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Flender D, Vilenne F, Adams C, Boonen K, Valkenborg D, Baggerman G. Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39152539 DOI: 10.1002/mas.21905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024]
Abstract
Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.
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Affiliation(s)
- Daniel Flender
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- Health Unit, VITO, Mol, Belgium
| | - Frédérique Vilenne
- Health Unit, VITO, Mol, Belgium
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- ImmuneSpec, Niel, Belgium
| | - Dirk Valkenborg
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Geert Baggerman
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
- ImmuneSpec, Niel, Belgium
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2
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Wang X, Yu N, Jiao Z, Li L, Yu H, Wei S. Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS. SCIENCE ADVANCES 2024; 10:eadn1039. [PMID: 38781329 PMCID: PMC11114235 DOI: 10.1126/sciadv.adn1039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.
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Affiliation(s)
| | | | - Zhaoyu Jiao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
| | - Laihui Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, People’s Republic of China
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3
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Na S, Paek E. Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls. Methods Mol Biol 2024; 2836:37-55. [PMID: 38995534 DOI: 10.1007/978-1-0716-4007-4_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.
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Affiliation(s)
- Seungjin Na
- Digital Omics Research Center, Korea Basic Science Institute, Cheongju, South Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul, South Korea.
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea.
- Institute for Artificial Intelligence Research, Hanyang University, Seoul, South Korea.
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4
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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5
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Caira S, Picariello G, Renzone G, Arena S, Troise AD, De Pascale S, Ciaravolo V, Pinto G, Addeo F, Scaloni A. Recent developments in peptidomics for the quali-quantitative analysis of food-derived peptides in human body fluids and tissues. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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Levitsky LI, Bubis JA, Gorshkov MV, Tarasova IA. AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results. J Proteomics 2021; 248:104350. [PMID: 34389500 DOI: 10.1016/j.jprot.2021.104350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/21/2021] [Accepted: 08/04/2021] [Indexed: 11/18/2022]
Abstract
Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.
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Affiliation(s)
- Lev I Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Irina A Tarasova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia.
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8
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Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021; 18:527-556. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text]. EXPERT OPINION Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).
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Affiliation(s)
- Luís Perpetuo
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Université Toulouse III, Toulouse, France
| | - Rita Ferreira
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Francisco Amado
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Adelino Leite-Moreira
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
| | - Artur M S Silva
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro.,LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro.,UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
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9
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Permiakova O, Guibert R, Kraut A, Fortin T, Hesse AM, Burger T. CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis. BMC Bioinformatics 2021; 22:68. [PMID: 33579189 PMCID: PMC7881590 DOI: 10.1186/s12859-021-03969-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 01/14/2021] [Indexed: 11/16/2022] Open
Abstract
Background The clustering of data produced by liquid chromatography coupled to mass spectrometry analyses (LC-MS data) has recently gained interest to extract meaningful chemical or biological patterns. However, recent instrumental pipelines deliver data which size, dimensionality and expected number of clusters are too large to be processed by classical machine learning algorithms, so that most of the state-of-the-art relies on single pass linkage-based algorithms. Results We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way. As a result, it can process LC-MS data in an acceptable time on a multicore machine. To do so, we combine three essential features: a compressive data representation, Nyström approximation and a hierarchical strategy. In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles. Conclusions Our method, referred to as CHICKN, is evaluated on proteomics data produced in our lab, as well as on benchmark data coming from the literature. From a computational viewpoint, it is particularly efficient on raw LC-MS data. From a data analysis viewpoint, it provides clusters which differ from those resulting from state-of-the-art methods, while achieving similar performances. This highlights the complementarity of differently principle algorithms to extract the best from complex LC-MS data.
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Affiliation(s)
- Olga Permiakova
- Univ. Grenoble Alpes, CEA, Inserm, BGE U1038, 38000, Grenoble, France
| | - Romain Guibert
- Univ. Grenoble Alpes, CEA, Inserm, BGE U1038, 38000, Grenoble, France
| | - Alexandra Kraut
- Univ. Grenoble Alpes, CEA, Inserm, BGE U1038, 38000, Grenoble, France
| | - Thomas Fortin
- Univ. Grenoble Alpes, CEA, Inserm, BGE U1038, 38000, Grenoble, France
| | - Anne-Marie Hesse
- Univ. Grenoble Alpes, CEA, Inserm, BGE U1038, 38000, Grenoble, France
| | - Thomas Burger
- Univ. Grenoble Alpes, CNRS, CEA, Inserm, BGE U1038, 38000, Grenoble, France.
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Xing S, Hu Y, Yin Z, Liu M, Tang X, Fang M, Huan T. Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation. Anal Chem 2020; 92:14476-14483. [DOI: 10.1021/acs.analchem.0c02521] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Yan Hu
- Department of Computer Sciences, University of British Columbia, 2366 Main Mall, Vancouver, V6T 1Z1 BC, Canada
| | - Zixuan Yin
- Fortinet, 4190 Still Creek Dr, Burnaby, V5C 6C6 BC, Canada
| | - Min Liu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Xiaoyu Tang
- Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Mingliang Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada
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11
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Na S, Paek E. Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions. Comput Struct Biotechnol J 2020; 18:1391-1402. [PMID: 32637038 PMCID: PMC7322682 DOI: 10.1016/j.csbj.2020.06.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/01/2020] [Accepted: 06/01/2020] [Indexed: 12/28/2022] Open
Abstract
Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.
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Affiliation(s)
- Seungjin Na
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Eunok Paek
- Dept. of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
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12
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Na S, Kim J, Paek E. MODplus: Robust and Unrestrictive Identification of Post-Translational Modifications Using Mass Spectrometry. Anal Chem 2019; 91:11324-11333. [PMID: 31365238 DOI: 10.1021/acs.analchem.9b02445] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Post-translational modifications regulate various cellular processes and are of great biological interest. Unrestrictive searches of mass spectrometry data enable the detection of any type of modification. Here we propose MODplus, which makes practical unrestrictive searches possible by allowing (1) hundreds of modifications, (2) multiple modifications per peptide, (3) the whole proteome database, and (4) any tolerant values in search parameters. The utility of MODplus was demonstrated in large human data sets of HEK293 cells and TMT-labeled phosphorylation enrichment. Notably, MODplus supports identifying different modification types at multiple sites and reports real chemical and biological modifications, as it has been very labor intensive to link unrestrictive search results to real modifications. We also confirmed the presence of Missing Precursor (MP) spectra that were not identifiable using targeted precursor masses. The MP spectra mostly resulted in identifications of wrong modifications and negatively affected the overall performance, often by as much as 10%. MODplus can rapidly recognize MP spectra and correct their identifications, resulting in increased identification rate up to 70% in the HEK293 data set as well as improved reliability.
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Affiliation(s)
- Seungjin Na
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
| | - Jihyung Kim
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
| | - Eunok Paek
- Department of Computer Science , Hanyang University , Seoul 04763 , South Korea
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13
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Maes E, Oeyen E, Boonen K, Schildermans K, Mertens I, Pauwels P, Valkenborg D, Baggerman G. The challenges of peptidomics in complementing proteomics in a clinical context. MASS SPECTROMETRY REVIEWS 2019; 38:253-264. [PMID: 30372792 DOI: 10.1002/mas.21581] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.
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Affiliation(s)
- Evelyne Maes
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
- Food and Bio-Based Products, AgResearch Ltd., Lincoln, New Zealand
| | - Eline Oeyen
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
| | - Karin Schildermans
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
| | - Inge Mertens
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
| | - Patrick Pauwels
- Molecular Pathology Unit, Department of Pathology, Antwerp University Hospital, Edegem, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
- Center for Statistics, Hasselt University, Diepenbeek, Belgium
| | - Geert Baggerman
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
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14
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Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat Commun 2019; 10:1516. [PMID: 30944337 PMCID: PMC6447530 DOI: 10.1038/s41467-019-09550-x] [Citation(s) in RCA: 195] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 03/13/2019] [Indexed: 12/31/2022] Open
Abstract
Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.
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15
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Tsugawa H, Nakabayashi R, Mori T, Yamada Y, Takahashi M, Rai A, Sugiyama R, Yamamoto H, Nakaya T, Yamazaki M, Kooke R, Bac-Molenaar JA, Oztolan-Erol N, Keurentjes JJB, Arita M, Saito K. A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms. Nat Methods 2019; 16:295-298. [PMID: 30923379 DOI: 10.1038/s41592-019-0358-2] [Citation(s) in RCA: 152] [Impact Index Per Article: 30.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 02/19/2019] [Indexed: 12/24/2022]
Abstract
We report a computational approach (implemented in MS-DIAL 3.0; http://prime.psc.riken.jp/) for metabolite structure characterization using fully 13C-labeled and non-labeled plants and LC-MS/MS. Our approach facilitates carbon number determination and metabolite classification for unknown molecules. Applying our method to 31 tissues from 12 plant species, we assigned 1,092 structures and 344 formulae to 3,604 carbon-determined metabolite ions, 69 of which were found to represent structures currently not listed in metabolome databases.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan. .,RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Tetsuya Mori
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Yutaka Yamada
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | | | - Amit Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | | | | | - Taiki Nakaya
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Mami Yamazaki
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Rik Kooke
- Laboratory of Genetics, Wageningen University & Research, Wageningen, the Netherlands
| | | | - Nihal Oztolan-Erol
- Laboratory of Genetics, Wageningen University & Research, Wageningen, the Netherlands
| | - Joost J B Keurentjes
- Laboratory of Genetics, Wageningen University & Research, Wageningen, the Netherlands
| | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan.,National Institute of Genetics, Mishima, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan. .,Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan.
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16
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An Z, Zhai L, Ying W, Qian X, Gong F, Tan M, Fu Y. PTMiner: Localization and Quality Control of Protein Modifications Detected in an Open Search and Its Application to Comprehensive Post-translational Modification Characterization in Human Proteome. Mol Cell Proteomics 2019; 18:391-405. [PMID: 30420486 PMCID: PMC6356076 DOI: 10.1074/mcp.ra118.000812] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 11/02/2018] [Indexed: 12/27/2022] Open
Abstract
The open (mass tolerant) search of tandem mass spectra of peptides shows great potential in the comprehensive detection of post-translational modifications (PTMs) in shotgun proteomics. However, this search strategy has not been widely used by the community, and one bottleneck of it is the lack of appropriate algorithms for automated and reliable post-processing of the coarse and error-prone search results. Here we present PTMiner, a software tool for confident filtering and localization of modifications (mass shifts) detected in an open search. After mass-shift-grouped false discovery rate (FDR) control of peptide-spectrum matches (PSMs), PTMiner uses an empirical Bayesian method to localize modifications through iterative learning of the prior probabilities of each type of modification occurring on different amino acids. The performance of PTMiner was evaluated on three data sets, including simulated data, chemically synthesized peptide library data and modified-peptide spiked-in proteome data. The results showed that PTMiner can effectively control the PSM FDR and accurately localize the modification sites. At 1% real false localization rate (FLR), PTMiner localized 93%, 84 and 83% of the modification sites in the three data sets, respectively, far higher than two open search engines we used and an extended version of the Ascore localization algorithm. We then used PTMiner to analyze a draft map of human proteome containing 25 million spectra from 30 tissues, and confidently identified over 1.7 million modified PSMs at 1% FDR and 1% FLR, which provided a system-wide view of both known and unknown PTMs in the human proteome.
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Affiliation(s)
- Zhiwu An
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Linhui Zhai
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Wantao Ying
- State key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, National Engineering Research Center for Protein Drugs, Beijing 102206, China, Beijing Institute of Lifeomics, Beijing 100850, China
| | - Xiaohong Qian
- State key Laboratory of Proteomics, National Center for Protein Sciences Beijing, Beijing Proteome Research Center, National Engineering Research Center for Protein Drugs, Beijing 102206, China, Beijing Institute of Lifeomics, Beijing 100850, China
| | - Fuzhou Gong
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Minjia Tan
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China;.
| | - Yan Fu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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17
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Borges H, Guibert R, Permiakova O, Burger T. Distinguishing between Spectral Clustering and Cluster Analysis of Mass Spectra. J Proteome Res 2018; 18:571-573. [PMID: 30394750 DOI: 10.1021/acs.jproteome.8b00516] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The term "spectral clustering" is sometimes used to refer to the clustering of mass spectrometry data. However, it also classically refers to a family of popular clustering algorithms. To avoid confusion, a more specific term could advantageously be coined.
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Affiliation(s)
- Hélène Borges
- Univ. Grenoble Alpes, CEA, INSERM, BIG-BGE, 38000 Grenoble , France
| | - Romain Guibert
- Univ. Grenoble Alpes, CEA, INSERM, BIG-BGE, 38000 Grenoble , France.,CNRS, BIG-BGE, F-38000 Grenoble , France
| | - Olga Permiakova
- Univ. Grenoble Alpes, CEA, INSERM, BIG-BGE, 38000 Grenoble , France
| | - Thomas Burger
- Univ. Grenoble Alpes, CEA, INSERM, BIG-BGE, 38000 Grenoble , France.,CNRS, BIG-BGE, F-38000 Grenoble , France
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18
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Dorl S, Winkler S, Mechtler K, Dorfer V. PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search. J Proteome Res 2017; 17:290-295. [PMID: 29057658 DOI: 10.1021/acs.jproteome.7b00563] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Standard proteomics workflows use tandem mass spectrometry followed by sequence database search to analyze complex biological samples. The identification of proteins carrying post-translational modifications, for example, phosphorylation, is typically addressed by allowing variable modifications in the searched sequences. Accounting for these variations exponentially increases the combinatorial space in the database, which leads to increased processing times and more false positive identifications. The here-presented tool PhoStar identifies spectra that originate from phosphorylated peptides before database search using a supervised machine learning approach. The model for the prediction of phosphorylation was trained and validated with an accuracy of 97.6% on a large set of high-confidence spectra collected from publicly available experimental data. Its power was further validated by predicting phosphorylation in the complete NIST human and mouse high collision-dissociation spectral libraries, achieving an accuracy of 98.2 and 97.9%, respectively. We demonstrate the application of PhoStar by using it for spectra filtering before database search. In database search of HeLa samples the peptide search space was reduced by 27-66% while finding at least 97% of total peptide identifications (at 1% FDR) compared with a standard workflow.
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Affiliation(s)
- Sebastian Dorl
- University of Applied Sciences Upper Austria , Bioinformatics Research Group, Softwarepark 11, 4232 Hagenberg, Austria
| | - Stephan Winkler
- University of Applied Sciences Upper Austria , Bioinformatics Research Group, Softwarepark 11, 4232 Hagenberg, Austria
| | - Karl Mechtler
- Research Institute of Molecular Pathology (IMP) , Protein Chemistry, Campus-Vienna-Biocenter 1, 1030 Vienna, Austria.,Institute of Molecular Biotechnology (IMBA), Protein Chemistry , Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria
| | - Viktoria Dorfer
- University of Applied Sciences Upper Austria , Bioinformatics Research Group, Softwarepark 11, 4232 Hagenberg, Austria
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19
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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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20
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David M, Fertin G, Rogniaux H, Tessier D. SpecOMS: A Full Open Modification Search Method Performing All-to-All Spectra Comparisons within Minutes. J Proteome Res 2017; 16:3030-3038. [PMID: 28660767 DOI: 10.1021/acs.jproteome.7b00308] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The analysis of discovery proteomics experiments relies on algorithms that identify peptides from their tandem mass spectra. The almost exhaustive interpretation of these spectra remains an unresolved issue. At present, an important number of missing interpretations is probably due to peptides displaying post-translational modifications and variants that yield spectra that are particularly difficult to interpret. However, the emergence of a new generation of mass spectrometers that provide high fragment ion accuracy has paved the way for more efficient algorithms. We present a new software, SpecOMS, that can handle the computational complexity of pairwise comparisons of spectra in the context of large volumes. SpecOMS can compare a whole set of experimental spectra generated by a discovery proteomics experiment to a whole set of theoretical spectra deduced from a protein database in a few minutes on a standard workstation. SpecOMS can ingeniously exploit those capabilities to improve the peptide identification process, allowing strong competition between all possible peptides for spectrum interpretation. Remarkably, this software resolves the drawbacks (i.e., efficiency problems and decreased sensitivity) that usually accompany open modification searches. We highlight this promising approach using results obtained from the analysis of a public human data set downloaded from the PRIDE (PRoteomics IDEntification) database.
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Affiliation(s)
- Matthieu David
- LS2N UMR CNRS 6004, Université de Nantes , F-44300 Nantes, France.,INRA UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France
| | - Guillaume Fertin
- LS2N UMR CNRS 6004, Université de Nantes , F-44300 Nantes, France
| | - Hélène Rogniaux
- INRA UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France
| | - Dominique Tessier
- INRA UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France
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21
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Acosta-Martin AE, Antinori P, Uppugunduri CRS, Daali Y, Ansari M, Scherl A, Müller M, Lescuyer P. Detection of busulfan adducts on proteins. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2016; 30:2517-2528. [PMID: 27599297 DOI: 10.1002/rcm.7730] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 08/26/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
RATIONALE Busulfan is a bifunctional alkyl sulfonate antineoplastic drug. This alkylating agent was described as forming covalent adducts on proteins. However, only limited data are available regarding the interaction of busulfan with proteins. Mass spectrometry and bioinformatics were used to identify busulfan adducts on human serum albumin and hemoglobin. METHODS Albumin and hemoglobin were incubated with busulfan or control compounds, digested with trypsin and analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS) on a Thermo Fisher LTQ Orbitrap Velos Pro. MS data were used to generate spectral libraries of non-modified peptides and an open modification search was performed to identify potential adduct mass shifts and possible modification sites. Results were confirmed by a second database search including identified mass shifts and by visual inspection of annotated tandem mass spectra of adduct-carrying peptides. RESULTS Five structures of busulfan adducts were detected and a chemical structure could be attributed to four of them. Two were primary adducts corresponding to busulfan monoalkylation and alkylation of two amino acid residues by a single busulfan molecule. Two others corresponded to secondary adducts generated during sample processing. Adducts were mainly detected on Asp, Glu, and His residues. These findings were confirmed by subsequent database searches and experiments with synthetic peptides. CONCLUSIONS The combination of in vitro incubation of proteins with the drug of interest or control compounds, high-resolution mass spectrometry, and open modification search allowed confirmation of the direct interaction of busulfan with proteins and characterization of the resulting adducts. Our results also showed that careful analysis of the data is required to detect experimental artifacts. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Adelina E Acosta-Martin
- Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Paola Antinori
- Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Swiss Centre of Applied Human Toxicology, Geneva, Switzerland
| | - Chakradhara Rao S Uppugunduri
- Onco-Hematology Unit, Department of Pediatrics, Geneva University Hospitals, Geneva, Switzerland
- Cansearch Research Laboratory, Geneva Medical University, Geneva, Switzerland
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland
| | - Marc Ansari
- Onco-Hematology Unit, Department of Pediatrics, Geneva University Hospitals, Geneva, Switzerland
- Cansearch Research Laboratory, Geneva Medical University, Geneva, Switzerland
| | - Alexander Scherl
- Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
- Swiss Centre of Applied Human Toxicology, Geneva, Switzerland
| | - Markus Müller
- SIB-Swiss Institute of Bioinformatics, University of Geneva, Switzerland
| | - Pierre Lescuyer
- Department of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland
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22
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Abstract
In computational proteomics, the identification of peptides with an unlimited number of post-translational modification (PTM) types is a challenging task. The computational cost associated with database search increases exponentially with respect to the number of modified amino acids and linearly with respect to the number of potential PTM types at each amino acid. The problem becomes intractable very quickly if we want to enumerate all possible PTM patterns. To address this issue, one group of methods named restricted tools (including Mascot, Comet, and MS-GF+) only allow a small number of PTM types in database search process. Alternatively, the other group of methods named unrestricted tools (including MS-Alignment, ProteinProspector, and MODa) avoids enumerating PTM patterns with an alignment-based approach to localizing and characterizing modified amino acids. However, because of the large search space and PTM localization issue, the sensitivity of these unrestricted tools is low. This paper proposes a novel method named PIPI to achieve PTM-invariant peptide identification. PIPI belongs to the category of unrestricted tools. It first codes peptide sequences into Boolean vectors and codes experimental spectra into real-valued vectors. For each coded spectrum, it then searches the coded sequence database to find the top scored peptide sequences as candidates. After that, PIPI uses dynamic programming to localize and characterize modified amino acids in each candidate. We used simulation experiments and real data experiments to evaluate the performance in comparison with restricted tools (i.e., Mascot, Comet, and MS-GF+) and unrestricted tools (i.e., Mascot with error tolerant search, MS-Alignment, ProteinProspector, and MODa). Comparison with restricted tools shows that PIPI has a close sensitivity and running speed. Comparison with unrestricted tools shows that PIPI has the highest sensitivity except for Mascot with error tolerant search and ProteinProspector. These two tools simplify the task by only considering up to one modified amino acid in each peptide, which results in a higher sensitivity but has difficulty in dealing with multiple modified amino acids. The simulation experiments also show that PIPI has the lowest false discovery proportion, the highest PTM characterization accuracy, and the shortest running time among the unrestricted tools.
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Affiliation(s)
- Fengchao Yu
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology , Hong Kong, China
| | - Ning Li
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology , Hong Kong, China.,Division of Life Science, The Hong Kong University of Science and Technology , Hong Kong, China
| | - Weichuan Yu
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology , Hong Kong, China.,Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology , Hong Kong, China
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23
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Na S, Payne SH, Bandeira N. Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks. Mol Cell Proteomics 2016; 15:3501-3512. [PMID: 27609420 PMCID: PMC5098046 DOI: 10.1074/mcp.o116.060913] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Indexed: 11/25/2022] Open
Abstract
Peptide and protein identification remains challenging in organisms with poorly annotated or rapidly evolving genomes, as are commonly encountered in environmental or biofuels research. Such limitations render tandem mass spectrometry (MS/MS) database search algorithms ineffective as they lack corresponding sequences required for peptide-spectrum matching. We address this challenge with the spectral networks approach to (1) match spectra of orthologous peptides across multiple related species and then (2) propagate peptide annotations from identified to unidentified spectra. We here present algorithms to assess the statistical significance of spectral alignments (Align-GF), reduce the impurity in spectral networks, and accurately estimate the error rate in propagated identifications. Analyzing three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides from highly divergent sequences from networks with dozens of variant peptides, including thousands of peptides in species lacking a sequenced genome. Our analysis further detected the presence of many novel putative peptides even in genomically characterized species, thus suggesting the possibility of gaps in our understanding of their proteomic and genomic expression. A web-based pipeline for spectral networks analysis is available at http://proteomics.ucsd.edu/software.
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Affiliation(s)
- Seungjin Na
- From the ‡Dept. of Computer Science and Engineering, University of California, San Diego, La Jolla, California, 92093.,§Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, 92093
| | - Samuel H Payne
- ¶Pacific Northwest National Laboratory, Richland, Washington 99354
| | - Nuno Bandeira
- From the ‡Dept. of Computer Science and Engineering, University of California, San Diego, La Jolla, California, 92093; .,§Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, California, 92093.,‖Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, 92093
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24
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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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25
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Vaudel M, Verheggen K, Csordas A, Raeder H, Berven FS, Martens L, Vizcaíno JA, Barsnes H. Exploring the potential of public proteomics data. Proteomics 2016; 16:214-25. [PMID: 26449181 PMCID: PMC4738454 DOI: 10.1002/pmic.201500295] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 08/25/2015] [Accepted: 09/28/2015] [Indexed: 12/22/2022]
Abstract
In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.
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Affiliation(s)
- Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Kenneth Verheggen
- Medical Biotechnology Center, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Attila Csordas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Helge Raeder
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, KG Jebsen Centre for Multiple Sclerosis Research, University of Bergen, Bergen, Norway
| | - Lennart Martens
- Medical Biotechnology Center, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Juan A Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
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26
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Abstract
Mass spectrometry-based proteomics provides a powerful tool for large-scale analysis of protein modifications. Statistical and computational analysis of mass spectrometry data is a key step in protein modification identification. This chapter presents common and advanced data analysis strategies for modification identification, including variable modification search, unrestrictive approaches for modification discovery, false discovery rate estimation and control methods, and tools for modification site localization.
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Affiliation(s)
- Yan Fu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road 55, Beijing, 100190, China.
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27
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Horlacher O, Lisacek F, Müller M. Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries. J Proteome Res 2015; 15:721-31. [PMID: 26653734 DOI: 10.1021/acs.jproteome.5b00877] [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] [Indexed: 11/28/2022]
Abstract
Experimental improvements in post-translational modification (PTM) detection by tandem mass spectrometry (MS/MS) has allowed the identification of vast numbers of PTMs. Open modification searches (OMSs) of MS/MS data, which do not require prior knowledge of the modifications present in the sample, further increased the diversity of detected PTMs. Despite much effort, there is still a lack of functional annotation of PTMs. One possibility to narrow the annotation gap is to mine MS/MS data deposited in public repositories and to correlate the PTM presence with biological meta-information attached to the data. Since the data volume can be quite substantial and contain tens of millions of MS/MS spectra, the data mining tools must be able to cope with big data. Here, we present two tools, Liberator and MzMod, which are built using the MzJava class library and the Apache Spark large scale computing framework. Liberator builds large MS/MS spectrum libraries, and MzMod searches them in an OMS mode. We applied these tools to a recently published set of 25 million spectra from 30 human tissues and present tissue specific PTMs. We also compared the results to the ones obtained with the OMS tool MODa and the search engine X!Tandem.
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Affiliation(s)
- Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
| | - Markus Müller
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva 1211, Switzerland.,Centre Universitaire de Bioinformatique, University of Geneva , Geneva 1211, Switzerland
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28
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Leon DR, Ytterberg AJ, Boontheung P, Kim U, Loo JA, Gunsalus RP, Ogorzalek Loo RR. Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1. Front Microbiol 2015; 6:149. [PMID: 25798134 PMCID: PMC4350412 DOI: 10.3389/fmicb.2015.00149] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2014] [Accepted: 02/09/2015] [Indexed: 12/11/2022] Open
Abstract
Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to "mine" information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154 proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.
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Affiliation(s)
- Deborah R Leon
- Department of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA
| | - A Jimmy Ytterberg
- Department of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA
| | - Pinmanee Boontheung
- Department of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA
| | - Unmi Kim
- Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles Los Angeles, CA, USA
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA ; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA
| | - Robert P Gunsalus
- Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA
| | - Rachel R Ogorzalek Loo
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA
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29
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Dallas DC, Guerrero A, Parker EA, Robinson RC, Gan J, German JB, Barile D, Lebrilla CB. Current peptidomics: applications, purification, identification, quantification, and functional analysis. Proteomics 2015; 15:1026-38. [PMID: 25429922 PMCID: PMC4371869 DOI: 10.1002/pmic.201400310] [Citation(s) in RCA: 156] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Revised: 10/08/2014] [Accepted: 11/24/2014] [Indexed: 12/28/2022]
Abstract
Peptidomics is an emerging field branching from proteomics that targets endogenously produced protein fragments. Endogenous peptides are often functional within the body-and can be both beneficial and detrimental. This review covers the use of peptidomics in understanding digestion, and identifying functional peptides and biomarkers. Various techniques for peptide and glycopeptide extraction, both at analytical and preparative scales, and available options for peptide detection with MS are discussed. Current algorithms for peptide sequence determination, and both analytical and computational techniques for quantification are compared. Techniques for statistical analysis, sequence mapping, enzyme prediction, and peptide function, and structure prediction are explored.
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Affiliation(s)
- David C. Dallas
- Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA, USA
- Foods for Health Institute, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Andres Guerrero
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Evan A. Parker
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Randall C. Robinson
- Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Junai Gan
- Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - J. Bruce German
- Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA, USA
- Foods for Health Institute, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Daniela Barile
- Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA, USA
- Foods for Health Institute, University of California, Davis, One Shields Avenue, Davis, CA, USA
| | - Carlito B. Lebrilla
- Foods for Health Institute, University of California, Davis, One Shields Avenue, Davis, CA, USA
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, CA, USA
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30
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Na S, Paek E. Software eyes for protein post-translational modifications. MASS SPECTROMETRY REVIEWS 2015; 34:133-147. [PMID: 24889695 DOI: 10.1002/mas.21425] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2012] [Revised: 07/18/2013] [Accepted: 11/20/2013] [Indexed: 06/03/2023]
Abstract
Post-translational modifications (PTMs) are critical to almost all aspects of complex processes of the cell. Identification of PTMs is one of the biggest challenges for proteomics, and there have been many computational studies for the analysis of PTMs from tandem mass spectrometry (MS/MS). Most early PTM identification studies have been performed by matching MS/MS data to protein databases, using database search tools, but they are prohibitively slow when a large number of PTMs is given as a search parameter. In this article, we present recent developments to search for more types of PTMs and to speed up the search, and discuss many computational issues and solutions in terms of identifying multiply modified peptides or searching for all possible modifications at once in unrestrictive mode. Apart from the most common type of PTMs involving covalent addition of functional groups to proteins, PTMs such as disulfide linkage require dedicated software for the analysis because they may involve cross-linking between two different parts of proteins. Finally, methods for identification of protein disulfide bonds are presented.
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Affiliation(s)
- Seungjin Na
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, 92093; Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, CA, 92093
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31
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Wilhelm T, Jones AME. Identification of related peptides through the analysis of fragment ion mass shifts. J Proteome Res 2014; 13:4002-11. [PMID: 25058668 DOI: 10.1021/pr500347e] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Mass spectrometry (MS) has become the method of choice to identify and quantify proteins, typically by fragmenting peptides and inferring protein identification by reference to sequence databases. Well-established programs have largely solved the problem of identifying peptides in complex mixtures. However, to prevent the search space from becoming prohibitively large, most search engines need a list of expected modifications. Therefore, unexpected modifications limit both the identification of proteins and peptide-based quantification. We developed mass spectrometry-peak shift analysis (MS-PSA) to rapidly identify related spectra in large data sets without reference to databases or specified modifications. Peptide identifications from established tools, such as MASCOT or SEQUEST, may be propagated onto MS-PSA results. Modification of a peptide alters the mass of the precursor ion and some of the fragmentation ions. MS-PSA identifies characteristic fragmentation masses from MS/MS spectra. Related spectra are identified by pattern matching of unchanged and mass-shifted fragment ions. We illustrate the use of MS-PSA with simple and complex mixtures with both high and low mass accuracy data sets. MS-PSA is not limited to the analysis of peptides but can be used for the identification of related groups of spectra in any set of fragmentation patterns.
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Affiliation(s)
- Thomas Wilhelm
- Institute of Food Research , Norwich Research Park, Norwich NR4 7UA, United Kingdom
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32
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Ma CWM, Lam H. Hunting for unexpected post-translational modifications by spectral library searching with tier-wise scoring. J Proteome Res 2014; 13:2262-71. [PMID: 24661115 DOI: 10.1021/pr401006g] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Discovering novel post-translational modifications (PTMs) to proteins and detecting specific modification sites on proteins is one of the last frontiers of proteomics. At present, hunting for post-translational modifications remains challenging in widely practiced shotgun proteomics workflows due to the typically low abundance of modified peptides and the greatly inflated search space as more potential mass shifts are considered by the search engines. Moreover, most popular search methods require that the user specifies the modification(s) for which to search; therefore, unexpected and novel PTMs will not be detected. Here a new algorithm is proposed to apply spectral library searching to the problem of open modification searches, namely, hunting for PTMs without prior knowledge of what PTMs are in the sample. The proposed tier-wise scoring method intelligently looks for unexpected PTMs by allowing mass-shifted peak matches but only when the number of matches found is deemed statistically significant. This allows the search engine to search for unexpected modifications while maintaining its ability to identify unmodified peptides effectively at the same time. The utility of the method is demonstrated using three different data sets, in which the numbers of spectrum identifications to both unmodified and modified peptides were substantially increased relative to a regular spectral library search as well as to another open modification spectral search method, pMatch.
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Affiliation(s)
- Chun Wai Manson Ma
- Division of Biomedical Engineering and ‡Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, China
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33
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Lichti CF, Wildburger NC, Emmett MR, Mostovenko E, Shavkunov AS, Strain SK, Nilsson CL. Post-translational Modifications in the Human Proteome. TRANSLATIONAL BIOINFORMATICS 2014. [DOI: 10.1007/978-94-017-9202-8_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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34
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Shao W, Zhu K, Lam H. Refining similarity scoring to enable decoy-free validation in spectral library searching. Proteomics 2013; 13:3273-83. [DOI: 10.1002/pmic.201300232] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 08/06/2013] [Accepted: 09/10/2013] [Indexed: 12/30/2022]
Affiliation(s)
- Wenguang Shao
- Division of Biomedical Engineering; The Hong Kong University of Science and Technology; Clear Water Bay Hong Kong China
| | - Kan Zhu
- Department of Chemical and Biomolecular Engineering; The Hong Kong University of Science and Technology; Clear Water Bay Hong Kong China
| | - Henry Lam
- Division of Biomedical Engineering; The Hong Kong University of Science and Technology; Clear Water Bay Hong Kong China
- Department of Chemical and Biomolecular Engineering; The Hong Kong University of Science and Technology; Clear Water Bay Hong Kong China
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35
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Shao W, Lam H. Denoising Peptide Tandem Mass Spectra for Spectral Libraries: A Bayesian Approach. J Proteome Res 2013; 12:3223-32. [DOI: 10.1021/pr400080b] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Wenguang Shao
- Division
of Biomedical Engineering, and ‡Department of Chemical and Biomolecular Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Henry Lam
- Division
of Biomedical Engineering, and ‡Department of Chemical and Biomolecular Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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36
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Vaudel M, Sickmann A, Martens L. Current methods for global proteome identification. Expert Rev Proteomics 2013. [PMID: 23194269 DOI: 10.1586/epr.12.51] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In a time frame of a few decades, protein identification went from laborious single protein identification to automated identification of entire proteomes. This shift was enabled by the emergence of peptide-centric, gel-free analyses, in particular the so-called shotgun approaches, which not only rely on extensive experiments, but also on cutting-edge data processing methods. The present review therefore provides an overview of a shotgun proteomics identification workflow, listing the state-of-the-art methods involved and software that implement these. The authors focus on freely available tools where possible. Finally, data analysis in the context of emerging across-omics studies will also be discussed briefly, where proteomics goes beyond merely delivering a list of protein accession numbers.
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Affiliation(s)
- Marc Vaudel
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany
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37
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Menschaert G, Hayakawa E, Schoofs L, Van Criekinge W, Baggerman G. Spectral Clustering in Peptidomics Studies Allows Homology Searching and Modification Profiling: HomClus, a Versatile Tool. J Proteome Res 2012; 11:2774-85. [DOI: 10.1021/pr201114m] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gerben Menschaert
- Faculty of Bioscience Engineering,
Laboratory for Bioinformatics and Computational Genomics, Ghent University, Ghent, Belgium
- Prometa, Interfaculty Center for Proteomics
and Metabolomics, K.U. Leuven, Leuven,
Belgium
| | - Eisuke Hayakawa
- Prometa, Interfaculty Center for Proteomics
and Metabolomics, K.U. Leuven, Leuven,
Belgium
- Research Group of
Functional Genomics and Proteomics, K.U. Leuven, 3000 Leuven, Belgium
| | - Liliane Schoofs
- Research Group of
Functional Genomics and Proteomics, K.U. Leuven, 3000 Leuven, Belgium
| | - Wim Van Criekinge
- Faculty of Bioscience Engineering,
Laboratory for Bioinformatics and Computational Genomics, Ghent University, Ghent, Belgium
| | - Geert Baggerman
- VITO Nv, 2400 Mol, Belgium
- CFP, Center for Proteomics, 2020 Antwerpen, Belgium
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38
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A semi-empirical approach for predicting unobserved peptide MS/MS spectra from spectral libraries. Proteomics 2011; 11:4702-11. [DOI: 10.1002/pmic.201100316] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2011] [Revised: 08/30/2011] [Accepted: 09/30/2011] [Indexed: 01/07/2023]
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39
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Minerva L, Boonen K, Menschaert G, Landuyt B, Baggerman G, Arckens L. Linking Mass Spectrometric Imaging and Traditional Peptidomics: A Validation in the Obese Mouse Model. Anal Chem 2011; 83:7682-91. [DOI: 10.1021/ac200888j] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
| | | | - G. Menschaert
- BioBix, Laboratory of Bioinformatics and Computational Genomics, Ghent University, 9000 Ghent, Belgium
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40
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Ahrné E, Ohta Y, Nikitin F, Scherl A, Lisacek F, Müller M. An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates. Proteomics 2011; 11:4085-95. [DOI: 10.1002/pmic.201000665] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2010] [Revised: 07/13/2011] [Accepted: 07/29/2011] [Indexed: 11/06/2022]
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41
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Lam H. Building and searching tandem mass spectral libraries for peptide identification. Mol Cell Proteomics 2011; 10:R111.008565. [PMID: 21900153 DOI: 10.1074/mcp.r111.008565] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. Conceptually, the premise of this approach is that the tandem MS fragmentation pattern of a peptide under some fixed conditions is a reproducible fingerprint of that peptide, such that unknown spectra acquired under the same conditions can be identified by spectral matching. In actual practice, a spectral library is first meticulously compiled from a large collection of previously observed and identified tandem MS spectra, usually obtained from shotgun proteomics experiments of complex mixtures. Then, a query spectrum is then identified by spectral matching using recently developed spectral search engines. This review discusses the basic principles of the two pillars of this approach: spectral library construction, and spectral library searching. An overview of the software tools available for these two tasks, as well as a high-level description of the underlying algorithms, will be given. Finally, several new methods that utilize spectral libraries for peptide identification in ways other than straightforward spectral matching will also be described.
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Affiliation(s)
- Henry Lam
- Department of Chemical and Biomolecular Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
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42
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Clustering of MS spectra for improved protein identification rate and screening for protein variants and modifications by MALDI-MS/MS. J Proteomics 2011; 74:1190-200. [DOI: 10.1016/j.jprot.2011.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2010] [Revised: 03/30/2011] [Accepted: 04/08/2011] [Indexed: 12/11/2022]
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43
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Kil YJ, Becker C, Sandoval W, Goldberg D, Bern M. Preview: a program for surveying shotgun proteomics tandem mass spectrometry data. Anal Chem 2011; 83:5259-67. [PMID: 21619057 DOI: 10.1021/ac200609a] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Database search programs for peptide identification by tandem mass spectrometry ask their users to set various parameters, including precursor and fragment mass tolerances, digestion specificity, and allowed types of modifications. Even proteomics experts with detailed knowledge of their samples may find it difficult to make these choices without significant investigation, and poor choices can lead to missed identifications and misleading results. Here we describe a program called Preview that analyzes a set of mass spectra for mass errors, digestion specificity, and known and unknown modifications, thereby facilitating parameter selection. Moreover, Preview optionally recalibrates mass over charge measurements, leading to further improvement in identification results. In a study of Bruton's tyrosine kinase, we find that the use of Preview improved the number of confidently identified mass spectra and phosphorylation sites by about 50%.
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Affiliation(s)
- Yong J Kil
- Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, California 94304, USA
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44
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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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45
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Ahrné E, Nikitin F, Lisacek F, Müller M. QuickMod: A tool for open modification spectrum library searches. J Proteome Res 2011; 10:2913-21. [PMID: 21500769 DOI: 10.1021/pr200152g] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
MS2 library spectra are rich in reproducible information about peptide fragmentation patterns compared to theoretical spectra modeled by a sequence search tool. So far, spectrum library searches are mostly applied to detect peptides as they are present in the library. However, they also allow finding modified variants of the library peptides if the search is done with a large precursor mass window and an adapted Spectrum-Spectrum Match (SSM) scoring algorithm. We perform a thorough evaluation on the use of library spectra as opposed to theoretical peptide spectra for the identification of PTMs, analyzing spectra of a well-annotated modification-rich test data set compiled from public data repositories. These initial studies motivate the development of our modification tolerant spectrum library search tool QuickMod, designed to identify modified variants of the peptides listed in the spectrum library without any prior input from the user estimating the modifications present in the sample. We built the search algorithm of QuickMod after carefully testing different SSM similarity scores. The final spectrum scoring scheme uses a support vector machine (SVM) on a selection of scoring features to classify correct and incorrect SSM. After identification of a list of modified peptides at a given False Discovery Rate (FDR), the modifications need to be positioned on the peptide sequence. We present a rapid modification site assignment algorithm and evaluate its positioning accuracy. Finally, we demonstrate that QuickMod performs favorably in terms of speed and identification rate when compared to other software solutions for PTM analysis.
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Affiliation(s)
- Erik Ahrné
- Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Switzerland.
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46
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Fu Y, Xiu LY, Jia W, Ye D, Sun RX, Qian XH, He SM. DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data. Mol Cell Proteomics 2011; 10:M110.000455. [PMID: 21321130 PMCID: PMC3098578 DOI: 10.1074/mcp.m110.000455] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2010] [Revised: 11/23/2010] [Indexed: 12/22/2022] Open
Abstract
Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.
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Affiliation(s)
- Yan Fu
- Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.
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47
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Zhang X, Li Y, Shao W, Lam H. Understanding the improved sensitivity of spectral library searching over sequence database searching in proteomics data analysis. Proteomics 2011; 11:1075-85. [PMID: 21298786 DOI: 10.1002/pmic.201000492] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2010] [Revised: 11/05/2010] [Accepted: 11/29/2010] [Indexed: 01/13/2023]
Abstract
Spectral library searching has been recently proposed as an alternative to sequence database searching for peptide identification from MS/MS. We performed a systematic comparison between spectral library searching and sequence database searching using a wide variety of data to better demonstrate, and understand, the superior sensitivity of the former observed in preliminary studies. By decoupling the effect of search space, we demonstrated that the success of spectral library searching is primarily attributable to the use of real library spectra for matching, without which the sensitivity advantage largely disappears. We further determined the extent to which the use of real peak intensities and non-canonical fragments, both under-utilized information in sequence database searching, contributes to the sensitivity advantage. Lastly, we showed that spectral library searching is disproportionately more successful in identifying low-quality spectra, and complex spectra of higher- charged precursors, both important frontiers in peptide sequencing. Our results answered important outstanding questions about this promising yet unproven method using well-controlled computational experiments and sound statistical approaches.
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Affiliation(s)
- Xin Zhang
- Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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48
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Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics. Methods 2011; 54:424-31. [PMID: 21277371 DOI: 10.1016/j.ymeth.2011.01.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2010] [Revised: 01/19/2011] [Accepted: 01/20/2011] [Indexed: 01/03/2023] Open
Abstract
Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. In spectral library searching, a spectral library is first meticulously compiled from a large collection of previously observed peptide MS/MS spectra that are conclusively assigned to their corresponding amino acid sequence. An unknown spectrum is then identified by comparing it to all the candidates in the spectral library for the most similar match. This review discusses the basic principles of spectral library building and searching, describes its advantages and limitations, and provides a primer for researchers interested in adopting this new approach in their data analysis. It will also discuss the future outlook on the evolution and utility of spectral libraries in the field of proteomics.
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49
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Dasari S, Chambers MC, Codreanu SG, Liebler DC, Collins BC, Pennington SR, Gallagher WM, Tabb DL. Sequence tagging reveals unexpected modifications in toxicoproteomics. Chem Res Toxicol 2011; 24:204-16. [PMID: 21214251 DOI: 10.1021/tx100275t] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Toxicoproteomic samples are rich in posttranslational modifications (PTMs) of proteins. Identifying these modifications via standard database searching can incur significant performance penalties. Here, we describe the latest developments in TagRecon, an algorithm that leverages inferred sequence tags to identify modified peptides in toxicoproteomic data sets. TagRecon identifies known modifications more effectively than the MyriMatch database search engine. TagRecon outperformed state of the art software in recognizing unanticipated modifications from LTQ, Orbitrap, and QTOF data sets. We developed user-friendly software for detecting persistent mass shifts from samples. We follow a three-step strategy for detecting unanticipated PTMs in samples. First, we identify the proteins present in the sample with a standard database search. Next, identified proteins are interrogated for unexpected PTMs with a sequence tag-based search. Finally, additional evidence is gathered for the detected mass shifts with a refinement search. Application of this technology on toxicoproteomic data sets revealed unintended cross-reactions between proteins and sample processing reagents. Twenty-five proteins in rat liver showed signs of oxidative stress when exposed to potentially toxic drugs. These results demonstrate the value of mining toxicoproteomic data sets for modifications.
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Affiliation(s)
- Surendra Dasari
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232-0006, United States
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Abstract
Spectral library searching in the field of proteomics has been gaining visibility and use in the last few years, primarily due to the expansion of public proteomics data repositories and the large spectral libraries that can be generated from them. Spectral library searching has several advantages over conventional sequence searching: it is generally much faster, and has higher specificity and sensitivity. The speed increase is primarily, due to having a smaller, fully indexable search space of real spectra that are known to be observable. The increase in specificity and sensitivity is primarily due to the ability of a search engine to utilize the known intensities of the fragment ions, rather than just comparing with theoretical spectra as is done with sequence searching. The main disadvantage of spectral library searching is that one can only identify peptide ions that have been seen before and are stored in the spectral library. In this chapter, an overview of spectral library searching and the libraries currently available are presented.
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