responsive Target Stability (DARTS) to Resolve Protein-Small Molecule Interaction in Arabidopsis. ACTA ACUST UNITED AC 2017; 2:370-378. [PMID: 33383985 DOI: 10.1002/cppb.20062] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Target identification remains a challenging step in plant chemical genomics approaches. Drug affinity responsive target stability (DARTS) represents a straightforward technique to identify small molecules' protein targets and assist in the characterization of interactions between small molecules and putative targets identified by other methods. When a small molecule interacts with a protein, it has the potential to stabilize the protein's structure, resulting in a reduced susceptibility to protease action. During the DARTS procedure, protein extracts are treated with proteolytic enzymes, and only proteins that bind to the small molecule are protected from proteolysis. DARTS represents a protocol independent of the molecule's mechanism of action or chemical structure. Another advantage of DARTS is that it does not require additional modifications or tagging of the small molecule. The protocols outlined in this article describe in detail the DARTS technique applied to plant proteins and propose several detection procedures according to protein abundance. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Cecilia Rodriguez-Furlan
- Center for Plant Cell Biology, Institute for Integrative Genome Biology, and Department of Botany and Plant Sciences, University of California, Riverside, California
| | - Chunhua Zhang
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana.,Purdue Center for Plant Biology, Purdue University, West Lafayette, Indiana
| | - Natasha Raikhel
- Center for Plant Cell Biology, Institute for Integrative Genome Biology, and Department of Botany and Plant Sciences, University of California, Riverside, California
| | - Glenn R Hicks
- Center for Plant Cell Biology, Institute for Integrative Genome Biology, and Department of Botany and Plant Sciences, University of California, Riverside, California
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11
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Barbieri R, Guryev V, Brandsma CA, Suits F, Bischoff R, Horvatovich P. Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 926:21-47. [PMID: 27686804 DOI: 10.1007/978-3-319-42316-6_3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.
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Affiliation(s)
- Ruggero Barbieri
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Corry-Anke Brandsma
- Department of Pathology & Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Frank Suits
- IBM T.J. Watson Research Centre, 1101 Kitchawan Road, Yorktown Heights, New York, 10598, NY, USA
| | - Rainer Bischoff
- Department of Analytical Biochemistry, Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Peter Horvatovich
- Department of Analytical Biochemistry, Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
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12
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Schräder CU, Lee L, Rey M, Sarpe V, Man P, Sharma S, Zabrouskov V, Larsen B, Schriemer DC. Neprosin, a Selective Prolyl Endoprotease for Bottom-up Proteomics and Histone Mapping. Mol Cell Proteomics 2017; 16:1162-1171. [PMID: 28404794 DOI: 10.1074/mcp.m116.066803] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 03/07/2017] [Indexed: 01/10/2023] Open
Abstract
Trypsin dominates bottom-up proteomics, but there are reasons to consider alternative enzymes. Improving sequence coverage, exposing proteomic "dark matter," and clustering post-translational modifications in different ways and with higher-order drive the pursuit of reagents complementary to trypsin. Additionally, enzymes that are easy to use and generate larger peptides that capitalize upon newer fragmentation technologies should have a place in proteomics. We expressed and characterized recombinant neprosin, a novel prolyl endoprotease of the DUF239 family, which preferentially cleaves C-terminal to proline residues under highly acidic conditions. Cleavage also occurs C-terminal to alanine with some frequency, but with an intriguingly high "skipping rate." Digestion proceeds to a stable end point, resulting in an average peptide mass of 2521 units and a higher dependence upon electron-transfer dissociation for peptide-spectrum matches. In contrast to most proline-cleaving enzymes, neprosin effectively degrades proteins of any size. For 1251 HeLa cell proteins identified in common using trypsin, Lys-C, and neprosin, almost 50% of the neprosin sequence contribution is unique. The high average peptide mass coupled with cleavage at residues not usually modified provide new opportunities for profiling clusters of post-translational modifications. We show that neprosin is a useful reagent for reading epigenetic marks on histones. It generates peptide 1-38 of histone H3 and peptide 1-32 of histone H4 in a single digest, permitting the analysis of co-occurring post-translational modifications in these important N-terminal tails.
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Affiliation(s)
- Christoph U Schräder
- From the ‡Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N4N1, Canada
| | - Linda Lee
- From the ‡Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N4N1, Canada
| | - Martial Rey
- From the ‡Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N4N1, Canada
| | - Vladimir Sarpe
- From the ‡Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N4N1, Canada
| | - Petr Man
- §BioCev-Institute of Microbiology, Czech Academy of Sciences, Vestec, Czech Republic 117 20.,¶Department of Biochemistry, Faculty of Science, Charles University in Prague, Prague, Czech Republic 116 36
| | - Seema Sharma
- ‖Thermo Fisher Scientific, San Jose, California 95134
| | | | - Brett Larsen
- **Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada M5G 1X5; and
| | - David C Schriemer
- From the ‡Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta T2N4N1, Canada; .,‡‡Department of Chemistry, University of Calgary, Calgary, Alberta, Canada T2N 4N1
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13
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Wu JX, Song X, Pascovici D, Zaw T, Care N, Krisp C, Molloy MP. SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries. Mol Cell Proteomics 2016; 15:2501-14. [PMID: 27161445 DOI: 10.1074/mcp.m115.055558] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Indexed: 12/26/2022] Open
Abstract
The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries.
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Affiliation(s)
- Jemma X Wu
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Xiaomin Song
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Dana Pascovici
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Thiri Zaw
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Natasha Care
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Christoph Krisp
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Mark P Molloy
- From the ‡Australian Proteome Analysis Facility (APAF), Dept. Chemistry & Biomolecular Sciences, Macquarie University, Sydney, Australia
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14
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Lehmann L, Rønnest NP, Jørgensen CI, Olsson L, Stocks SM, Jørgensen HS, Hobley T. Linking hydrolysis performance to Trichoderma reesei cellulolytic enzyme profile. Biotechnol Bioeng 2015; 113:1001-10. [PMID: 26524197 DOI: 10.1002/bit.25871] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Revised: 10/22/2015] [Accepted: 10/26/2015] [Indexed: 11/07/2022]
Abstract
Trichoderma reesei expresses a large number of enzymes involved in lignocellulose hydrolysis and the mechanism of how these enzymes work together is too complex to study by traditional methods, for example, by spiking with single enzymes and monitoring hydrolysis performance. In this study, a multivariate approach, partial least squares regression, was used to see whether it could help explain the correlation between enzyme profile and hydrolysis performance. Diverse enzyme mixtures were produced by T. reesei Rut-C30 by exploiting various fermentation conditions and used for hydrolysis of washed pretreated corn stover as a measure of enzyme performance. In addition, the enzyme mixtures were analyzed by liquid chromatography-tandem mass spectrometry to identify and quantify the different proteins. A multivariate model was applied for the prediction of enzyme performance based on the combination of different proteins present in an enzyme mixture. The multivariate model was used for identification of candidate proteins that are correlated to enzyme performance on pretreated corn stover. A very large variation in hydrolysis performance was observed and this was clearly caused by the difference in fermentation conditions. Besides β-glucosidase, the multivariate model identified several xylanases, Cip1 and Cip2, as relevant proteins to study further.
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Affiliation(s)
- Linda Lehmann
- Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
| | - Nanna P Rønnest
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | | | - Lisbeth Olsson
- Industrial Biotechnology, Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | | | | | - Timothy Hobley
- Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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15
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Eng JK, Hoopmann MR, Jahan TA, Egertson JD, Noble WS, MacCoss MJ. A deeper look into Comet--implementation and features. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2015; 26:1865-74. [PMID: 26115965 PMCID: PMC4607604 DOI: 10.1007/s13361-015-1179-x] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Revised: 04/22/2015] [Accepted: 04/27/2015] [Indexed: 05/04/2023]
Abstract
The Comet database search software was initially released as an open source project in late 2012. Prior to that, Comet existed as the University of Washington's academic version of the SEQUEST database search tool. Despite its availability and widespread use over the years, some details about its implementation have not been previously disseminated or are not well understood. We address a few of these details in depth and highlight new features available in the latest release. Comet is freely available for download at http://comet-ms.sourceforge.net or it can be accessed as a component of a number of larger software projects into which it has been incorporated. Graphical Abstract ᅟ.
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Affiliation(s)
- Jimmy K Eng
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | | | - Tahmina A Jahan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jarrett D Egertson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
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16
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Sunagar K, Morgenstern D, Reitzel AM, Moran Y. Ecological venomics: How genomics, transcriptomics and proteomics can shed new light on the ecology and evolution of venom. J Proteomics 2015; 135:62-72. [PMID: 26385003 DOI: 10.1016/j.jprot.2015.09.015] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 09/02/2015] [Accepted: 09/09/2015] [Indexed: 01/18/2023]
Abstract
Animal venom is a complex cocktail of bioactive chemicals that traditionally drew interest mostly from biochemists and pharmacologists. However, in recent years the evolutionary and ecological importance of venom is realized as this trait has direct and strong influence on interactions between species. Moreover, venom content can be modulated by environmental factors. Like many other fields of biology, venom research has been revolutionized in recent years by the introduction of systems biology approaches, i.e., genomics, transcriptomics and proteomics. The employment of these methods in venom research is known as 'venomics'. In this review we describe the history and recent advancements of venomics and discuss how they are employed in studying venom in general and in particular in the context of evolutionary ecology. We also discuss the pitfalls and challenges of venomics and what the future may hold for this emerging scientific field.
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Affiliation(s)
- Kartik Sunagar
- Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - David Morgenstern
- Proteomics Resource Center, Langone Medical Center, New York University, New York, USA.
| | - Adam M Reitzel
- Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Yehu Moran
- Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel.
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17
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Krishna R, Xia D, Sanderson S, Shanmugasundram A, Vermont S, Bernal A, Daniel-Naguib G, Ghali F, Brunk BP, Roos DS, Wastling JM, Jones AR. A large-scale proteogenomics study of apicomplexan pathogens-Toxoplasma gondii and Neospora caninum. Proteomics 2015; 15:2618-28. [PMID: 25867681 PMCID: PMC4692086 DOI: 10.1002/pmic.201400553] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 02/09/2015] [Accepted: 04/09/2015] [Indexed: 01/08/2023]
Abstract
Proteomics data can supplement genome annotation efforts, for example being used to confirm gene models or correct gene annotation errors. Here, we present a large-scale proteogenomics study of two important apicomplexan pathogens: Toxoplasma gondii and Neospora caninum. We queried proteomics data against a panel of official and alternate gene models generated directly from RNASeq data, using several newly generated and some previously published MS datasets for this meta-analysis. We identified a total of 201 996 and 39 953 peptide-spectrum matches for T. gondii and N. caninum, respectively, at a 1% peptide FDR threshold. This equated to the identification of 30 494 distinct peptide sequences and 2921 proteins (matches to official gene models) for T. gondii, and 8911 peptides/1273 proteins for N. caninum following stringent protein-level thresholding. We have also identified 289 and 140 loci for T. gondii and N. caninum, respectively, which mapped to RNA-Seq-derived gene models used in our analysis and apparently absent from the official annotation (release 10 from EuPathDB) of these species. We present several examples in our study where the RNA-Seq evidence can help in correction of the current gene model and can help in discovery of potential new genes. The findings of this study have been integrated into the EuPathDB. The data have been deposited to the ProteomeXchange with identifiers PXD000297and PXD000298.
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Affiliation(s)
- Ritesh Krishna
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK.,Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Dong Xia
- Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Sanya Sanderson
- Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Achchuthan Shanmugasundram
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK.,Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Sarah Vermont
- Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Axel Bernal
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Fawaz Ghali
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK
| | - Brian P Brunk
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - David S Roos
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan M Wastling
- Institute of Infection and Global Health, University of Liverpool, Liverpool, Merseyside, UK
| | - Andrew R Jones
- Institute of Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK
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18
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Abstract
Proteolysis is a critical modification leading to alteration of protein function with important outcomes in many biological processes. However, for the majority of proteases, we have an incomplete understanding of both cellular substrates and downstream effects. Here, we describe detailed protocols and applications for using the rationally engineered peptide ligase, subtiligase, to specifically label and capture protein N-termini generated by proteases either induced or added to complex biological samples. This method allows identification of the protein targets as well as their precise cleavage locations. This approach has revealed >8000 proteolytic sites in healthy and apoptotic cells including >1700 caspase cleavages. One can further determine substrate preferences through rate analysis with quantitative mass spectrometry, physiological substrate specificities, and even infer the identity of proteases operating in the cell. In this chapter, we also describe how this experimental method can be generalized to investigate proteolysis in any biological sample.
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19
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L.S. Tang N, Poon T, Poon TCW. Advances in MALDI mass spectrometry in clinical diagnostic applications. Top Curr Chem (Cham) 2013; 336:139-75. [PMID: 23563502 PMCID: PMC7121589 DOI: 10.1007/128_2012_413] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The concept of matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) was first reported in 1985. Since then, MALDI MS technologies have been evolving, and successfully used in genome, proteome, metabolome, and clinical diagnostic research. These technologies are high-throughput and sensitive. Emerging evidence has shown that they are not only useful in qualitative and quantitative analyses of proteins, but also of other types of biomolecules, such as DNA, glycans, and metabolites. Recently, parallel fragmentation monitoring (PFM), which is a method comparable to selected reaction monitoring, has been reported. This highlights the potentials of MALDI-TOF/TOF tandem MS in quantification of metabolites. Here we critically review the applications of the major MALDI MS technologies, including MALDI-TOF MS, MALDI-TOF/TOF MS, SALDI-TOF MS, MALDI-QqQ MS, and SELDI-TOF MS, to the discovery and quantification of disease biomarkers in biological specimens, especially those in plasma/serum specimens. Using SELDI-TOF MS as an example, the presence of systemic bias in biomarker discovery studies employing MALDI-TOF MS and its possible solutions are also discussed in this chapter. The concepts of MALDI, SALDI, SELDI, and PFM are complementary to each other. Theoretically, all these technologies can be combined, leading to the next generation of the MALDI MS technologies. Real applications of MALDI MS technologies in clinical diagnostics should be forthcoming.
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Affiliation(s)
- Nelson L.S. Tang
- grid.10784.3a0000000419370482Dept. of Chemical Pathology and Lab. of Genetics of Disease Suscept., The Chinese University of Hong Kong, Hong Kong, People's Republic of China
| | - Terence Poon
- grid.10784.3a0000000419370482Department of Paediatrics and Proteomics Laboratory, The Chinese University of Hong Kong, Hong Kong, People's Republic of China
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20
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Wang P, Wilson SR. Mass spectrometry-based protein identification by integrating de novo sequencing with database searching. BMC Bioinformatics 2013; 14 Suppl 2:S24. [PMID: 23369017 PMCID: PMC3549845 DOI: 10.1186/1471-2105-14-s2-s24] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Mass spectrometry-based protein identification is a very challenging task. The main identification approaches include de novo sequencing and database searching. Both approaches have shortcomings, so an integrative approach has been developed. The integrative approach firstly infers partial peptide sequences, known as tags, directly from tandem spectra through de novo sequencing, and then puts these sequences into a database search to see if a close peptide match can be found. However the current implementation of this integrative approach has several limitations. Firstly, simplistic de novo sequencing is applied and only very short sequence tags are used. Secondly, most integrative methods apply an algorithm similar to BLAST to search for exact sequence matches and do not accommodate sequence errors well. Thirdly, by applying these methods the integrated de novo sequencing makes a limited contribution to the scoring model which is still largely based on database searching. RESULTS We have developed a new integrative protein identification method which can integrate de novo sequencing more efficiently into database searching. Evaluated on large real datasets, our method outperforms popular identification methods.
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Affiliation(s)
- Penghao Wang
- Prince of Wales Clinical School, University of New South Wales, Australia.
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21
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Li F, Glinskii OV, Glinsky VV. Glycobioinformatics: Current strategies and tools for data mining in MS-based glycoproteomics. Proteomics 2012; 13:341-54. [DOI: 10.1002/pmic.201200149] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2012] [Revised: 10/06/2012] [Accepted: 11/06/2012] [Indexed: 12/18/2022]
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22
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Akhtar MN, Southey BR, Andrén PE, Sweedler JV, Rodriguez-Zas SL. Evaluation of database search programs for accurate detection of neuropeptides in tandem mass spectrometry experiments. J Proteome Res 2012; 11:6044-55. [PMID: 23082934 PMCID: PMC3516866 DOI: 10.1021/pr3007123] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
![]()
Neuropeptide identification in mass spectrometry experiments
using
database search programs developed for proteins is challenging. Unlike
proteins, the detection of the complete sequence using a single spectrum
is required to identify neuropeptides or prohormone peptides. This
study compared the performance of three open-source programs used
to identify proteins, OMSSA, X!Tandem and Crux, to identify prohormone
peptides. From a target database of 7850 prohormone peptides, 23550
query spectra were simulated across different scenarios. Crux was
the only program that correctly matched all peptides regardless of p-value and at p-value < 1 × 10–2, 33%, 64%, and >75%, of the 5, 6, and ≥7
amino
acid-peptides were detected. Crux also had the best performance in
the identification of peptides from chimera spectra and in a variety
of missing ion scenarios. OMSSA, X!Tandem and Crux correctly detected
98.9% (99.9%), 93.9% (97.4%) and 88.7% (98.3%) of the peptides at E- or p-value < 1 × 10–6 (< 1 × 10–2), respectively. OMSSA and
X!Tandem outperformed the other programs in significance level and
computational speed, respectively. A consensus approach is not recommended
because some prohormone peptides were only identified by one program.
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Affiliation(s)
- Malik N Akhtar
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Illinois 61801, United States
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23
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Helmy M, Sugiyama N, Tomita M, Ishihama Y. Mass spectrum sequential subtraction speeds up searching large peptide MS/MS spectra datasets against large nucleotide databases for proteogenomics. Genes Cells 2012; 17:633-44. [PMID: 22686349 DOI: 10.1111/j.1365-2443.2012.01615.x] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Accepted: 04/14/2012] [Indexed: 01/18/2023]
Abstract
We have developed a novel bioinformatics method called mass spectrum sequential subtraction (MSSS) to search large peptide spectra datasets produced by liquid chromatography/mass spectrometry (LC-MS/MS) against protein and large-sized nucleotide sequence databases. The main principle in MSSS is to search the peptide spectra set against the protein database, followed by removal of the spectra corresponding to the identified peptides to create a smaller set of the remaining peptide spectra for searching against the nucleotide sequences database. Therefore, we reduce the number of spectra to be searched to limit the peptide search space. Comparing MSSS and conventional search approach using a dataset of 27 LC-MS/MS runs of rice culture cells indicated that MSSS reduced the search queries to 50% and the search time to 75% on average. In addition, MSSS had no effect on the identification false-positive rate (FPR) or the novel peptide sequences identification ability. We used MSSS to analyze another dataset of 34 LC-MS/MS runs, resulting in identifying additional 74 novel peptides. Proteogenomic analysis with these additional peptides yielded 47 new genomic features in 24 rice genes plus 24 intergenic peptides. These results show that the utility of MSSS in searching large databases with large MS/MS datasets for proteogenomics.
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Affiliation(s)
- Mohamed Helmy
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
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24
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Cardoza JD, Parikh JR, Ficarro SB, Marto JA. Mass spectrometry-based proteomics: qualitative identification to activity-based protein profiling. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2012; 4:141-62. [PMID: 22231900 PMCID: PMC3288153 DOI: 10.1002/wsbm.166] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Mass spectrometry has become the method of choice for proteome characterization, including multicomponent protein complexes (typically tens to hundreds of proteins) and total protein expression (up to tens of thousands of proteins), in biological samples. Qualitative sequence assignment based on MS/MS spectra is relatively well-defined, while statistical metrics for relative quantification have not completely stabilized. Nonetheless, proteomics studies have progressed to the point whereby various gene-, pathway-, or network-oriented computational frameworks may be used to place mass spectrometry data into biological context. Despite this progress, the dynamic range of protein expression remains a significant hurdle, and impedes comprehensive proteome analysis. Methods designed to enrich specific protein classes have emerged as an effective means to characterize enzymes or other catalytically active proteins that are otherwise difficult to detect in typical discovery mode proteomics experiments. Collectively, these approaches will facilitate identification of biomarkers and pathways relevant to diagnosis and treatment of human disease.
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Affiliation(s)
- Job D. Cardoza
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Jignesh R. Parikh
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Bioinformatics Program, Boston University, Boston, MA 02115
| | - Scott B. Ficarro
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Blais Proteomics Center, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
| | - Jarrod A. Marto
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115
- Blais Proteomics Center, Dana-Farber Cancer Institute, Boston, MA 02115
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115
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25
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Lee S, Kwon MS, Lee HJ, Paik YK, Tang H, Lee JK, Park T. Enhanced peptide quantification using spectral count clustering and cluster abundance. BMC Bioinformatics 2011; 12:423. [PMID: 22034872 PMCID: PMC3234305 DOI: 10.1186/1471-2105-12-423] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2011] [Accepted: 10/28/2011] [Indexed: 12/24/2022] Open
Abstract
Background Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries. To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets. Results We applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers. Conclusions We proposed a novel statistical method, Q-FISH, for accurately identifying protein species and simultaneously quantifying the expression levels of identified peptides from mass spectrometry data. Q-FISH analysis on human HCC and liver tissue samples identified many protein biomarkers that are highly relevant to HCC. Q-FISH can be a useful tool both for peptide identification and quantification on mass spectrometry data analysis. It may also prove to be more effective in discovering novel protein biomarkers than SEQUEST and other standard methods.
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Affiliation(s)
- Seungmook Lee
- Department of Statistics, Seoul National University, Korea
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26
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Vensel WH, Dupont FM, Sloane S, Altenbach SB. Effect of cleavage enzyme, search algorithm and decoy database on mass spectrometric identification of wheat gluten proteins. PHYTOCHEMISTRY 2011; 72:1154-1161. [PMID: 21292286 DOI: 10.1016/j.phytochem.2011.01.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2010] [Revised: 11/22/2010] [Accepted: 01/03/2011] [Indexed: 05/30/2023]
Abstract
While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein database content and size on the false discovery rate. We found that as database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy database influenced the number of proteins identified. Using three enzymes, two search algorithms and a specialized database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.
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Affiliation(s)
- William H Vensel
- USDA-ARS, Western Regional Research Center, 800 Buchanan St., Albany, CA 94710, USA.
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27
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Brejnholt SM, Dionisio G, Glitsoe V, Skov LK, Brinch-Pedersen H. The degradation of phytate by microbial and wheat phytases is dependent on the phytate matrix and the phytase origin. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2011; 91:1398-1405. [PMID: 21387323 DOI: 10.1002/jsfa.4324] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2010] [Revised: 11/08/2010] [Accepted: 01/06/2011] [Indexed: 05/30/2023]
Abstract
BACKGROUND Phytases increase utilization of phytate phosphorus in feed. Since wheat is rich in endogenous phytase activity it was examined whether wheat phytases could improve phytate degradation compared to microbial phytases. Moreover, it was investigated whether enzymatic degradation of phytate is influenced by the matrix surrounding it. Phytate degradation was defined as the decrease in the sum of InsP₆ + InsP₅. RESULTS Endogenous wheat phytase effectively degraded wheat Ins₆ + InsP₅ at pH 4 and pH 5, while this was not true for a recombinant wheat phytase or phytase extracted from wheat bran. Only microbial phytases were able to degrade InsP₆ + InsP₅ in the entire pH range from 3 to 5, which is relevant for feed applications. A microbial phytase was efficient towards InsP₆ + InsP₅ in different phytate samples, whereas the ability to degrade InsP₆ + InsP₅ in the different phytate samples ranged from 12% to 70% for the recombinant wheat phytase. CONCLUSION Wheat phytase appeared to have an interesting potential. However, the wheat phytases studied could not improve phytate degradation compared to microbial phytases. The ability to degrade phytate in different phytate samples varied greatly for some phytases, indicating that phytase efficacy may be affected by the phytate matrix.
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28
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Baumgardner LA, Shanmugam AK, Lam H, Eng JK, Martin DB. Fast parallel tandem mass spectral library searching using GPU hardware acceleration. J Proteome Res 2011; 10:2882-8. [PMID: 21545112 DOI: 10.1021/pr200074h] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.
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29
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Shashilov VA, Lednev IK. Advanced statistical and numerical methods for spectroscopic characterization of protein structural evolution. Chem Rev 2011; 110:5692-713. [PMID: 20593900 DOI: 10.1021/cr900152h] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Victor A Shashilov
- Aegis Analytical Corporation, 1380 Forest Park Circle, Suite 200, Lafayette, Colorado 80026, USA
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30
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Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC–MS for biomarker discovery. Talanta 2011; 83:1209-24. [DOI: 10.1016/j.talanta.2010.10.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2010] [Revised: 10/18/2010] [Accepted: 10/21/2010] [Indexed: 01/30/2023]
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31
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Karpievitch YV, Polpitiya AD, Anderson GA, Smith RD, Dabney AR. Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects. Ann Appl Stat 2010; 4:1797-1823. [PMID: 21593992 DOI: 10.1214/10-aoas341] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.
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32
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Lietzén N, Natri L, Nevalainen OS, Salmi J, Nyman TA. Compid: A New Software Tool To Integrate and Compare MS/MS Based Protein Identification Results from Mascot and Paragon. J Proteome Res 2010; 9:6795-800. [DOI: 10.1021/pr100824w] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Niina Lietzén
- Protein Chemistry Research Group, Institute of Biotechnology, University of Helsinki, Finland, and Department of Information Technology and TUCS, University of Turku, Finland
| | - Lari Natri
- Protein Chemistry Research Group, Institute of Biotechnology, University of Helsinki, Finland, and Department of Information Technology and TUCS, University of Turku, Finland
| | - Olli S. Nevalainen
- Protein Chemistry Research Group, Institute of Biotechnology, University of Helsinki, Finland, and Department of Information Technology and TUCS, University of Turku, Finland
| | - Jussi Salmi
- Protein Chemistry Research Group, Institute of Biotechnology, University of Helsinki, Finland, and Department of Information Technology and TUCS, University of Turku, Finland
| | - Tuula A. Nyman
- Protein Chemistry Research Group, Institute of Biotechnology, University of Helsinki, Finland, and Department of Information Technology and TUCS, University of Turku, Finland
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Chi Y, Clurman BE. Mass spectrometry-based identification of protein kinase substrates utilizing engineered kinases and thiophosphate labeling. CURRENT PROTOCOLS IN CHEMICAL BIOLOGY 2010; 2:ch100151. [PMID: 21743840 PMCID: PMC3131159 DOI: 10.1002/9780470559277.ch100151] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Protein kinases constitute a large enzyme family with key roles in cellular signal transduction. One way to elucidate the functions of protein kinases is to systematically identify their downstream targets. We present here a simple and effective method to identify direct protein kinase substrates in native cell lysates. First, we isolate the activity of the kinase of interest by engineering the normal kinase to utilize bulky ATP analogs that cannot be used by normal cellular kinases. This allows specific labeling of substrates with thiophosphate tags by performing kinase reactions in cell lysates that also include bulky ATP-γ-S analogs. After digesting the proteins in the reaction mixture, thiophosphopeptides are isolated using a single-step capture-and-release protocol and identified by mass spectrometry. This technique is easy to use and generally applicable.
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Affiliation(s)
- Yong Chi
- Divisions of Clinical Research and Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109
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Premsler T, Zahedi RP, Lewandrowski U, Sickmann A. Recent advances in yeast organelle and membrane proteomics. Proteomics 2009; 9:4731-43. [DOI: 10.1002/pmic.200900201] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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