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Celis-Giraldo C, Ordoñez D, Díaz-Arévalo D, Bohórquez MD, Ibarrola N, Suárez CF, Rodríguez K, Yepes Y, Rodríguez A, Avendaño C, López-Abán J, Manzano-Román R, Patarroyo MA. Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243. Vaccine 2024; 42:3445-3454. [PMID: 38631956 DOI: 10.1016/j.vaccine.2024.04.042] [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: 12/22/2023] [Revised: 04/04/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.
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Affiliation(s)
- Carmen Celis-Giraldo
- Animal Science Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Bogotá, Colombia; PhD Programme in Tropical Health and Development, Doctoral School "Studii Salamantini", Universidad de Salamanca, Salamanca, Spain
| | - Diego Ordoñez
- Animal Science Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Bogotá, Colombia; PhD Programme in Tropical Health and Development, Doctoral School "Studii Salamantini", Universidad de Salamanca, Salamanca, Spain
| | - Diana Díaz-Arévalo
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Michel D Bohórquez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia; MSc Programme in Microbiology, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Nieves Ibarrola
- Centro de Investigación del Cáncer and Instituto de Biología Molecular y Celular del Cáncer (IBMCC), CSIC-University of Salamanca, Salamanca, Spain
| | - Carlos F Suárez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Kewin Rodríguez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Yoelis Yepes
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Alexander Rodríguez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Catalina Avendaño
- Department of Immunology and Theranostics, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute of City of Hope, National Medical Center, Duarte, CA, United States
| | - Julio López-Abán
- Infectious and Tropical Diseases Group (e-INTRO), IBSAL-CIETUS (Instituto de Investigación Biomédica de Salamanca - Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca), Pharmacy Faculty, Universidad de Salamanca, C/ L. Méndez Nieto s/n, 37007 Salamanca, Spain
| | - Raúl Manzano-Román
- Infectious and Tropical Diseases Group (e-INTRO), IBSAL-CIETUS (Instituto de Investigación Biomédica de Salamanca - Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca), Pharmacy Faculty, Universidad de Salamanca, C/ L. Méndez Nieto s/n, 37007 Salamanca, Spain
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia.
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Eskandari A, Leow TC, Rahman MBA, Oslan SN. Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy. Mol Biotechnol 2024:10.1007/s12033-024-01144-3. [PMID: 38625508 DOI: 10.1007/s12033-024-01144-3] [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: 01/26/2024] [Accepted: 03/15/2024] [Indexed: 04/17/2024]
Abstract
Over the past few decades, cancer immunotherapy has experienced a significant revolution due to the advancements in immune checkpoint inhibitors (ICIs) and adoptive cell therapies (ACTs), along with their regulatory approvals. In recent times, there has been hope in the effectiveness of cancer vaccines for therapy as they have been able to stimulate de novo T-cell reactions against tumor antigens. These tumor antigens include both tumor-associated antigen (TAA) and tumor-specific antigen (TSA). Nevertheless, the constant quest to fully achieve these abilities persists. Therefore, this review offers a broad perspective on the existing status of cancer immunizations. Cancer vaccine design has been revolutionized due to the advancements made in antigen selection, the development of antigen delivery systems, and a deeper understanding of the strategic intricacies involved in effective antigen presentation. In addition, this review addresses the present condition of clinical tests and deliberates on their approaches, with a particular emphasis on the immunogenicity specific to tumors and the evaluation of effectiveness against tumors. Nevertheless, the ongoing clinical endeavors to create cancer vaccines have failed to produce remarkable clinical results as a result of substantial obstacles, such as the suppression of the tumor immune microenvironment, the identification of suitable candidates, the assessment of immune responses, and the acceleration of vaccine production. Hence, there are possibilities for the industry to overcome challenges and enhance patient results in the coming years. This can be achieved by recognizing the intricate nature of clinical issues and continuously working toward surpassing existing limitations.
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Affiliation(s)
- Azadeh Eskandari
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
| | - Thean Chor Leow
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
| | | | - Siti Nurbaya Oslan
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
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3
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Wang X, Wu T, Jiang Y, Chen T, Pan D, Jin Z, Xie J, Quan L, Lyu Q. RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding. Bioinformatics 2024; 40:btad785. [PMID: 38175759 PMCID: PMC10796178 DOI: 10.1093/bioinformatics/btad785] [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: 05/04/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.
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Affiliation(s)
- Xuejiao Wang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Yelu Jiang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Taoning Chen
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Deng Pan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhi Jin
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Jingxin Xie
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
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Fan T, Zhang M, Yang J, Zhu Z, Cao W, Dong C. Therapeutic cancer vaccines: advancements, challenges, and prospects. Signal Transduct Target Ther 2023; 8:450. [PMID: 38086815 PMCID: PMC10716479 DOI: 10.1038/s41392-023-01674-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 12/18/2023] Open
Abstract
With the development and regulatory approval of immune checkpoint inhibitors and adoptive cell therapies, cancer immunotherapy has undergone a profound transformation over the past decades. Recently, therapeutic cancer vaccines have shown promise by eliciting de novo T cell responses targeting tumor antigens, including tumor-associated antigens and tumor-specific antigens. The objective was to amplify and diversify the intrinsic repertoire of tumor-specific T cells. However, the complete realization of these capabilities remains an ongoing pursuit. Therefore, we provide an overview of the current landscape of cancer vaccines in this review. The range of antigen selection, antigen delivery systems development the strategic nuances underlying effective antigen presentation have pioneered cancer vaccine design. Furthermore, this review addresses the current status of clinical trials and discusses their strategies, focusing on tumor-specific immunogenicity and anti-tumor efficacy assessment. However, current clinical attempts toward developing cancer vaccines have not yielded breakthrough clinical outcomes due to significant challenges, including tumor immune microenvironment suppression, optimal candidate identification, immune response evaluation, and vaccine manufacturing acceleration. Therefore, the field is poised to overcome hurdles and improve patient outcomes in the future by acknowledging these clinical complexities and persistently striving to surmount inherent constraints.
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Affiliation(s)
- Ting Fan
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Mingna Zhang
- Postgraduate Training Base, Shanghai East Hospital, Jinzhou Medical University, Shanghai, 200120, China
| | - Jingxian Yang
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Zhounan Zhu
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Wanlu Cao
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
| | - Chunyan Dong
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
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5
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De Groot AS, Khan S, Mattei AE, Lelias S, Martin WD. Does human homology reduce the potential immunogenicity of non-antibody scaffolds? Front Immunol 2023; 14:1215939. [PMID: 38022550 PMCID: PMC10664710 DOI: 10.3389/fimmu.2023.1215939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Biologics developers are moving beyond antibodies for delivery of a wide range of therapeutic interventions. These non-antibody modalities are often based on 'natural' protein scaffolds that are modified to deliver bioactive sequences. Both human-derived and non-human-sourced scaffold proteins have been developed. New types of "non-antibody" scaffolds are still being discovered, as they offer attractive alternatives to monoclonals due to their smaller size, improved stability, and ease of synthesis. They are believed to have low immunogenic potential. However, while several human-sourced protein scaffolds have not been immunogenic in clinical studies, this may not predict their overall performance in other therapeutic applications. A preliminary evaluation of their potential for immunogenicity is warranted. Immunogenicity risk potential has been clearly linked to the presence of T "helper" epitopes in the sequence of biologic therapeutics. In addition, tolerogenic epitopes are present in some human proteins and may decrease their immunogenic potential. While the detailed sequences of many non-antibody scaffold therapeutic candidates remain unpublished, their backbone sequences are available for review and analysis. We assessed 12 example non-antibody scaffold backbone sequences using our epitope-mapping tools (EpiMatrix) for this perspective. Based on EpiMatrix scoring, their HLA DRB1-restricted T cell epitope content appears to be lower than the average protein, and sequences that may act as tolerogenic epitopes are present in selected human-derived scaffolds. Assessing the potential immunogenicity of scaffold proteins regarding self and non-self T cell epitopes may be of use for drug developers and clinicians, as these exciting new non-antibody molecules begin to emerge from the preclinical pipeline into clinical use.
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Affiliation(s)
- Anne S. De Groot
- EpiVax, Providence, RI, United States
- University of Georgia, Center for Vaccines and Immunology, Athens, GA, United States
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6
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Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Hung YM, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 2023; 56:1681-1698.e13. [PMID: 37301199 PMCID: PMC10519123 DOI: 10.1016/j.immuni.2023.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/08/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
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Affiliation(s)
- Martin Stražar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jihye Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas K Pedersen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuan-Mao Hung
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kayla Ortiz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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7
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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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8
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Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity 2023:S1074-7613(23)00129-2. [PMID: 37023751 DOI: 10.1016/j.immuni.2023.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/09/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023]
Abstract
CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.
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Affiliation(s)
- Julien Racle
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
| | - Philippe Guillaume
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Justine Michaux
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Amédé Larabi
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kelvin Lau
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marta A S Perez
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Raphaël Genolet
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Vincent Zoete
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Florence Pojer
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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9
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Sollid LM, Iversen R. Tango of B cells with T cells in the making of secretory antibodies to gut bacteria. Nat Rev Gastroenterol Hepatol 2023; 20:120-128. [PMID: 36056203 DOI: 10.1038/s41575-022-00674-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/27/2022] [Indexed: 02/03/2023]
Abstract
Polymeric IgA and IgM are transported across the epithelial barrier from plasma cells in the lamina propria to exert a function in the gut lumen as secretory antibodies. Many secretory antibodies are reactive with the gut bacteria, and mounting evidence suggests that these antibodies are important for the host to control gut bacterial communities. However, we have incomplete knowledge of how bacteria-reactive secretory antibodies are formed. Antibodies from gut plasma cells often show bacterial cross-species reactivity, putting the degree of specificity behind anti-bacterial antibody responses into question. Such cross-species reactive antibodies frequently recognize non-genome-encoded membrane glycan structures. On the other hand, the T cell epitopes are peptides encoded in the bacterial genomes, thereby allowing a higher degree of predictable specificity on the T cell side of anti-bacterial immune responses. In this Perspective, we argue that the production of bacteria-reactive secretory antibodies is mainly controlled by the antigen specificity of T cells, which provide help to B cells. To be able to harness this system (for instance, for manipulation with vaccines), we need to obtain insight into the bacterial epitopes recognized by T cells in addition to characterizing the reactivity of the antibodies.
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Affiliation(s)
- Ludvig M Sollid
- K.G. Jebsen Coeliac Disease Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. .,Department of Immunology, Oslo University Hospital - Rikshospitalet, Oslo, Norway.
| | - Rasmus Iversen
- K.G. Jebsen Coeliac Disease Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. .,Department of Immunology, Oslo University Hospital - Rikshospitalet, Oslo, Norway.
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10
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Liu Z, Jin J, Cui Y, Xiong Z, Nasiri A, Zhao Y, Hu J. DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2188-2196. [PMID: 33886473 DOI: 10.1109/tcbb.2021.3074927] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.
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11
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McCaffrey P. Bioinformatic Techniques for Vaccine Development: Epitope Prediction and Structural Vaccinology. Methods Mol Biol 2022; 2412:413-423. [PMID: 34918258 DOI: 10.1007/978-1-0716-1892-9_21] [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: 06/14/2023]
Abstract
Structural vaccinology involves characterizing the interactions between an antigen and antibodies or host immune receptors. Central to this is the task of epitope prediction, which involves describing the binding affinity and interactions of a given peptide typically to the major histocompatibility complex in the case of T-cells or to the antibodies in the case of B-cells. Several computational models exist for this purpose which we will review here. Generally, epitope predictions for MHC-I and MHC-II are substantially different tasks as well as epitope prediction for continuous versus discontinuous B-cell epitopes. Overall, these models suffer from overprediction of epitopes although general themes support both the use of neural networks as well as the incorporation of more abundant and more varied experimental annotation into model training as valuable in improving predictive performance.
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Affiliation(s)
- Peter McCaffrey
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.
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12
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Symonds P, Marcu A, Cook KW, Metheringham RL, Durrant LG, Brentville VA. Citrullinated Epitopes Identified on Tumour MHC Class II by Peptide Elution Stimulate Both Regulatory and Th1 Responses and Require Careful Selection for Optimal Anti-Tumour Responses. Front Immunol 2021; 12:764462. [PMID: 34858415 PMCID: PMC8630742 DOI: 10.3389/fimmu.2021.764462] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background Somatic mutations or post-translational modifications of proteins result in changes that enable immune recognition. One such post-translational modification is citrullination, the conversion of arginine residues to citrulline. Citrullinated peptides are presented on MHC class II (MHCII) via autophagy which is upregulated by cellular stresses such as tumourigenesis. Methods Peptides were eluted from B16 melanoma expressing HLA-DP4 and analysed by mass spectrometry to profile the presented citrullinated repertoire. Initially, seven of the identified citrullinated peptides were used in combination to vaccinate HLA-DP4 transgenic mice. Immune responses were characterised from the combination and individual vaccines by ex vivo cytokine ELISpot assay and assessed for tumour therapy. Results The combination vaccine induced only weak anti-tumour therapy in the B16cDP4 melanoma model. Immune phenotyping revealed a dominant IFNγ response to citrullinated matrix metalloproteinase-21 peptide (citMMP21) and an IL-10 response to cytochrome p450 peptide (citCp450). Exclusion of the IL-10 inducing citCp450 peptide from the combined vaccine failed to recover a strong anti-tumour response. Single peptide immunisation confirmed the IFNγ response from citMMP21 and the IL-10 response from citCp450 but also showed that citrullinated Glutamate receptor ionotropic (citGRI) peptide stimulated a low avidity IFNγ response. Interestingly, both citMMP21 and citGRI peptides individually, stimulated strong anti-tumour responses that were significantly better than the combined vaccine. In line with the citGRI T cell avidity, it required high dose immunisation to induce an anti-tumour response. This suggests that as the peptides within the combined vaccine had similar binding affinities to MHC-II the combination vaccine may have resulted in lower presentation of each epitope and weak anti-tumour immunity. Conclusion We demonstrate that tumours present citrullinated peptides that can stimulate Th1 and regulatory responses and that competition likely exists between similar affinity peptides. Characterisation of responses from epitopes identified by peptide elution are necessary to optimise selection for tumour therapy.
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Affiliation(s)
- Peter Symonds
- Scancell Limited, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany.,Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumour Therapies", University of Tübingen, Tübingen, Germany
| | - Katherine W Cook
- Scancell Limited, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Rachael L Metheringham
- Scancell Limited, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
| | - Lindy G Durrant
- Scancell Limited, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom.,Biodiscovery Institute, Division of Cancer and Stem Cells, University of Nottingham, Nottingham, United Kingdom
| | - Victoria A Brentville
- Scancell Limited, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom
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13
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Kovalchik KA, Ma Q, Wessling L, Saab F, Despault J, Kubiniok P, Hamelin DJ, Faridi P, Li C, Purcell AW, Jang A, Paramithiotis E, Tognetti M, Reiter L, Bruderer R, Lanoix J, Bonneil É, Courcelles M, Thibault P, Caron E, Sirois I. MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets. Mol Cell Proteomics 2021; 21:100178. [PMID: 34798331 PMCID: PMC8717601 DOI: 10.1016/j.mcpro.2021.100178] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.
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Affiliation(s)
| | - Qing Ma
- School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, ON K1N 6N5, Canada
| | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Frederic Saab
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Jérôme Despault
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - David J Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Pouya Faridi
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Chen Li
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anthony W Purcell
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anne Jang
- CellCarta, Montreal, QC H2X 3Y7, Canada
| | | | | | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | | | - Joël Lanoix
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Éric Bonneil
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Mathieu Courcelles
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Pierre Thibault
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada; Department of Chemistry, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada.
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada.
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14
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A simple pan-specific RNN model for predicting HLA-II binding peptides. Mol Immunol 2021; 139:177-183. [PMID: 34555693 DOI: 10.1016/j.molimm.2021.09.004] [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: 11/27/2020] [Revised: 08/17/2021] [Accepted: 09/02/2021] [Indexed: 11/19/2022]
Abstract
The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.
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15
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Ramarathinam SH, Ho BK, Dudek NL, Purcell AW. HLA class II immunopeptidomics reveals that co-inherited HLA-allotypes within an extended haplotype can improve proteome coverage for immunosurveillance. Proteomics 2021; 21:e2000160. [PMID: 34357683 DOI: 10.1002/pmic.202000160] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 01/05/2023]
Abstract
Human leucocyte antigen (HLA) class II molecules in humans are encoded by three different loci, HLA-DR, -DQ, and -DP. These molecules share approximately 70% sequence similarity and all present peptide ligands to circulating T cells. While the peptide repertoires of numerous HLA-DR, -DQ, and -DP allotypes have been examined, there have been few reports on the combined repertoire of these co-inherited molecules expressed in a single cell as an extended HLA haplotype. Here we describe the endogenous peptide repertoire of a human B lymphoblastoid cell line (C1R) expressing the class II haplotype HLA-DR12/DQ7/DP4. We have identified 71350 unique naturally processed peptides presented collectively by HLA-DR12, HLA-DQ7, or HLA-DP4. The resulting "haplodome" is complemented by the cellular proteome defined by standard LC-MS/MS approaches. This large dataset has shed light on properties of these class II ligands especially the preference for membrane and extracellular source proteins. Our data also provides insights into the co-evolution of these conserved haplotypes of closely linked and co-inherited HLA molecules; which together increase sequence coverage of cellular proteins for immune surveillance with minimal overlap between each co-inherited HLA-class II allomorph.
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Affiliation(s)
- Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Bosco K Ho
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Nadine L Dudek
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
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16
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Taylor HB, Klaeger S, Clauser KR, Sarkizova S, Weingarten-Gabbay S, Graham DB, Carr SA, Abelin JG. MS-Based HLA-II Peptidomics Combined With Multiomics Will Aid the Development of Future Immunotherapies. Mol Cell Proteomics 2021; 20:100116. [PMID: 34146720 PMCID: PMC8327157 DOI: 10.1016/j.mcpro.2021.100116] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 12/25/2022] Open
Abstract
Immunotherapies have emerged to treat diseases by selectively modulating a patient's immune response. Although the roles of T and B cells in adaptive immunity have been well studied, it remains difficult to select targets for immunotherapeutic strategies. Because human leukocyte antigen class II (HLA-II) peptides activate CD4+ T cells and regulate B cell activation, proliferation, and differentiation, these peptide antigens represent a class of potential immunotherapy targets and biomarkers. To better understand the molecular basis of how HLA-II antigen presentation is involved in disease progression and treatment, systematic HLA-II peptidomics combined with multiomic analyses of diverse cell types in healthy and diseased states is required. For this reason, MS-based innovations that facilitate investigations into the interplay between disease pathologies and the presentation of HLA-II peptides to CD4+ T cells will aid in the development of patient-focused immunotherapies.
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Affiliation(s)
- Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Susan Klaeger
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Shira Weingarten-Gabbay
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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17
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New peptides with immunomodulatory activity identified from rice proteins through peptidomic and in silico analysis. Food Chem 2021; 364:130357. [PMID: 34174647 DOI: 10.1016/j.foodchem.2021.130357] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 12/22/2022]
Abstract
The new food-derived bio-functional peptides are urgently needed globally, but the separation and purification process for obtaining the immunopeptides from food is low efficiency and highly time-consuming. In the present study, rice proteins were extracted and identified by using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Furthermore, a strategy combining immuno-prediction and in silico simulation was used to screen for peptides showing immunomodulatory activity, including inhibition of the release of nitric oxide, tumor necrosis factor-α, and the interleukins IL-6 and IL-1β in lipopolysaccharide-induced RAW264.7 mouse macrophages. This LC-MS/MS identification and immuno-prediction method may provide insights for the potential identification of more food-derived immunopeptides.
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18
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Chen I, Chen MY, Goedegebuure SP, Gillanders WE. Challenges targeting cancer neoantigens in 2021: a systematic literature review. Expert Rev Vaccines 2021; 20:827-837. [PMID: 34047245 DOI: 10.1080/14760584.2021.1935248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.
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Affiliation(s)
- Ina Chen
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA
| | - Michael Y Chen
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA
| | - S Peter Goedegebuure
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA.,The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, MO, USA
| | - William E Gillanders
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA.,The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, MO, USA
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19
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Fisch A, Reynisson B, Benedictus L, Nicastri A, Vasoya D, Morrison I, Buus S, Ferreira BR, Kinney Ferreira de Miranda Santos I, Ternette N, Connelley T, Nielsen M. Integral Use of Immunopeptidomics and Immunoinformatics for the Characterization of Antigen Presentation and Rational Identification of BoLA-DR-Presented Peptides and Epitopes. THE JOURNAL OF IMMUNOLOGY 2021; 206:2489-2497. [PMID: 33789985 DOI: 10.4049/jimmunol.2001409] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 03/01/2021] [Indexed: 02/04/2023]
Abstract
MHC peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein Ags to elicit functional T cell responses. Liquid chromatography-mass spectrometry analysis of MHC-eluted ligand data has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of Ag presentation have reached a high level of accuracy for both MHC class I and class II. In this study, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte Ag class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by eluted ligand data derived from bovine cell lines expressing a range of DRB3 alleles prevalent in Holstein-Friesian populations. The model generated (NetBoLAIIpan, available as a Web server at www.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR-restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced mass spectrometry peptidomics with immunoinformatics for characterization of the BoLA-DR Ag presentation system and provide a prediction tool that can be used to assist in rational evaluation and selection of bovine CD4 T cell epitopes.
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Affiliation(s)
- Andressa Fisch
- Ribeirão Preto College of Nursing, University of São Paulo, Av Bandeirantes, Ribeirão Preto, Brazil
| | - Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | - Annalisa Nicastri
- The Jenner Institute, Nuffield Department of Medicine, Oxford, United Kingdom
| | - Deepali Vasoya
- The Roslin Institute, Edinburgh, Midlothian, United Kingdom
| | - Ivan Morrison
- The Roslin Institute, Edinburgh, Midlothian, United Kingdom
| | - Søren Buus
- Laboratory of Experimental Immunology, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Nicola Ternette
- The Jenner Institute, Nuffield Department of Medicine, Oxford, United Kingdom
| | - Tim Connelley
- The Roslin Institute, Edinburgh, Midlothian, United Kingdom
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark .,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
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20
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A comparative analysis of SLA-DRB1 genetic diversity in Colombian (creoles and commercial line) and worldwide swine populations. Sci Rep 2021; 11:4340. [PMID: 33619347 PMCID: PMC7900169 DOI: 10.1038/s41598-021-83637-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/18/2021] [Indexed: 12/30/2022] Open
Abstract
Analysing pig class II mayor histocompatibility complex (MHC) molecules is mainly related to antigen presentation. Identifying frequently-occurring alleles in pig populations is an important aspect to be considered when developing peptide-based vaccines. Colombian creole pig populations have had to adapt to local conditions since entering Colombia; a recent census has shown low amounts of pigs which is why they are considered protected by the Colombian government. Commercial hybrids are more attractive regarding production. This research has been aimed at describing the allele distribution of Colombian pigs from diverse genetic backgrounds and comparing Colombian SLA-DRB1 locus diversity to that of internationally reported populations. Twenty SLA-DRB1 alleles were identified in the six populations analysed here using sequence-based typing. The amount of alleles ranged from six (Manta and Casco Mula) to nine (San Pedreño). Only one allele (01:02) having > 5% frequency was shared by all three commercial line populations. Allele 02:01:01 was shared by five populations (around > 5% frequency). Global FST indicated that pig populations were clearly structured, as 20.6% of total allele frequency variation was explained by differences between populations (FST = 0.206). This study’s results confirmed that the greatest diversity occurred in wild boars, thereby contrasting with low diversity in domestic pig populations.
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21
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Jappe EC, Garde C, Ramarathinam SH, Passantino E, Illing PT, Mifsud NA, Trolle T, Kringelum JV, Croft NP, Purcell AW. Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design. Nat Commun 2020; 11:6305. [PMID: 33298915 PMCID: PMC7726561 DOI: 10.1038/s41467-020-20166-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 11/18/2020] [Indexed: 12/12/2022] Open
Abstract
The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.
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Affiliation(s)
- Emma C Jappe
- Evaxion Biotech, Bredgade 34E, 1260, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, 2800, Lyngby, Denmark
| | | | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Ethan Passantino
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Patricia T Illing
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Nicole A Mifsud
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Thomas Trolle
- Evaxion Biotech, Bredgade 34E, 1260, Copenhagen, Denmark
| | | | - Nathan P Croft
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
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22
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Rahman MA, Murata K, Burt BD, Hirano N. Changing the landscape of tumor immunology: novel tools to examine T cell specificity. Curr Opin Immunol 2020; 69:1-9. [PMID: 33307272 DOI: 10.1016/j.coi.2020.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 11/10/2020] [Indexed: 01/14/2023]
Abstract
Immunotherapy has established itself as a stalwart arm in patient care and with precision medicine forms the new paradigm in cancer treatment. T cells are an important group of immune cells capable of potent cancer immune surveillance and immunity. The advent of bioinformatics, particularly more recent advances incorporating algorithms employing machine learning, provide a seemingly limitless ability for T cell analysis and hypothesis generation. Such endeavors have become indispensable to research efforts accelerating and evolving to such an extent that there exists an appreciable gap between knowledge and proof of function and application. Exciting new technologies such as DNA barcoding, cytometry by time-of-flight (CyTOF), and peptide-exchangeable pHLA multimers inclusive of rare and difficult HLA alleles offer high-throughput cell-by-cell analytical capabilities. These outstanding recent contributions to T cell research will help close this gap and potentially bring practical benefit to patients.
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Affiliation(s)
- Muhammed A Rahman
- University of Queensland, Australia; Tumor Immunotherapy Program, Princess Margaret Cancer Centre, University Health Network, Canada
| | - Kenji Murata
- Tumor Immunotherapy Program, Princess Margaret Cancer Centre, University Health Network, Canada; Department of Pathology, Sapporo Medical University School of Medicine, Japan
| | - Brian D Burt
- Tumor Immunotherapy Program, Princess Margaret Cancer Centre, University Health Network, Canada
| | - Naoto Hirano
- Tumor Immunotherapy Program, Princess Margaret Cancer Centre, University Health Network, Canada; Department of Immunology, University of Toronto, Canada.
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23
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Koşaloğlu-Yalçın Z, Sidney J, Chronister W, Peters B, Sette A. Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA-DQ2.5. Immunology 2020; 162:235-247. [PMID: 33064841 PMCID: PMC7808151 DOI: 10.1111/imm.13279] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/06/2020] [Accepted: 10/07/2020] [Indexed: 12/02/2022] Open
Abstract
Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co‐expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.
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Affiliation(s)
| | - John Sidney
- La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Alessandro Sette
- La Jolla Institute for Immunology, La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, La Jolla, CA, USA
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24
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Attermann AS, Barra C, Reynisson B, Schultz HS, Leurs U, Lamberth K, Nielsen M. Improved prediction of HLA antigen presentation hotspots: Applications for immunogenicity risk assessment of therapeutic proteins. Immunology 2020; 162:208-219. [PMID: 33010039 DOI: 10.1111/imm.13274] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 12/11/2022] Open
Abstract
Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.
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Affiliation(s)
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Heidi Schiøler Schultz
- Assay, Analysis & Characterisation, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Ulrike Leurs
- Assay, Analysis & Characterisation, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Kasper Lamberth
- Assay, Analysis & Characterisation, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
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25
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Sawant MS, Streu CN, Wu L, Tessier PM. Toward Drug-Like Multispecific Antibodies by Design. Int J Mol Sci 2020; 21:E7496. [PMID: 33053650 PMCID: PMC7589779 DOI: 10.3390/ijms21207496] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 12/18/2022] Open
Abstract
The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.
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Affiliation(s)
- Manali S. Sawant
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Craig N. Streu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemistry, Albion College, Albion, MI 49224, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; (M.S.S.); (C.N.S.)
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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26
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Wendorff M, Garcia Alvarez HM, Østerbye T, ElAbd H, Rosati E, Degenhardt F, Buus S, Franke A, Nielsen M. Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction. Front Immunol 2020; 11:1705. [PMID: 32903714 PMCID: PMC7438773 DOI: 10.3389/fimmu.2020.01705] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 06/25/2020] [Indexed: 12/12/2022] Open
Abstract
Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.
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Affiliation(s)
- Mareike Wendorff
- Genetics & Bioinformatics, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | | | - Thomas Østerbye
- Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
| | - Hesham ElAbd
- Genetics & Bioinformatics, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Elisa Rosati
- Genetics & Bioinformatics, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Frauke Degenhardt
- Genetics & Bioinformatics, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Søren Buus
- Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
| | - Andre Franke
- Genetics & Bioinformatics, Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Morten Nielsen
- IIBIO, UNSAM-CONICET, Buenos Aires, Argentina.,Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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27
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Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 2020; 48:W449-W454. [PMID: 32406916 PMCID: PMC7319546 DOI: 10.1093/nar/gkaa379] [Citation(s) in RCA: 850] [Impact Index Per Article: 212.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/17/2020] [Accepted: 04/29/2020] [Indexed: 12/12/2022] Open
Abstract
Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.
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Affiliation(s)
- Birkir Reynisson
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
| | - Bruno Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, BA 16503, Argentina
| | - Sinu Paul
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Department of Medicine, University of California, San Diego, CA 92093, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, BA 16503, Argentina
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28
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Osterbye T, Nielsen M, Dudek NL, Ramarathinam SH, Purcell AW, Schafer-Nielsen C, Buus S. HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions. THE JOURNAL OF IMMUNOLOGY 2020; 205:290-299. [PMID: 32482711 DOI: 10.4049/jimmunol.2000224] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 04/22/2020] [Indexed: 01/26/2023]
Abstract
The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.
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Affiliation(s)
- Thomas Osterbye
- Department of Immunology and Microbiology, University of Copenhagen, DK-2200 Copenhagen, Denmark;
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 San Martín, Argentina
| | - Nadine L Dudek
- Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia; and
| | - Sri H Ramarathinam
- Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia; and
| | - Anthony W Purcell
- Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia; and
| | | | - Soren Buus
- Department of Immunology and Microbiology, University of Copenhagen, DK-2200 Copenhagen, Denmark
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29
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In-depth mining of the immunopeptidome of an acute myeloid leukemia cell line using complementary ligand enrichment and data acquisition strategies. Mol Immunol 2020; 123:7-17. [PMID: 32387766 DOI: 10.1016/j.molimm.2020.04.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/07/2020] [Accepted: 04/12/2020] [Indexed: 12/15/2022]
Abstract
The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.
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30
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Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data. J Proteome Res 2020; 19:2304-2315. [PMID: 32308001 DOI: 10.1021/acs.jproteome.9b00874] [Citation(s) in RCA: 224] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.
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Affiliation(s)
- Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby 2800, Denmark
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, Lyngby 2800, Denmark
| | | | - William H Hildebrand
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, United States
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, California 92037, United States.,Department of Medicine, University of California, San Diego, San Diego, California 92093, United States
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby 2800, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín CP1650, Argentina
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31
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Biernacki MA, Bleakley M. Neoantigens in Hematologic Malignancies. Front Immunol 2020; 11:121. [PMID: 32117272 PMCID: PMC7033457 DOI: 10.3389/fimmu.2020.00121] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/16/2020] [Indexed: 12/18/2022] Open
Abstract
T cell cancer neoantigens are created from peptides derived from cancer-specific aberrant proteins, such as mutated and fusion proteins, presented in complex with human leukocyte antigens on the cancer cell surface. Because expression of the aberrant target protein is exclusive to malignant cells, immunotherapy directed against neoantigens should avoid “on-target, off-tumor” toxicity. The efficacy of neoantigen vaccines in melanoma and glioblastoma and of adoptive transfer of neoantigen-specific T cells in epithelial tumors indicates that neoantigens are valid therapeutic targets. Improvements in sequencing technology and innovations in antigen discovery approaches have facilitated the identification of neoantigens. In comparison to many solid tumors, hematologic malignancies have few mutations and thus fewer potential neoantigens. Despite this, neoantigens have been identified in a wide variety of hematologic malignancies. These include mutated nucleophosmin1 and PML-RARA in acute myeloid leukemia, ETV6-RUNX1 fusions and other mutated proteins in acute lymphoblastic leukemia, BCR-ABL1 fusions in chronic myeloid leukemia, driver mutations in myeloproliferative neoplasms, immunoglobulins in lymphomas, and proteins derived from patient-specific mutations in chronic lymphoid leukemias. We will review advances in the field of neoantigen discovery, describe the spectrum of identified neoantigens in hematologic malignancies, and discuss the potential of these neoantigens for clinical translation.
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Affiliation(s)
- Melinda A Biernacki
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.,Department of Medicine, University of Washington, Seattle, WA, United States
| | - Marie Bleakley
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.,Department of Pediatrics, University of Washington, Seattle, WA, United States
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32
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Paul S, Grifoni A, Peters B, Sette A. Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions. Front Immunol 2020; 10:3151. [PMID: 32117208 PMCID: PMC7012937 DOI: 10.3389/fimmu.2019.03151] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 12/30/2019] [Indexed: 01/01/2023] Open
Abstract
Antidrug antibody (ADA) responses impact drug safety, potency, and efficacy. It is generally assumed that ADA responses are associated with human leukocyte antigen (HLA) class II-restricted CD4+ T-cell reactivity. Although this review does not address ADA responses per se, the analysis presented here is relevant to the topic, because measuring or predicting CD4+ T-cell reactivity is a common strategy to address ADA and immunogenicity concerns. Because human CD4+ T-cell reactivity relies on the recognition of peptides bound to HLA class II, prediction, or measurement of the capacity of different peptides to bind or be natural ligands of HLA class II is used as a predictor of CD4+ T-cell reactivity and ADA development. Thus, three different interconnected variables are commonly utilized in predicting T-cell reactivity: major histocompatibility complex (MHC) binding, capacity to be generated as natural HLA ligands, and T-cell immunogenicity. To provide the scientific community with guidance in the relative merit of different approaches, it is necessary to clearly define what outcomes are being considered. Thus, the accuracy of HLA binding predictions varies as a function of what the outcome predicted is, whether it is binding itself, natural processing, or T-cell immunogenicity. Furthermore, it is necessary that the accuracy of prediction is based on rigorous benchmarking, grounded by fair, objective, transparent, and experimental criteria. In this review, we provide our perspective on how different variables and methodologies predict each of the various outcomes and point out knowledge gaps and areas to be addressed by further experimental work.
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Affiliation(s)
- Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Alba Grifoni
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
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33
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Zhao T, Cheng L, Zang T, Hu Y. Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature. Front Genet 2019; 10:1191. [PMID: 31850062 PMCID: PMC6892951 DOI: 10.3389/fgene.2019.01191] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 10/28/2019] [Indexed: 12/27/2022] Open
Abstract
Peptide-based vaccine development needs accurate prediction of the binding affinity between major histocompatibility complex I (MHC I) proteins and their peptide ligands. Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools. However most of them are designed by the shallow neural networks. Bengio said that deep neural networks can learn better fits with less data than shallow neural networks. In our case, some of the alleles only have dozens of peptide data. In addition, we transform each peptide into a characteristic matrix and input it into the model. As we know when dealing with the problem that the input is a matrix, convolutional neural network (CNN) can find the most critical features by itself. Obviously, compared with the traditional neural network model, CNN is more suitable for predicting binding affinity. Different from the previous studies which are based on blocks substitution matrix (BLOSUM), we used novel feature to do the prediction. Since we consider that the order of the sequence, hydropathy index, polarity and the length of the peptide could affect the binding affinity and the properties of these amino acids are key factors for their binding to MHC, we extracted these information from each peptide. In order to make full use of the data we have obtained, we have integrated different lengths of peptides into 15mer based on the binding mode of peptide to MHC I. In order to demonstrate that our method is reliable to predict peptide-MHC binding, we compared our method with several popular methods. The experiments show the superiority of our method.
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Affiliation(s)
- Tianyi Zhao
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Tianyi Zang
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yang Hu
- Department of Computer Science and Technology, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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34
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Mösch A, Raffegerst S, Weis M, Schendel DJ, Frishman D. Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors. Front Genet 2019; 10:1141. [PMID: 31798635 PMCID: PMC6878726 DOI: 10.3389/fgene.2019.01141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/21/2019] [Indexed: 12/30/2022] Open
Abstract
In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.
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Affiliation(s)
- Anja Mösch
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Silke Raffegerst
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Manon Weis
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dolores J. Schendel
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
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Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat Biotechnol 2019; 37:1283-1286. [PMID: 31611696 DOI: 10.1038/s41587-019-0289-6] [Citation(s) in RCA: 174] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 09/11/2019] [Indexed: 02/07/2023]
Abstract
Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.
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