1
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Aparicio B, Theunissen P, Hervas-Stubbs S, Fortes P, Sarobe P. Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies. Hum Vaccin Immunother 2024; 20:2303799. [PMID: 38346926 PMCID: PMC10863374 DOI: 10.1080/21645515.2024.2303799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/06/2024] [Indexed: 02/15/2024] Open
Abstract
Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.
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Affiliation(s)
- Belen Aparicio
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
| | - Patrick Theunissen
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
- DNA and RNA Medicine Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Sandra Hervas-Stubbs
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
| | - Puri Fortes
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
- DNA and RNA Medicine Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Spanish Network for Advanced Therapies (TERAV ISCIII), Spain
| | - Pablo Sarobe
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
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2
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Wei F, Kouro T, Nakamura Y, Ueda H, Iiizumi S, Hasegawa K, Asahina Y, Kishida T, Morinaga S, Himuro H, Horaguchi S, Tsuji K, Mano Y, Nakamura N, Kawamura T, Sasada T. Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation. Comput Struct Biotechnol J 2024; 23:859-869. [PMID: 38356658 PMCID: PMC10864759 DOI: 10.1016/j.csbj.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.
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Affiliation(s)
- Feifei Wei
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Taku Kouro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yuko Nakamura
- Isotope Science Center, The University of Tokyo, Tokyo, Japan
| | - Hiroki Ueda
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Susumu Iiizumi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Kyoko Hasegawa
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Yuki Asahina
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Takeshi Kishida
- Department of Urology, Kanagawa Cancer Center, Yokohama, Japan
| | - Soichiro Morinaga
- Department of Hepato-Biliary and Pancreatic Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Hidetomo Himuro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Shun Horaguchi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
- Department of Pediatric Surgery, Nihon University School of Medicine, Tokyo, Japan
| | - Kayoko Tsuji
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yasunobu Mano
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Norihiro Nakamura
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | | | - Tetsuro Sasada
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
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3
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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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4
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Beltrao P, Van Den Bossche T, Gabriels R, Holstein T, Kockmann T, Nameni A, Panse C, Schlapbach R, Lautenbacher L, Mattanovich M, Nesvizhskii A, Van Puyvelde B, Scheid J, Schwämmle V, Strauss M, Susmelj AK, The M, Webel H, Wilhelm M, Winkelhardt D, Wolski WE, Xi M. Proceedings of the EuBIC-MS developers meeting 2023. J Proteomics 2024; 305:105246. [PMID: 38964537 DOI: 10.1016/j.jprot.2024.105246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/19/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Abstract
The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.
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Affiliation(s)
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Tanja Holstein
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Tobias Kockmann
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Alireza Nameni
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Christian Panse
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland.
| | - Ralph Schlapbach
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, D - 85354 Freising, Germany
| | - Matthias Mattanovich
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark
| | - Alexey Nesvizhskii
- Departments of Pathology and Computational Medicine and Bioinfoirmatics, University of Michigan, Ann Arbor, MI 48105, USA
| | - Bart Van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, BE-9000 Ghent, Belgium
| | - Jonas Scheid
- Department of Peptide-based Immunotherapy, Institute of Immunology, University and University hospital Tübingen, Auf der Morgenstelle 15, D-72076 Tübingen, Germany; Quantitative Biology Center (QBiC), University of Tübingen, Auf der Morgenstelle 10, D-72076 Tübingen, Germany
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Maximilian Strauss
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Matthew The
- TUM School of Life Sciences Technische Universität München, D - 85354 Freising, Germany
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, D - 85354 Freising, Germany
| | | | - Witold E Wolski
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Muyao Xi
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark
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5
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Buur LM, Declercq A, Strobl M, Bouwmeester R, Degroeve S, Martens L, Dorfer V, Gabriels R. MS 2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0. J Proteome Res 2024; 23:3200-3207. [PMID: 38491990 DOI: 10.1021/acs.jproteome.3c00785] [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: 03/18/2024]
Abstract
Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.
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Affiliation(s)
- Louise M Buur
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Marina Strobl
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
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6
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Dens C, Adams C, Laukens K, Bittremieux W. Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39074335 DOI: 10.1021/jasms.4c00180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.
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Affiliation(s)
- Ceder Dens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
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7
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Taurozzi AJ, Rüther PL, Patramanis I, Koenig C, Sinclair Paterson R, Madupe PP, Harking FS, Welker F, Mackie M, Ramos-Madrigal J, Olsen JV, Cappellini E. Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel. Nat Protoc 2024; 19:2085-2116. [PMID: 38671208 DOI: 10.1038/s41596-024-00975-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 01/12/2024] [Indexed: 04/28/2024]
Abstract
In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.
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Affiliation(s)
| | - Patrick L Rüther
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Claire Koenig
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Palesa P Madupe
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Florian Simon Harking
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Frido Welker
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Meaghan Mackie
- Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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8
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Petrovskiy DV, Nikolsky KS, Kulikova LI, Rudnev VR, Butkova TV, Malsagova KA, Kopylov AT, Kaysheva AL. PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models. Sci Rep 2024; 14:15000. [PMID: 38951578 PMCID: PMC11217302 DOI: 10.1038/s41598-024-65861-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 06/25/2024] [Indexed: 07/03/2024] Open
Abstract
The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.
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9
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Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 PMCID: PMC11269915 DOI: 10.1016/j.mcpro.2024.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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10
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Webel H, Niu L, Nielsen AB, Locard-Paulet M, Mann M, Jensen LJ, Rasmussen S. Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning. Nat Commun 2024; 15:5405. [PMID: 38926340 PMCID: PMC11208500 DOI: 10.1038/s41467-024-48711-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.
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Affiliation(s)
- Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Annelaura Bach Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Marie Locard-Paulet
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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11
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Hu Z, Guo X, Li Z, Meng Z, Huang S. The neoantigens derived from transposable elements - A hidden treasure for cancer immunotherapy. Biochim Biophys Acta Rev Cancer 2024; 1879:189126. [PMID: 38849060 DOI: 10.1016/j.bbcan.2024.189126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 05/26/2024] [Accepted: 06/02/2024] [Indexed: 06/09/2024]
Abstract
Neoantigen-based therapy is a promising approach that selectively activates the immune system of the host to recognize and eradicate cancer cells. Preliminary clinical trials have validated the feasibility, safety, and immunogenicity of personalized neoantigen-directed vaccines, enhancing their effectiveness and broad applicability in immunotherapy. While many ongoing oncological trials concentrate on neoantigens derived from mutations, these targets do not consistently provoke an immune response in all patients harboring the mutations. Additionally, tumors like ovarian cancer, which have a low tumor mutational burden (TMB), may be less amenable to mutation-based neoantigen therapies. Recent advancements in next-generation sequencing and bioinformatics have uncovered a rich source of neoantigens from non-canonical RNAs associated with transposable elements (TEs). Considering the substantial presence of TEs in the human genome and the proven immunogenicity of TE-derived neoantigens in various tumor types, this review investigates the latest findings on TE-derived neoantigens, examining their clinical implications, challenges, and unique advantages in enhancing tumor immunotherapy.
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Affiliation(s)
- Zhixiang Hu
- Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xinyi Guo
- Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ziteng Li
- Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiqiang Meng
- Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Shenglin Huang
- Department of Integrative Oncology, Fudan University Shanghai Cancer Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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12
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Adams C, Gabriel W, Laukens K, Picciani M, Wilhelm M, Bittremieux W, Boonen K. Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nat Commun 2024; 15:3956. [PMID: 38730277 PMCID: PMC11087512 DOI: 10.1038/s41467-024-48322-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.
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Affiliation(s)
- Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, Antwerp, Belgium.
| | - Kurt Boonen
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium.
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13
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Manoutcharian K, Gevorkian G. Are we getting closer to a successful neoantigen cancer vaccine? Mol Aspects Med 2024; 96:101254. [PMID: 38354548 DOI: 10.1016/j.mam.2024.101254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.
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Affiliation(s)
- Karen Manoutcharian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
| | - Goar Gevorkian
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico (UNAM), CDMX, Apartado Postal 70228, Cuidad Universitaria, Mexico DF, CP, 04510, Mexico.
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14
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Adams C, Laukens K, Bittremieux W, Boonen K. Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome. Proteomics 2024; 24:e2300336. [PMID: 38009585 DOI: 10.1002/pmic.202300336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
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Affiliation(s)
- Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- ImmuneSpec BV, Niel, Belgium
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15
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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16
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Gomez-Zepeda D, Arnold-Schild D, Beyrle J, Declercq A, Gabriels R, Kumm E, Preikschat A, Łącki MK, Hirschler A, Rijal JB, Carapito C, Martens L, Distler U, Schild H, Tenzer S. Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS 2Rescore with MS 2PIP timsTOF fragmentation prediction model. Nat Commun 2024; 15:2288. [PMID: 38480730 PMCID: PMC10937930 DOI: 10.1038/s41467-024-46380-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 02/26/2024] [Indexed: 03/17/2024] Open
Abstract
Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.
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Affiliation(s)
- David Gomez-Zepeda
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany.
- Helmholtz Institute for Translational Oncology Mainz (HI-TRON Mainz) - A Helmholtz Institute of the DKFZ, Mainz, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Division 191, Heidelberg, Germany.
| | - Danielle Arnold-Schild
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Julian Beyrle
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
- Helmholtz Institute for Translational Oncology Mainz (HI-TRON Mainz) - A Helmholtz Institute of the DKFZ, Mainz, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division 191, Heidelberg, Germany
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Elena Kumm
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Annica Preikschat
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Mateusz Krzysztof Łącki
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Aurélie Hirschler
- BioOrganic Mass Spectrometry Laboratory (LSMBO), IPHC UMR 7178, University of Strasbourg, CNRS, ProFI - FR2048, Strasbourg, France
| | - Jeewan Babu Rijal
- BioOrganic Mass Spectrometry Laboratory (LSMBO), IPHC UMR 7178, University of Strasbourg, CNRS, ProFI - FR2048, Strasbourg, France
| | - Christine Carapito
- BioOrganic Mass Spectrometry Laboratory (LSMBO), IPHC UMR 7178, University of Strasbourg, CNRS, ProFI - FR2048, Strasbourg, France
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Ute Distler
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Hansjörg Schild
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Stefan Tenzer
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany.
- Helmholtz Institute for Translational Oncology Mainz (HI-TRON Mainz) - A Helmholtz Institute of the DKFZ, Mainz, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Division 191, Heidelberg, Germany.
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University, Mainz, Germany.
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17
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Lapin J, Yan X, Dong Q. UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow. Anal Chem 2024. [PMID: 38329031 DOI: 10.1021/acs.analchem.3c02321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).
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Affiliation(s)
- Joel Lapin
- Department of Physics, Georgetown University, Washington, D.C. 20057, United States
- Associate, Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Xinjian Yan
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Qian Dong
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
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18
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Soh WT, Roetschke HP, Cormican JA, Teo BF, Chiam NC, Raabe M, Pflanz R, Henneberg F, Becker S, Chari A, Liu H, Urlaub H, Liepe J, Mishto M. Protein degradation by human 20S proteasomes elucidates the interplay between peptide hydrolysis and splicing. Nat Commun 2024; 15:1147. [PMID: 38326304 PMCID: PMC10850103 DOI: 10.1038/s41467-024-45339-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
If and how proteasomes catalyze not only peptide hydrolysis but also peptide splicing is an open question that has divided the scientific community. The debate has so far been based on immunopeptidomics, in vitro digestions of synthetic polypeptides as well as ex vivo and in vivo experiments, which could only indirectly describe proteasome-catalyzed peptide splicing of full-length proteins. Here we develop a workflow-and cognate software - to analyze proteasome-generated non-spliced and spliced peptides produced from entire proteins and apply it to in vitro digestions of 15 proteins, including well-known intrinsically disordered proteins such as human tau and α-Synuclein. The results confirm that 20S proteasomes produce a sizeable variety of cis-spliced peptides, whereas trans-spliced peptides are a minority. Both peptide hydrolysis and splicing produce peptides with well-defined characteristics, which hint toward an intricate regulation of both catalytic activities. At protein level, both non-spliced and spliced peptides are not randomly localized within protein sequences, but rather concentrated in hotspots of peptide products, in part driven by protein sequence motifs and proteasomal preferences. At sequence level, the different peptide sequence preference of peptide hydrolysis and peptide splicing suggests a competition between the two catalytic activities of 20S proteasomes during protein degradation.
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Affiliation(s)
- Wai Tuck Soh
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Hanna P Roetschke
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK
| | - John A Cormican
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Bei Fang Teo
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK
- Immunology Programme, Life Sciences Institute; Immunology Translational Research Program and Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Nyet Cheng Chiam
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Monika Raabe
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Ralf Pflanz
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Fabian Henneberg
- Department of Structural Dynamics, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Stefan Becker
- Department of NMR-based Structural Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Ashwin Chari
- Research Group of Structural Biochemistry and Mechanisms, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Haiyan Liu
- Immunology Programme, Life Sciences Institute; Immunology Translational Research Program and Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Henning Urlaub
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
- Institute of Clinical Chemistry, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Juliane Liepe
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany.
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK.
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK.
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19
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Ye J, He X, Wang S, Dong MQ, Wu F, Lu S, Feng F. Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification. J Proteome Res 2024; 23:550-559. [PMID: 38153036 DOI: 10.1021/acs.jproteome.3c00229] [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: 12/29/2023]
Abstract
In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, enabling the calculation of similarity scores between the predicted and experimental spectra for peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard data sets and directly employs the model on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To tackle this problem, we developed a test-time training paradigm that adapts the pretrained model to generate experimental data-specific models, namely, PepT3. PepT3 yields a 10-40% increase in peptide identification depending on the variability in training and experimental data. Intriguingly, when applied to a patient-derived immunopeptidomic sample, PepT3 increases the identification of tumor-specific immunopeptide candidates by 60%. Two-thirds of the newly identified candidates are predicted to bind to the patient's human leukocyte antigen isoforms. To facilitate access of the model and all the results, we have archived all the intermediate files in Zenodo.org with identifier 8231084.
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Affiliation(s)
- Jianbai Ye
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiangnan He
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shujuan Wang
- National Institute of Biological Sciences, Beijing 102206, China
| | - Meng-Qiu Dong
- National Institute of Biological Sciences, Beijing 102206, China
| | - Feng Wu
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shan Lu
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California 92093, United States
| | - Fuli Feng
- MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, Anhui 230026, China
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20
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Pounraj S, Chen S, Ma L, Mazzieri R, Dolcetti R, Rehm BHA. Targeting Tumor Heterogeneity with Neoantigen-Based Cancer Vaccines. Cancer Res 2024; 84:353-363. [PMID: 38055891 DOI: 10.1158/0008-5472.can-23-2042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/24/2023] [Accepted: 12/04/2023] [Indexed: 12/08/2023]
Abstract
Neoantigen-based cancer vaccines have emerged as a promising immunotherapeutic approach to treat cancer. Nevertheless, the high degree of heterogeneity in tumors poses a significant hurdle for developing a vaccine that targets the therapeutically relevant neoantigens capable of effectively stimulating an immune response as each tumor contains numerous unique putative neoantigens. Understanding the complexities of tumor heterogeneity is crucial for the development of personalized neoantigen-based vaccines, which hold the potential to revolutionize cancer treatment and improve patient outcomes. In this review, we discuss recent advancements in the design of neoantigen-based cancer vaccines emphasizing the identification, validation, formulation, and targeting of neoantigens while addressing the challenges posed by tumor heterogeneity. The review highlights the application of cutting-edge approaches, such as single-cell sequencing and artificial intelligence to identify immunogenic neoantigens, while outlining current limitations and proposing future research directions to develop effective neoantigen-based vaccines.
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Affiliation(s)
- Saranya Pounraj
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Shuxiong Chen
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Linlin Ma
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
- School of Environment and Science, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
| | - Roberta Mazzieri
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Riccardo Dolcetti
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, The University of Melbourne, Melbourne, Victoria, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Bernd H A Rehm
- Centre for Cell Factories and Biopolymers (CCFB), Griffith Institute for Drug Discovery, Griffith University (Nathan Campus), Brisbane, Queensland, Australia
- Menzies Health Institute Queensland (MHIQ), Griffith University (Gold Coast Campus), Queensland, Australia
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21
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Yi X, Wen B, Ji S, Saltzman AB, Jaehnig EJ, Lei JT, Gao Q, Zhang B. Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification. Mol Cell Proteomics 2024; 23:100707. [PMID: 38154692 PMCID: PMC10831110 DOI: 10.1016/j.mcpro.2023.100707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 11/06/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023] Open
Abstract
Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.
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Affiliation(s)
- Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Shuyi Ji
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion of the Ministry of China, Fudan University, Shanghai, China
| | - Alexander B Saltzman
- Mass Spectrometry Proteomics Core, Advanced Technology Cores, Baylor College of Medicine, Houston, Texas, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital and Key Laboratory of Carcinogenesis and Cancer Invasion of the Ministry of China, Fudan University, Shanghai, China
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
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22
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Gorshkov V, Kjeldsen F. Exploiting Charge State Distribution To Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses. Anal Chem 2024; 96:1167-1177. [PMID: 38183295 DOI: 10.1021/acs.analchem.3c04270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization is the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public data sets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimensions of the CSD demonstrate high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence and constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments) qualified CSD data suitable to train a deep-learning model capable of accurately predicting CSD values (mean cosine similarity - 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely used retention time dimension, it still holds the potential for application in direct infusion proteomics.
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Affiliation(s)
- Vladimir Gorshkov
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
| | - Frank Kjeldsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark
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23
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Liao H, Barra C, Zhou Z, Peng X, Woodhouse I, Tailor A, Parker R, Carré A, Borrow P, Hogan MJ, Paes W, Eisenlohr LC, Mallone R, Nielsen M, Ternette N. MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer. Nat Commun 2024; 15:661. [PMID: 38253617 PMCID: PMC10803737 DOI: 10.1038/s41467-023-44460-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.
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Affiliation(s)
- Hanqing Liao
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | | | - Zhicheng Zhou
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
| | - Xu Peng
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
| | - Isaac Woodhouse
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Arun Tailor
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Robert Parker
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Alexia Carré
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
| | - Persephone Borrow
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Michael J Hogan
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wayne Paes
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Laurence C Eisenlohr
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Roberto Mallone
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
- Assistance Publique Hôpitaux de Paris, Service de Diabétologie et Immunologie Clinique, Cochin Hospital, 75014, Paris, France
| | | | - Nicola Ternette
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK.
- University of Utrecht, Department of Pharmaceutical Sciences, 3584 CH, Utrecht, The Netherlands.
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24
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Li G, Mahajan S, Ma S, Jeffery ED, Zhang X, Bhattacharjee A, Venkatasubramanian M, Weirauch MT, Miraldi ER, Grimes HL, Sheynkman GM, Tilburgs T, Salomonis N. Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy. Sci Transl Med 2024; 16:eade2886. [PMID: 38232136 DOI: 10.1126/scitranslmed.ade2886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 12/13/2023] [Indexed: 01/19/2024]
Abstract
Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.
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Affiliation(s)
- Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Shweta Mahajan
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Siyuan Ma
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Erin D Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
| | - Xuan Zhang
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Anukana Bhattacharjee
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Meenakshi Venkatasubramanian
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Matthew T Weirauch
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
- Division of Human Genetics, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Emily R Miraldi
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - H Leighton Grimes
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
| | - Tamara Tilburgs
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
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25
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Klaproth-Andrade D, Hingerl J, Bruns Y, Smith NH, Träuble J, Wilhelm M, Gagneur J. Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing. Nat Commun 2024; 15:151. [PMID: 38167372 PMCID: PMC10762064 DOI: 10.1038/s41467-023-44323-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.
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Affiliation(s)
- Daniela Klaproth-Andrade
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Data Science Institute, Technical University of Munich, Garching, Germany
| | - Johannes Hingerl
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Yanik Bruns
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Nicholas H Smith
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Jakob Träuble
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Mathias Wilhelm
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
- Computational Mass Spectrometry, School of Life Sciences, Technical University of Munich, Freising, Germany.
| | - Julien Gagneur
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
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26
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Schrader M. Origins, Technological Advancement, and Applications of Peptidomics. Methods Mol Biol 2024; 2758:3-47. [PMID: 38549006 DOI: 10.1007/978-1-0716-3646-6_1] [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: 04/02/2024]
Abstract
Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.
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Affiliation(s)
- Michael Schrader
- Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany.
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27
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Gabriel W, Picciani M, The M, Wilhelm M. Deep Learning-Assisted Analysis of Immunopeptidomics Data. Methods Mol Biol 2024; 2758:457-483. [PMID: 38549030 DOI: 10.1007/978-1-0716-3646-6_25] [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: 04/02/2024]
Abstract
Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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28
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Schrader M, Fricker LD. Current Challenges and Future Directions in Peptidomics. Methods Mol Biol 2024; 2758:485-498. [PMID: 38549031 DOI: 10.1007/978-1-0716-3646-6_26] [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: 04/02/2024]
Abstract
The field of peptidomics has been under development since its start more than 20 years ago. In this chapter we provide a personal outlook for future directions in this field. The applications of peptidomics technologies are spreading more and more from classical research of peptide hormones and neuropeptides towards commercial applications in plant and food-science. Many clinical applications have been developed to analyze the complexity of biofluids, which are being addressed with new instrumentation, automization, and data processing. Additionally, the newly developed field of immunopeptidomics is showing promise for cancer therapies. In conclusion, peptidomics will continue delivering important information in classical fields like neuropeptides and peptide hormones, benefiting from improvements in state-of-the-art technologies. Moreover, new directions of research such as immunopeptidomics will further complement classical omics technologies and may become routine clinical procedures. Taken together, discoveries of new substances, networks, and applications of peptides can be expected in different disciplines.
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Affiliation(s)
- Michael Schrader
- Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany.
| | - Lloyd D Fricker
- Departments of Molecular Pharmacology and Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
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29
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Van Bael S, Ludwig C, Baggerman G, Temmerman L. Identification and Targeted Quantification of Endogenous Neuropeptides in the Nematode Caenorhabditis elegans Using Mass Spectrometry. Methods Mol Biol 2024; 2758:341-373. [PMID: 38549024 DOI: 10.1007/978-1-0716-3646-6_19] [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: 04/02/2024]
Abstract
The nematode Caenorhabditis elegans lends itself as an excellent model organism for peptidomics studies. Its ease of cultivation and quick generation time make it suitable for high-throughput studies. The nervous system, with its 302 neurons, is probably the best-known and studied endocrine tissue. Moreover, its neuropeptidergic signaling pathways display numerous similarities with those observed in other metazoans. Here, we describe two label-free approaches for neuropeptidomics in C. elegans: one for discovery purposes, and another for targeted quantification and comparisons of neuropeptide levels between different samples. Starting from a detailed peptide extraction procedure, we here outline the liquid chromatography tandem mass spectrometry (LC-MS/MS) setup and describe subsequent data analysis approaches.
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Affiliation(s)
- Sven Van Bael
- Department of Biology, Animal Physiology & Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
| | - Geert Baggerman
- Center for Proteomics, University of Antwerp, Antwerp, Belgium
| | - Liesbet Temmerman
- Department of Biology, Animal Physiology & Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium.
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30
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Bichmann L, Gupta S, Röst H. Data-Independent Acquisition Peptidomics. Methods Mol Biol 2024; 2758:77-88. [PMID: 38549009 DOI: 10.1007/978-1-0716-3646-6_4] [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: 04/02/2024]
Abstract
In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework "Nextflow" ( https://nextflow.io ). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user's interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.
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Affiliation(s)
- Leon Bichmann
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
| | - Shubham Gupta
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Hannes Röst
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
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31
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Meng W, Schreiber RD, Lichti CF. Recent advances in immunopeptidomic-based tumor neoantigen discovery. Adv Immunol 2023; 160:1-36. [PMID: 38042584 DOI: 10.1016/bs.ai.2023.10.001] [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: 12/04/2023]
Abstract
The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.
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Affiliation(s)
- Wei Meng
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States
| | - Robert D Schreiber
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
| | - Cheryl F Lichti
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
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32
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Fan KT, Hsu CW, Chen YR. Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches. MASS SPECTROMETRY REVIEWS 2023; 42:2404-2425. [PMID: 35765846 DOI: 10.1002/mas.21789] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.
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Affiliation(s)
- Kai-Ting Fan
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Chia-Wei Hsu
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Yet-Ran Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
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33
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Chen Z, Lim YW, Neo JY, Ting Chan RS, Koh LQ, Yuen TY, Lim YH, Johannes CW, Gates ZP. De Novo Sequencing of Synthetic Bis-cysteine Peptide Macrocycles Enabled by "Chemical Linearization" of Compound Mixtures. Anal Chem 2023; 95:14870-14878. [PMID: 37724843 PMCID: PMC10569172 DOI: 10.1021/acs.analchem.3c01742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/04/2023] [Indexed: 09/21/2023]
Abstract
A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.
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Affiliation(s)
- Zhi’ang Chen
- Institute
of Molecular and Cell Biology (IMCB), Agency
for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Republic of Singapore
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Yi Wee Lim
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Jin Yong Neo
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Rachel Shu Ting Chan
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Li Quan Koh
- Institute
of Molecular and Cell Biology (IMCB), Agency
for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Republic of Singapore
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Tsz Ying Yuen
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Yee Hwee Lim
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
| | - Charles W. Johannes
- Institute
of Molecular and Cell Biology (IMCB), Agency
for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Republic of Singapore
| | - Zachary P. Gates
- Institute
of Molecular and Cell Biology (IMCB), Agency
for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Republic of Singapore
- Institute
of Sustainability for Chemicals, Energy and Environment (ISCE), Agency for Science, Technology
and Research (A*STAR), 8 Biomedical Grove, #07-01 Neuros, Singapore 138665, Republic
of Singapore
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34
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Skiadopoulou D, Vašíček J, Kuznetsova K, Bouyssié D, Käll L, Vaudel M. Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides. J Proteome Res 2023; 22:3190-3199. [PMID: 37656829 PMCID: PMC10563157 DOI: 10.1021/acs.jproteome.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 09/03/2023]
Abstract
Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.
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Affiliation(s)
- Dafni Skiadopoulou
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - Jakub Vašíček
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - Ksenia Kuznetsova
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - David Bouyssié
- Institut
de Pharmacologie et de Biologie Structurale (IPBS), Université
de Toulouse, CNRS, Université Toulouse III—Paul Sabatier
(UT3), 31000 Toulouse, France
| | - Lukas Käll
- Science
for Life Laboratory, School of Engineering Sciences in Chemistry,
Biotechnology and Health, KTH Royal Institute
of Technology, SE-100 44 Stockholm, Sweden
| | - Marc Vaudel
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
- Department
of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, N-0213 Oslo, Norway
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35
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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36
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Nicholas B, Skipp P. What do cancer-specific CD8+ T cells see? The contribution of immunopeptidomics. Essays Biochem 2023; 67:957-965. [PMID: 37503576 DOI: 10.1042/ebc20220246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Immunopeptidomics is the survey of all peptides displayed on a cell or tissue when bound to human leukocyte antigen (HLA) molecules using tandem mass spectrometry. When attempting to determine the targets of tumour-specific CD8+ T cells, a survey of the potential ligands in tumour tissues is invaluable, and, in comparison with in-silico predictions, provides greater certainty of the existence of individual epitopes, as immunopeptidomics-confirmed CD8+ T-cell epitopes are known to be immunogenic, and direct observation should avoid the risk of autoreactivity which could arise following immunisation with structural homologues. The canonical sources of CD8+ T-cell tumour specific epitopes, such as tumour associated antigens, may be well conserved between patients and tumour types, but are often only weakly immunogenic. Direct observation of tumour-specific neoantigens by immunopeptidomics is rare, although valuable. Thus, there has been increasing interest in the non-canonical origins of tumour-reactive CD8+ T-cell epitopes, such as those arising from proteasomal splicing events, translational/turnover defects and alternative open reading frame reads. Such epitopes can be identified in silico, although validation is more challenging. Non-self CD8+ T-cell epitopes such as viral epitopes may be useful in certain cancer types with known viral origins, however these have been relatively unexplored with immunopeptidomics to date, possibly due to the paucity of source viral proteins in tumour tissues. This review examines the latest evidence for canonical, non-canonical and non-human CD8+ T-cell epitopes identified by immunopeptidomics, and concludes that the relative contribution for each of these sources to anti-tumour CD8+ T-cell reactivity is currently uncertain.
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Affiliation(s)
- Ben Nicholas
- Centre for Proteomic Research, Biological Sciences and Institute for Life Sciences, Building 85, University of Southampton, SO17 1BJ, U.K
| | - Paul Skipp
- Centre for Proteomic Research, Biological Sciences and Institute for Life Sciences, Building 85, University of Southampton, SO17 1BJ, U.K
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37
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Schendel DJ. Evolution by innovation as a driving force to improve TCR-T therapies. Front Oncol 2023; 13:1216829. [PMID: 37810959 PMCID: PMC10552759 DOI: 10.3389/fonc.2023.1216829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/16/2023] [Indexed: 10/10/2023] Open
Abstract
Adoptive cell therapies continually evolve through science-based innovation. Specialized innovations for TCR-T therapies are described here that are embedded in an End-to-End Platform for TCR-T Therapy Development which aims to provide solutions for key unmet patient needs by addressing challenges of TCR-T therapy, including selection of target antigens and suitable T cell receptors, generation of TCR-T therapies that provide long term, durable efficacy and safety and development of efficient and scalable production of patient-specific (personalized) TCR-T therapy for solid tumors. Multiple, combinable, innovative technologies are used in a systematic and sequential manner in the development of TCR-T therapies. One group of technologies encompasses product enhancements that enable TCR-T therapies to be safer, more specific and more effective. The second group of technologies addresses development optimization that supports discovery and development processes for TCR-T therapies to be performed more quickly, with higher quality and greater efficiency. Each module incorporates innovations layered onto basic technologies common to the field of immunology. An active approach of "evolution by innovation" supports the overall goal to develop best-in-class TCR-T therapies for treatment of patients with solid cancer.
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Affiliation(s)
- Dolores J. Schendel
- Medigene Immunotherapies GmbH, Planegg, Germany
- Medigene AG, Planegg, Germany
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38
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Moritz RL, Deutsch EW, van Heesch S. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome? Mol Cell Proteomics 2023; 22:100631. [PMID: 37572790 PMCID: PMC10506109 DOI: 10.1016/j.mcpro.2023.100631] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/14/2023] Open
Abstract
Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."
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Affiliation(s)
- John R Prensner
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
| | | | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Agora Center Bugnon 25A, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, Washington, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington, USA
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39
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Tretter C, de Andrade Krätzig N, Pecoraro M, Lange S, Seifert P, von Frankenberg C, Untch J, Zuleger G, Wilhelm M, Zolg DP, Dreyer FS, Bräunlein E, Engleitner T, Uhrig S, Boxberg M, Steiger K, Slotta-Huspenina J, Ochsenreither S, von Bubnoff N, Bauer S, Boerries M, Jost PJ, Schenck K, Dresing I, Bassermann F, Friess H, Reim D, Grützmann K, Pfütze K, Klink B, Schröck E, Haller B, Kuster B, Mann M, Weichert W, Fröhling S, Rad R, Hiltensperger M, Krackhardt AM. Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification. Nat Commun 2023; 14:4632. [PMID: 37532709 PMCID: PMC10397250 DOI: 10.1038/s41467-023-39570-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/19/2023] [Indexed: 08/04/2023] Open
Abstract
Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.
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Affiliation(s)
- Celina Tretter
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Niklas de Andrade Krätzig
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Matteo Pecoraro
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Sebastian Lange
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Philipp Seifert
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Clara von Frankenberg
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Johannes Untch
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Gabriela Zuleger
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Mathias Wilhelm
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Computational Mass Spectrometry, Freising, Germany
| | - Daniel P Zolg
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
| | - Florian S Dreyer
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Eva Bräunlein
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Thomas Engleitner
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Sebastian Uhrig
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Precision Oncology Program, NCT Heidelberg, Heidelberg, Germany
| | - Melanie Boxberg
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Katja Steiger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Julia Slotta-Huspenina
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Sebastian Ochsenreither
- German Cancer Consortium (DKTK), partner site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Hematology, Oncology and Tumor Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nikolas von Bubnoff
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Sebastian Bauer
- German Cancer Consortium (DKTK), partner site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology and Sarcoma Center, West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Melanie Boerries
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp J Jost
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Clinical Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- University Comprehensive Cancer Center Graz, Medical University of Graz, Graz, Austria
| | - Kristina Schenck
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Iska Dresing
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Florian Bassermann
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
| | - Helmut Friess
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Daniel Reim
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Konrad Grützmann
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Core Unit Molecular Tumor Diagnostics (CMTD), NCT Dresden, Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Katrin Pfütze
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Klink
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
| | - Evelin Schröck
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases Dresden (NCT/UCC), Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Bernhard Haller
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of AI and Informatics in Medicine, Munich, Germany
| | - Bernhard Kuster
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Freising, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
| | - Wilko Weichert
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roland Rad
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Michael Hiltensperger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Angela M Krackhardt
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany.
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany.
- Malteser Krankenhaus St. Franziskus-Hospital, Flensburg, Germany.
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40
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Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
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41
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Sun W, Zhang Q, Zhang X, Tran NH, Ziaur Rahman M, Chen Z, Peng C, Ma J, Li M, Xin L, Shan B. Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics. Nat Commun 2023; 14:4046. [PMID: 37422459 PMCID: PMC10329677 DOI: 10.1038/s41467-023-39699-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 06/19/2023] [Indexed: 07/10/2023] Open
Abstract
Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.
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Affiliation(s)
- Weiping Sun
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Qianqiu Zhang
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Xiyue Zhang
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Ngoc Hieu Tran
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - M Ziaur Rahman
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Zheng Chen
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Chao Peng
- BaizhenBio Inc., Wuhan, China
- Wuhan BioBank, Wuhan, China
| | - Jun Ma
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
- Henan Academy of Sciences, Zhengzhou, Henan, China.
| | - Lei Xin
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada.
| | - Baozhen Shan
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada.
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42
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Geer LY, Lapin J, Slotta DJ, Mak TD, Stein SE. AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence. J Proteome Res 2023; 22:2246-2255. [PMID: 37232537 PMCID: PMC10542943 DOI: 10.1021/acs.jproteome.2c00807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.
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Affiliation(s)
- Lewis Y. Geer
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Joel Lapin
- Department of Physics, Georgetown University, Washington, DC 20057, United States
- Associate, Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Douglas J. Slotta
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Tytus D. Mak
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
| | - Stephen E. Stein
- Mass Spectrometry Data Center, National Institute of Standards and Technology, Biomolecular Measurement Division, 100 Bureau Dr., Gaithersburg, Maryland 20899, United States
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43
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Ternette N, Adamopoulou E, Purcell AW. How mass spectrometric interrogation of MHC class I ligandomes has advanced our understanding of immune responses to viruses. Semin Immunol 2023; 68:101780. [PMID: 37276649 DOI: 10.1016/j.smim.2023.101780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Affiliation(s)
- Nicola Ternette
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford OX37BN, UK.
| | - Eleni Adamopoulou
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford OX37BN, UK
| | - Anthony W Purcell
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
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44
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Stutzmann C, Peng J, Wu Z, Savoie C, Sirois I, Thibault P, Wheeler AR, Caron E. Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery. CELL REPORTS METHODS 2023; 3:100511. [PMID: 37426761 PMCID: PMC10326451 DOI: 10.1016/j.crmeth.2023.100511] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.
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Affiliation(s)
| | - Jiaxi Peng
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Zhaoguan Wu
- CHU Sainte Justine Research Center, Montreal, QC, Canada
| | | | | | - Pierre Thibault
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada
- Department of Chemistry, University of Montreal, Montreal, QC, Canada
| | - Aaron R. Wheeler
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Etienne Caron
- CHU Sainte Justine Research Center, Montreal, QC, Canada
- Department of Pathology and Cellular Biology, University of Montreal, Montreal, QC, Canada
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45
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Wilburn DB, Shannon AE, Spicer V, Richards AL, Yeung D, Swaney DL, Krokhin OV, Searle BC. Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.30.542978. [PMID: 37398395 PMCID: PMC10312522 DOI: 10.1101/2023.05.30.542978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.
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46
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Deutsch EW, van Heesch S. What can Ribo-seq and proteomics tell us about the non-canonical proteome? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541049. [PMID: 37292611 PMCID: PMC10245706 DOI: 10.1101/2023.05.16.541049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.
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Affiliation(s)
- John R. Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | | | - Leron W. Kok
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
| | - Karl R. Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jonathan M. Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Eric W. Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
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47
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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48
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Oreper D, Klaeger S, Jhunjhunwala S, Delamarre L. The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens. Semin Immunol 2023; 67:101758. [PMID: 37027981 DOI: 10.1016/j.smim.2023.101758] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/08/2023]
Abstract
Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.
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Affiliation(s)
- Daniel Oreper
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
| | - Susan Klaeger
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
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49
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Jiang J, Li J, Li J, Pei H, Li M, Zou Q, Lv Z. A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features. Foods 2023; 12:foods12071498. [PMID: 37048319 PMCID: PMC10094688 DOI: 10.3390/foods12071498] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.
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Affiliation(s)
- Jici Jiang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jiayu Li
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Junxian Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Hongdi Pei
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
- Wu Yuzhang Honors College, Sichuan University, Chengdu 610065, China
| | - Mingxin Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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50
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Peng X, Woodhouse I, Hancock G, Parker R, Marx K, Müller J, Salatino S, Partridge T, Nicastri A, Liao H, Kruppa G, Hellner K, Dorrell L, Ternette N. Novel canonical and non-canonical viral antigens extend current targets for immunotherapy of HPV-driven cervical cancer. iScience 2023; 26:106101. [PMID: 36876126 PMCID: PMC9978627 DOI: 10.1016/j.isci.2023.106101] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/30/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023] Open
Abstract
Current immunotherapeutic approaches for human papillomavirus (HPV)-driven cervical cancer target the viral oncogenes E6 and E7. We report viral canonical and alternative reading frame (ARF)-derived sequences presented on cervical tumor cells, including antigens encoded by the conserved viral gene E1. We confirm immunogenicity of the identified viral peptides in HPV-positive women, and women with cervical intraepithelial neoplasia. We observe consistent transcription of the E1, E6, and E7 genes in 10 primary cervical tumor resections from the four most common high-risk HPV subtypes (HPV16, 18, 31, and 45), suggesting the suitability of E1 as therapeutic target. We finally confirm HLA presentation of canonical peptides derived from E6 and E7, and ARF-derived viral peptides from a reverse-strand transcript spanning the HPV E1 and E2 genes in primary human cervical tumor tissue. Our results extend currently known viral immunotherapeutic targets in cervical cancer and highlight E1 as an important cervical cancer antigen.
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Affiliation(s)
- Xu Peng
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
| | - Isaac Woodhouse
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
| | - Gemma Hancock
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
| | - Robert Parker
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Kristina Marx
- Bruker Daltonics, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Julius Müller
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Silvia Salatino
- Wellcome Centre Human Genetics, University of Oxford, OX3 7BN Oxford, UK
| | - Thomas Partridge
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
| | - Annalisa Nicastri
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Hanqing Liao
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Gary Kruppa
- Bruker Daltonics, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Karin Hellner
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, John Radcliffe Hospital, OX3 9DU Oxford, UK
| | - Lucy Dorrell
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
- Immunocore Ltd., OX14 4RY Abingdon, UK
| | - Nicola Ternette
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
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