1
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Elrashedy A, Nayel M, Salama A, Salama MM, Hasan ME. Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31. Sci Rep 2024; 14:11951. [PMID: 38789443 PMCID: PMC11126717 DOI: 10.1038/s41598-024-61991-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.
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Affiliation(s)
- Alyaa Elrashedy
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt.
| | - Mohamed Nayel
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Akram Salama
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Mohammed M Salama
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
| | - Mohamed E Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt
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2
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Yang Q, Xu L, Dong W, Li X, Wang K, Dong S, Zhang X, Yang T, Jiang F, Zhang B, Luo G, Gao X, Wang G. HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses. Brief Bioinform 2024; 25:bbae302. [PMID: 38920343 PMCID: PMC11200192 DOI: 10.1093/bib/bbae302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/20/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024] Open
Abstract
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.
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Affiliation(s)
- Qiang Yang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Long Xu
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Weihe Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xiaokun Li
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, Harbin 150090, China
- Shandong Hengxun Technology Co., Ltd., Miaoling Road, Qingdao 266100, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xianyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Haping Road, Harbin 150081, China
| | - Tiansong Yang
- Department of Rehabilitation, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, and Traditional Chinese Medicine Informatics Key Laboratory of Heilongjiang Province, Heping Road, Harbin 150040, China
| | - Feng Jiang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Bin Zhang
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
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3
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Natrajan MS, Hall JM, Weigand MR, Peng Y, Williams MM, Momin M, Damron FH, Dubey P, Tondella ML, Pawloski LC. Genome-based prediction of cross-protective, HLA-DR-presented epitopes as putative vaccine antigens for multiple Bordetella species. Microbiol Spectr 2024; 12:e0352723. [PMID: 38054724 PMCID: PMC10783135 DOI: 10.1128/spectrum.03527-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/07/2023] [Indexed: 12/07/2023] Open
Abstract
IMPORTANCE Pertussis, caused by Bordetella pertussis, can cause debilitating respiratory symptoms, so whole-cell pertussis vaccines (wPVs) were introduced in the 1940s. However, reactogenicity of wPV necessitated the development of acellular pertussis vaccines (aPVs) that were introduced in the 1990s. Since then, until the COVID-19 pandemic began, reported pertussis incidence was increasing, suggesting that aPVs do not induce long-lasting immunity and may not effectively prevent transmission. Additionally, aPVs do not provide protection against other Bordetella species that are observed during outbreaks. The significance of this work is in determining potential new vaccine antigens for multiple Bordetella species that are predicted to elicit long-term immune responses. Genome-based approaches have aided the development of novel vaccines; here, these methods identified Bordetella vaccine candidates that may be cross-protective and predicted to induce strong memory responses. These targets can lead to an improved vaccine with a strong safety profile while also strengthening the longevity of the immune response.
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Affiliation(s)
- Muktha S. Natrajan
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Laboratory Leadership Service, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jesse M. Hall
- Department of Microbial Infection and Immunity, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Michael R. Weigand
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Yanhui Peng
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Margaret M. Williams
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Mohamed Momin
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Frederick Heath Damron
- Department of Microbiology, Immunology, and Cell Biology, West Virginia University, Morgantown, West Virginia, USA
| | - Purnima Dubey
- Department of Microbial Infection and Immunity, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Maria Lucia Tondella
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Lucia C. Pawloski
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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4
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Sun R, Qian MG, Zhang X. T and B cell epitope analysis for the immunogenicity evaluation and mitigation of antibody-based therapeutics. MAbs 2024; 16:2324836. [PMID: 38512798 PMCID: PMC10962608 DOI: 10.1080/19420862.2024.2324836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
The surge in the clinical use of therapeutic antibodies has reshaped the landscape of pharmaceutical therapy for many diseases, including rare and challenging conditions. However, the administration of exogenous biologics could potentially trigger unwanted immune responses such as generation of anti-drug antibodies (ADAs). Real-world experiences have illuminated the clear correlation between the ADA occurrence and unsatisfactory therapeutic outcomes as well as immune-related adverse events. By retrospectively examining research involving immunogenicity analysis, we noticed the growing emphasis on elucidating the immunogenic epitope profiles of antibody-based therapeutics aiming for mechanistic understanding the immunogenicity generation and, ideally, mitigating the risks. As such, we have comprehensively summarized here the progress in both experimental and computational methodologies for the characterization of T and B cell epitopes of therapeutics. Furthermore, the successful practice of epitope-driven deimmunization of biotherapeutics is exceptionally highlighted in this article.
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Affiliation(s)
- Ruoxuan Sun
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
| | - Mark G. Qian
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
| | - Xiaobin Zhang
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
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5
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Shah RK, Cygan E, Kozlik T, Colina A, Zamora AE. Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy. Front Immunol 2023; 14:1301100. [PMID: 38149253 PMCID: PMC10749952 DOI: 10.3389/fimmu.2023.1301100] [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: 09/24/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.
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Affiliation(s)
- Ravi K. Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Erin Cygan
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alfredo Colina
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Anthony E. Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
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6
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Matos AS, Invenção MDCV, Moura IAD, Freitas ACD, Batista MVDA. Immunoinformatics applications in the development of therapeutic vaccines against human papillomavirus-related infections and cervical cancer. Rev Med Virol 2023; 33:e2463. [PMID: 37291746 DOI: 10.1002/rmv.2463] [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: 03/15/2023] [Revised: 05/09/2023] [Accepted: 05/27/2023] [Indexed: 06/10/2023]
Abstract
The human papillomavirus (HPV) represents the most prevalent sexually transmitted infectious agent worldwide. HPV penetrates the epithelium through microlesions and establishes an infectious focus that can lead to the development of cervical cancer. Prophylactic HPV vaccines are available, but do not affect already-established infections. Using in silico prediction tools is a promising strategy for identifying and selecting vaccine candidate T cell epitopes. An advantage of this strategy is that epitopes can be selected according to the degree of conservation within a group of antigenic proteins. This makes achieving comprehensive genotypic coverage possible with a small set of epitopes. Therefore, this paper revises the general characteristics of HPV biology and the current knowledge on developing therapeutic peptide vaccines against HPV-related infections and cervical cancer.
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Affiliation(s)
- Alexandre Santos Matos
- Laboratory of Molecular Genetics and Biotechnology (GMBio), Department of Biology, Center for Biological and Health Sciences, Federal University of Sergipe, Sao Cristovao, Brazil
| | - Maria da Conceição Viana Invenção
- Laboratory of Molecular Studies and Experimental Therapy (LEMTE), Department of Genetics, Federal University of Pernambuco, Recife, Brazil
| | - Ingrid Andrêssa de Moura
- Laboratory of Molecular Studies and Experimental Therapy (LEMTE), Department of Genetics, Federal University of Pernambuco, Recife, Brazil
| | - Antonio Carlos de Freitas
- Laboratory of Molecular Studies and Experimental Therapy (LEMTE), Department of Genetics, Federal University of Pernambuco, Recife, Brazil
| | - Marcus Vinicius de Aragão Batista
- Laboratory of Molecular Genetics and Biotechnology (GMBio), Department of Biology, Center for Biological and Health Sciences, Federal University of Sergipe, Sao Cristovao, Brazil
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7
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Nilsson JB, Kaabinejadian S, Yari H, Peters B, Barra C, Gragert L, Hildebrand W, Nielsen M. Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome. Commun Biol 2023; 6:442. [PMID: 37085710 PMCID: PMC10121683 DOI: 10.1038/s42003-023-04749-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/23/2023] [Indexed: 04/23/2023] Open
Abstract
Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .
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Affiliation(s)
| | - Saghar Kaabinejadian
- Pure MHC, LLC, Oklahoma City, OK, USA
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hooman Yari
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, California, USA
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, DK-2800, Lyngby, Denmark
| | - Loren Gragert
- Department of Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - William Hildebrand
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800, Lyngby, Denmark.
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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: 13] [Impact Index Per Article: 13.0] [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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Charles T, Moss DL, Bhat P, Moore PW, Kummer NA, Bhattacharya A, Landry SJ, Mettu RR. CD4+ T-Cell Epitope Prediction by Combined Analysis of Antigen Conformational Flexibility and Peptide-MHCII Binding Affinity. Biochemistry 2022; 61:1585-1599. [PMID: 35834502 PMCID: PMC9352311 DOI: 10.1021/acs.biochem.2c00237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Antigen processing in the class II MHC pathway depends
on conventional
proteolytic enzymes, potentially acting on antigens in native-like
conformational states. CD4+ epitope dominance arises from a competition
among antigen folding, proteolysis, and MHCII binding. Protease-sensitive
sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped
in plague vaccine candidate F1-V to evaluate the various contributions
to CD4+ epitope dominance. Using X-ray crystal structures, antigen
processing likelihood (APL) predicts CD4+ epitopes with significant
accuracy for F1-V without considering peptide-MHCII binding affinity.
We also show that APL achieves excellent performance over two benchmark
antigen sets. The profiles of conformational flexibility derived from
the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV,
were similar to the biochemical profiles of linear antibody epitope
reactivity and protease sensitivity, suggesting that the role of structure
in proteolysis was captured by the analysis of the crystal structures.
The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and
BALB/c mice were compared to epitope predictions based on APL, MHCII
binding, or both. For a sample of 13 diverse antigens, the accuracy
of epitope prediction by the combination of APL and I-Ab-MHCII-peptide affinity reached 36%. When MHCII allele specificity
was also diverse, such as in human immunity, prediction of dominant
epitopes by APL alone reached 42% when using a stringent scoring threshold.
Because dominant CD4+ epitopes tend to occur in conformationally stable
antigen domains, crystal structures typically are available for analysis
by APL, and thus, the requirement for a crystal structure is not a
severe limitation.
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Affiliation(s)
- Tysheena Charles
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Daniel L Moss
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Pawan Bhat
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Peyton W Moore
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Nicholas A Kummer
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Avik Bhattacharya
- Department of Computer Science, Tulane University, New Orleans, Louisiana 70118, United States
| | - Samuel J Landry
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Ramgopal R Mettu
- Department of Computer Science, Tulane University, New Orleans, Louisiana 70118, United States
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10
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Araf Y, Moin AT, Timofeev VI, Faruqui NA, Saiara SA, Ahmed N, Parvez MSA, Rahaman TI, Sarkar B, Ullah MA, Hosen MJ, Zheng C. Immunoinformatic Design of a Multivalent Peptide Vaccine Against Mucormycosis: Targeting FTR1 Protein of Major Causative Fungi. Front Immunol 2022; 13:863234. [PMID: 35720422 PMCID: PMC9204303 DOI: 10.3389/fimmu.2022.863234] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/12/2022] [Indexed: 12/14/2022] Open
Abstract
Mucormycosis is a potentially fatal illness that arises in immunocompromised people due to diabetic ketoacidosis, neutropenia, organ transplantation, and elevated serum levels of accessible iron. The sudden spread of mucormycosis in COVID-19 patients engendered massive concern worldwide. Comorbidities including diabetes, cancer, steroid-based medications, long-term ventilation, and increased ferritin serum concentration in COVID-19 patients trigger favorable fungi growth that in turn effectuate mucormycosis. The necessity of FTR1 gene-encoded ferrous permease for host iron acquisition by fungi has been found in different studies recently. Thus, targeting the transit component could be a potential solution. Unfortunately, no appropriate antifungal vaccine has been constructed as of yet. To date, mucormycosis has been treated with antiviral therapy and surgical treatment only. Thus, in this study, the FTR1 protein has been targeted to design a convenient and novel epitope-based vaccine with the help of immunoinformatics against four different virulent fungal species. Furthermore, the vaccine was constructed using 8 CTL, 2 HTL, and 1 LBL epitopes that were found to be highly antigenic, non-allergenic, non-toxic, and fully conserved among the fungi under consideration. The vaccine has very reassuring stability due to its high pI value of 9.97, conclusive of a basic range. The vaccine was then subjected to molecular docking, molecular dynamics, and immune simulation studies to confirm the biological environment’s safety, efficacy, and stability. The vaccine constructs were found to be safe in addition to being effective. Finally, we used in-silico cloning to develop an effective strategy for vaccine mass production. The designed vaccine will be a potential therapeutic not only to control mucormycosis in COVID-19 patients but also be effective in general mucormycosis events. However, further in vitro, and in vivo testing is needed to confirm the vaccine’s safety and efficacy in controlling fungal infections. If successful, this vaccine could provide a low-cost and effective method of preventing the spread of mucormycosis worldwide.
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Affiliation(s)
- Yusha Araf
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh.,Department of Immunology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh
| | - Abu Tayab Moin
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Vladimir I Timofeev
- Shubnikov Institute of Crystallography, Federal Scientific Research Centre, Crystallography and Photonics, Russian Academy of Sciences, Moscow, Russia
| | - Nairita Ahsan Faruqui
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Biotechnology Program, Department of Mathematics and Natural Sciences, School of Data and Sciences, Brac University, Dhaka, Bangladesh
| | - Syeda Afra Saiara
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh
| | - Nafisa Ahmed
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Biotechnology Program, Department of Mathematics and Natural Sciences, School of Data and Sciences, Brac University, Dhaka, Bangladesh
| | - Md Sorwer Alam Parvez
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh.,Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tanjim Ishraq Rahaman
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, Faculty of Life Sciences, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh
| | - Bishajit Sarkar
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Md Asad Ullah
- Department of Research and Development, Community of Biotechnology, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Mohammad Jakir Hosen
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Chunfu Zheng
- Department of Immunology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada
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11
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Nielsen M, Ternette N, Barra C. The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome. Expert Rev Proteomics 2022; 19:77-88. [PMID: 35390265 DOI: 10.1080/14789450.2022.2064278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data. AREAS COVERED Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches. EXPERT OPINION Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.
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Affiliation(s)
- Morten Nielsen
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Nicola Ternette
- Centre for Cellular and Molecular Physiology, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Carolina Barra
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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12
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Saylor K, Donnan B, Zhang C. Computational mining of MHC class II epitopes for the development of universal immunogenic proteins. PLoS One 2022; 17:e0265644. [PMID: 35349604 PMCID: PMC8963548 DOI: 10.1371/journal.pone.0265644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/04/2022] [Indexed: 11/21/2022] Open
Abstract
The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts.
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Affiliation(s)
- Kyle Saylor
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Ben Donnan
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Chenming Zhang
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
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13
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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14
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Yu ED, Wang E, Garrigan E, Goodwin B, Sutherland A, Tarke A, Chang J, Gálvez RI, Mateus J, Ramirez SI, Rawlings SA, Smith DM, Filaci G, Frazier A, Weiskopf D, Dan JM, Crotty S, Grifoni A, Sette A, da Silva Antunes R. Development of a T cell-based immunodiagnostic system to effectively distinguish SARS-CoV-2 infection and COVID-19 vaccination status. Cell Host Microbe 2022; 30:388-399.e3. [PMID: 35172129 PMCID: PMC8824221 DOI: 10.1016/j.chom.2022.02.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/16/2022] [Accepted: 02/02/2022] [Indexed: 11/18/2022]
Abstract
Both SARS-CoV-2 infections and COVID-19 vaccines elicit memory T cell responses. Here, we report the development of 2 pools of experimentally defined SARS-CoV-2 T cell epitopes that, in combination with spike, were used to discriminate 4 groups of subjects with different SARS-CoV-2 infection and COVID-19 vaccine status. The overall T cell-based classification accuracy was 89.2% and 88.5% in the experimental and validation cohorts. This scheme was applicable to different mRNA vaccines and different lengths of time post infection/post vaccination and yielded increased accuracy when compared to serological readouts. T cell responses from breakthrough infections were also studied and effectively segregated from vaccine responses, with a combined performance of 86.6% across all 239 subjects from the 5 groups. We anticipate that a T cell-based immunodiagnostic scheme to classify subjects based on their vaccination and natural infection history will be an important tool for longitudinal monitoring of vaccinations and for establishing SARS-CoV-2 correlates of protection.
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Affiliation(s)
- Esther Dawen Yu
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Eric Wang
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Emily Garrigan
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Benjamin Goodwin
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Aaron Sutherland
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Alison Tarke
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Internal Medicine and Center of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa 16132, Italy
| | - James Chang
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Rosa Isela Gálvez
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Jose Mateus
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Sydney I Ramirez
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA
| | - Stephen A Rawlings
- Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA
| | - Davey M Smith
- Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA
| | - Gilberto Filaci
- Department of Internal Medicine and Center of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa 16132, Italy; Bioterapy Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - April Frazier
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Daniela Weiskopf
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Jennifer M Dan
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA
| | - Shane Crotty
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA
| | - Alba Grifoni
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA UC92037, USA.
| | - Ricardo da Silva Antunes
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
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15
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Perez MAS, Cuendet MA, Röhrig UF, Michielin O, Zoete V. Structural Prediction of Peptide-MHC Binding Modes. Methods Mol Biol 2022; 2405:245-282. [PMID: 35298818 DOI: 10.1007/978-1-0716-1855-4_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.
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Affiliation(s)
- Marta A S Perez
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michel A Cuendet
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland
| | - Ute F Röhrig
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olivier Michielin
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland.
| | - Vincent Zoete
- Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, Lausanne, Switzerland.
- Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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16
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Santos SP, Lisboa AB, Silva FS, Tiwari S, Azevedo V, Cruz ÁA, Silva ES, Pinheiro CS, Alcantara-Neves NM, Pacheco LG. Rationally designed hypoallergenic mutant variants of the house dust mite allergen Der p 21. Biochim Biophys Acta Gen Subj 2022; 1866:130096. [DOI: 10.1016/j.bbagen.2022.130096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/22/2021] [Accepted: 01/17/2022] [Indexed: 11/29/2022]
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17
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Alburquerque-González B, López-Abellán MD, Luengo-Gil G, Montoro-García S, Conesa-Zamora P. Design of Personalized Neoantigen RNA Vaccines Against Cancer Based on Next-Generation Sequencing Data. Methods Mol Biol 2022; 2547:165-185. [PMID: 36068464 DOI: 10.1007/978-1-0716-2573-6_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The good clinical results of immune checkpoint inhibitors (ICIs) in recent cancer therapy and the success of RNA vaccines against SARS-nCoV2 have provided important lessons to the scientific community. On the one hand, the efficacy of ICI depends on the number and immunogenicity of tumor neoantigens (TNAs) which unfortunately are not abundantly expressed in many cancer subtypes. On the other hand, novel RNA vaccines have significantly improved both the stability and immunogenicity of mRNA and its efficient delivery, this way overcoming past technique limitations and also allowing a quick vaccine development at the same time. These two facts together have triggered a resurgence of therapeutic cancer vaccines which can be designed to include individual TNAs and be synthesized in a timeframe short enough to be suitable for the tailored treatment of a given cancer patient.In this chapter, we explain the pipeline for the synthesis of TNA-carrying RNA vaccines which encompasses several steps such as individual tumor next-generation sequencing (NGS), selection of immunogenic TNAs, nucleic acid synthesis, drug delivery systems, and immunogenicity assessment, all of each step comprising different alternatives and variations which will be discussed.
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Affiliation(s)
- Begoña Alburquerque-González
- Pathology and Histology Department Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain
| | - María Dolores López-Abellán
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain
| | - Ginés Luengo-Gil
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain
| | - Silvia Montoro-García
- Cell Culture Lab, Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain
| | - Pablo Conesa-Zamora
- Pathology and Histology Department Facultad de Ciencias de la Salud, UCAM Universidad Católica San Antonio de Murcia, Murcia, Spain.
- Laboratory Medicine Department, Group of Molecular Pathology and Pharmacogenetics, Biomedical Research Institute from Murcia (IMIB), Hospital Universitario Santa Lucía, Cartagena, Spain.
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18
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Fotakis G, Trajanoski Z, Rieder D. Computational cancer neoantigen prediction: current status and recent advances. IMMUNO-ONCOLOGY TECHNOLOGY 2021; 12:100052. [PMID: 35755950 PMCID: PMC9216660 DOI: 10.1016/j.iotech.2021.100052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them. Tumors have the ability to acquire immune escape mechanisms. Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system. The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research. A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.
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Affiliation(s)
- G Fotakis
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Z Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - D Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
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19
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Verdon DJ, Jenkins MR. Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy. Cancers (Basel) 2021; 13:4245. [PMID: 34439399 PMCID: PMC8391927 DOI: 10.3390/cancers13164245] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/19/2021] [Accepted: 08/19/2021] [Indexed: 12/30/2022] Open
Abstract
In recent decades, adoptive cell transfer and checkpoint blockade therapies have revolutionized immunotherapeutic approaches to cancer treatment. Advances in whole exome/genome sequencing and bioinformatic detection of tumour-specific genetic variations and the amino acid sequence alterations they induce have revealed that T cell mediated anti-tumour immunity is substantially directed at mutated peptide sequences, and the identification and therapeutic targeting of patient-specific mutated peptide antigens now represents an exciting and rapidly progressing frontier of personalized medicine in the treatment of cancer. This review outlines the historical identification and validation of mutated peptide neoantigens as a target of the immune system, and the technical development of bioinformatic and experimental strategies for detecting, confirming and prioritizing both patient-specific or "private" and frequently occurring, shared "public" neoantigenic targets. Further, we examine the range of therapeutic modalities that have demonstrated preclinical and clinical anti-tumour efficacy through specifically targeting neoantigens, including adoptive T cell transfer, checkpoint blockade and neoantigen vaccination.
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Affiliation(s)
- Daniel J. Verdon
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia;
| | - Misty R. Jenkins
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia;
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3052, Australia
- La Trobe Institute of Molecular Science, La Trobe University, Bundoora, VIC 3086, Australia
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20
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Grifoni A, Sidney J, Vita R, Peters B, Crotty S, Weiskopf D, Sette A. SARS-CoV-2 human T cell epitopes: Adaptive immune response against COVID-19. Cell Host Microbe 2021; 29:1076-1092. [PMID: 34237248 PMCID: PMC8139264 DOI: 10.1016/j.chom.2021.05.010] [Citation(s) in RCA: 192] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/23/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023]
Abstract
Over the past year, numerous studies in the peer reviewed and preprint literature have reported on the virological, epidemiological and clinical characteristics of the coronavirus, SARS-CoV-2. To date, 25 studies have investigated and identified SARS-CoV-2-derived T cell epitopes in humans. Here, we review these recent studies, how they were performed, and their findings. We review how epitopes identified throughout the SARS-CoV2 proteome reveal significant correlation between number of epitopes defined and size of the antigen provenance. We also report additional analysis of SARS-CoV-2 human CD4 and CD8 T cell epitope data compiled from these studies, identifying 1,400 different reported SARS-CoV-2 epitopes and revealing discrete immunodominant regions of the virus and epitopes that are more prevalently recognized. This remarkable breadth of epitope repertoire has implications for vaccine design, cross-reactivity, and immune escape by SARS-CoV-2 variants.
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Affiliation(s)
- Alba Grifoni
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - John Sidney
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Randi Vita
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA 92037, USA
| | - Shane Crotty
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA 92037, USA
| | - Daniela Weiskopf
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA 92037, USA.
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21
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Arega AM, Pattanaik KP, Nayak S, Mahapatra RK. Computational discovery and ex-vivo validation study of novel antigenic vaccine candidates against tuberculosis. Acta Trop 2021; 217:105870. [PMID: 33636152 DOI: 10.1016/j.actatropica.2021.105870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 01/27/2021] [Accepted: 02/19/2021] [Indexed: 12/26/2022]
Abstract
Tuberculosis (TB) is a complex infectious bacterial disease, which has evolved with highly successful mechanisms to interfere with host defenses and existing classes of antibiotics to resist eradication. The single obtainable TB vaccine, Bacille Calmette-Guerin (BCG) has failed to provide regular defense for respiratory TB in adults. In this study, a bioinformatics and immunoinformatics approach was applied on Mycobacterium tuberculosis (Mtb) H37Rv proteomes to discover the potential subunit vaccine candidates that elicit both tuberculosis-specific T-cells and B-cell immune response. A total of 4049 proteins of MtbH37RvMtbH37Rv were retrieved and subjected to in silico sequence-based analysis. Finally, five (P9WL69 (Rv2599), P9WIG1 (Rv0747), P9WLQ1 (Rv1987), O53608 (Rv0063), O06624 (Rv1566c)) novel putative proteins were selected. Among the five putative antigenic vaccine candidates, P9WL69 protein was selected for the ex-vivo validation study. The P9WL69 protein encoding gene was amplified and cloned on pET21b vector. The success of the recombinant clone (pET21b-RV2599) was confirmed by colony PCR, insert release test and sequencing. Furthermore, the identified epitopes of the P9WL69 protein were considered for in silico docking and molecular dynamics simulation study using Toll-like Receptors (TLRs) (TLR-2, TLR-4, TLR-9), Mannose receptor, and Myeloid differentiation 88 (MYD88) to understand their binding affinity towards the development of immunogenic vaccines against tuberculosis.
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Affiliation(s)
- Aregitu Mekuriaw Arega
- School of Biotechnology, KIIT Deemed to be University, Bhubaneswar, Odisha, India; National Veterinary Institute, Debre Zeit, Ethiopia
| | | | - Sasmita Nayak
- School of Biotechnology, KIIT Deemed to be University, Bhubaneswar, Odisha, India
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22
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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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23
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de Vrij N, Meysman P, Gielis S, Adriaensen W, Laukens K, Cuypers B. HLA-DRB1 Alleles Associated with Lower Leishmaniasis Susceptibility Share Common Amino Acid Polymorphisms and Epitope Binding Repertoires. Vaccines (Basel) 2021; 9:270. [PMID: 33803005 PMCID: PMC8002611 DOI: 10.3390/vaccines9030270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
Abstract
Susceptibility for leishmaniasis is largely dependent on host genetic and immune factors. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors for leishmaniasis, little is known regarding the mechanisms that underpin these associations. To better understand this underlying functionality, we first collected all known leishmaniasis-associated HLA variants in a thorough literature review. Next, we aligned and compared the protection- and risk-associated HLA-DRB1 allele sequences. This identified several amino acid polymorphisms that distinguish protection- from risk-associated HLA-DRB1 alleles. Subsequently, T cell epitope binding predictions were carried out across these alleles to map the impact of these polymorphisms on the epitope binding repertoires. For these predictions, we used epitopes derived from entire proteomes of multiple Leishmania species. Epitopes binding to protection-associated HLA-DRB1 alleles shared common binding core motifs, mapping to the identified HLA-DRB1 amino acid polymorphisms. These results strongly suggest that HLA polymorphism, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes. A set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, was prioritized for further epitope discovery in the search for novel subunit-based vaccines.
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Affiliation(s)
- Nicky de Vrij
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium; (N.d.V.); (P.M.); (S.G.)
- Department of Clinical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium;
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), University of Antwerp, 2020 Antwerp, Belgium
| | - Pieter Meysman
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium; (N.d.V.); (P.M.); (S.G.)
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), University of Antwerp, 2020 Antwerp, Belgium
| | - Sofie Gielis
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium; (N.d.V.); (P.M.); (S.G.)
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), University of Antwerp, 2020 Antwerp, Belgium
| | - Wim Adriaensen
- Department of Clinical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium;
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium; (N.d.V.); (P.M.); (S.G.)
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, 2020 Antwerp, Belgium
- Biomedical Informatics Network Antwerpen (Biomina), University of Antwerp, 2020 Antwerp, Belgium
| | - Bart Cuypers
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium; (N.d.V.); (P.M.); (S.G.)
- Biomedical Informatics Network Antwerpen (Biomina), University of Antwerp, 2020 Antwerp, Belgium
- Department of Biomedical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
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24
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Ferreira JA, Relvas-Santos M, Peixoto A, M N Silva A, Lara Santos L. Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:25-43. [PMID: 34118464 PMCID: PMC8498922 DOI: 10.1016/j.gpb.2021.03.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/25/2021] [Accepted: 03/01/2021] [Indexed: 12/24/2022]
Abstract
Molecular-assisted precision oncology gained tremendous ground with high-throughput next-generation sequencing (NGS), supported by robust bioinformatics. The quest for genomics-based cancer medicine set the foundations for improved patient stratification, while unveiling a wide array of neoantigens for immunotherapy. Upfront pre-clinical and clinical studies have successfully used tumor-specific peptides in vaccines with minimal off-target effects. However, the low mutational burden presented by many lesions challenges the generalization of these solutions, requiring the diversification of neoantigen sources. Oncoproteogenomics utilizing customized databases for protein annotation by mass spectrometry (MS) is a powerful tool toward this end. Expanding the concept toward exploring proteoforms originated from post-translational modifications (PTMs) will be decisive to improve molecular subtyping and provide potentially targetable functional nodes with increased cancer specificity. Walking through the path of systems biology, we highlight that alterations in protein glycosylation at the cell surface not only have functional impact on cancer progression and dissemination but also originate unique molecular fingerprints for targeted therapeutics. Moreover, we discuss the outstanding challenges required to accommodate glycoproteomics in oncoproteogenomics platforms. We envisage that such rationale may flag a rather neglected research field, generating novel paradigms for precision oncology and immunotherapy.
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Affiliation(s)
- José Alexandre Ferreira
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto 4200-072, Portugal; Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto 4050-313, Portugal; Porto Comprehensive Cancer Center (P.ccc), Porto 4200-072, Portugal.
| | - Marta Relvas-Santos
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto 4200-072, Portugal; Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto 4050-313, Portugal; REQUIMTE-LAQV, Department of Chemistry and Biochemistry, Faculty of Sciences of the University of Porto, Porto 4169-007, Portugal
| | - Andreia Peixoto
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto 4200-072, Portugal; Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto 4050-313, Portugal
| | - André M N Silva
- REQUIMTE-LAQV, Department of Chemistry and Biochemistry, Faculty of Sciences of the University of Porto, Porto 4169-007, Portugal
| | - Lúcio Lara Santos
- Experimental Pathology and Therapeutics Group, Portuguese Institute of Oncology, Porto 4200-072, Portugal; Institute of Biomedical Sciences Abel Salazar, University of Porto, Porto 4050-313, Portugal; Porto Comprehensive Cancer Center (P.ccc), Porto 4200-072, Portugal
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25
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Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases. CELL REPORTS MEDICINE 2021; 2:100204. [PMID: 33521695 PMCID: PMC7837622 DOI: 10.1016/j.xcrm.2021.100204] [Citation(s) in RCA: 322] [Impact Index Per Article: 107.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/17/2020] [Accepted: 01/20/2021] [Indexed: 12/19/2022]
Abstract
T cells are involved in control of SARS-CoV-2 infection. To establish the patterns of immunodominance of different SARS-CoV-2 antigens and precisely measure virus-specific CD4+ and CD8+ T cells, we study epitope-specific T cell responses of 99 convalescent coronavirus disease 2019 (COVID-19) cases. The SARS-CoV-2 proteome is probed using 1,925 peptides spanning the entire genome, ensuring an unbiased coverage of human leukocyte antigen (HLA) alleles for class II responses. For HLA class I, we study an additional 5,600 predicted binding epitopes for 28 prominent HLA class I alleles, accounting for wide global coverage. We identify several hundred HLA-restricted SARS-CoV-2-derived epitopes. Distinct patterns of immunodominance are observed, which differ for CD4+ T cells, CD8+ T cells, and antibodies. The class I and class II epitopes are combined into epitope megapools to facilitate identification and quantification of SARS-CoV-2-specific CD4+ and CD8+ T cells.
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26
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Tarke A, Sidney J, Kidd CK, Dan JM, Ramirez SI, Yu ED, Mateus J, da Silva Antunes R, Moore E, Rubiro P, Methot N, Phillips E, Mallal S, Frazier A, Rawlings SA, Greenbaum JA, Peters B, Smith DM, Crotty S, Weiskopf D, Grifoni A, Sette A. Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 33330869 DOI: 10.1101/2020.12.08.416750] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
T cells are involved in control of SARS-CoV-2 infection. To establish the patterns of immunodominance of different SARS-CoV-2 antigens, and precisely measure virus-specific CD4 + and CD8 + T cells, we studied epitope-specific T cell responses of approximately 100 convalescent COVID-19 cases. The SARS-CoV-2 proteome was probed using 1,925 peptides spanning the entire genome, ensuring an unbiased coverage of HLA alleles for class II responses. For HLA class I, we studied an additional 5,600 predicted binding epitopes for 28 prominent HLA class I alleles, accounting for wide global coverage. We identified several hundred HLA-restricted SARS-CoV-2-derived epitopes. Distinct patterns of immunodominance were observed, which differed for CD4 + T cells, CD8 + T cells, and antibodies. The class I and class II epitopes were combined into new epitope megapools to facilitate identification and quantification of SARS-CoV-2-specific CD4 + and CD8 + T cells.
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27
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The Landscape of Pseudomonas aeruginosa Membrane-Associated Proteins. Cells 2020; 9:cells9112421. [PMID: 33167383 PMCID: PMC7694347 DOI: 10.3390/cells9112421] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/29/2020] [Accepted: 11/01/2020] [Indexed: 12/15/2022] Open
Abstract
Background: Pseudomonas aeruginosa cell envelope-associated proteins play a relevant role in infection mechanisms. They can contribute to the antibiotic resistance of the bacterial cells and be involved in the interaction with host cells. Thus, studies contributing to elucidating these key molecular elements are of great importance to find alternative therapeutics. Methods: Proteins and peptides were extracted by different methods and analyzed by Multidimensional Protein Identification Technology (MudPIT) approach. Proteomic data were processed by Discoverer2.1 software and multivariate statistics, i.e., Linear Discriminant Analysis (LDA), while the Immune Epitope Database (IEDB) resources were used to predict antigenicity and immunogenicity of experimental identified peptides and proteins. Results: The combination of 29 MudPIT runs allowed the identification of 10,611 peptides and 2539 distinct proteins. Following application of extraction methods enriching specific protein domains, about 15% of total identified peptides were classified in trans inner-membrane, inner-membrane exposed, trans outer-membrane and outer-membrane exposed. In this scenario, nine outer membrane proteins (OprE, OprI, OprF, OprD, PagL, OprG, PA1053, PAL and PA0833) were predicted to be highly antigenic. Thus, they were further processed and epitopes target of T cells (MHC Class I and Class II) and B cells were predicted. Conclusion: The present study represents one of the widest characterizations of the P. aeruginosa membrane-associated proteome. The feasibility of our method may facilitates the investigation of other bacterial species whose envelope exposed protein domains are still unknown. Besides, the stepwise prioritization of proteome, by combining experimental proteomic data and reverse vaccinology, may be useful for reducing the number of proteins to be tested in vaccine development.
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28
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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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29
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Gopanenko AV, Kosobokova EN, Kosorukov VS. Main Strategies for the Identification of Neoantigens. Cancers (Basel) 2020; 12:E2879. [PMID: 33036391 PMCID: PMC7600129 DOI: 10.3390/cancers12102879] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/01/2020] [Accepted: 10/05/2020] [Indexed: 12/24/2022] Open
Abstract
Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.
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Affiliation(s)
| | | | - Vyacheslav S. Kosorukov
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, 115478 Moscow, Russia; (A.V.G.); (E.N.K.)
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30
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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: 842] [Impact Index Per Article: 210.5] [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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31
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Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H, Jespersen MC, Jurtz V, Andreatta M, Greenbaum JA, Marcatili P, Sette A, Nielsen M, Peters B. IEDB-AR: immune epitope database-analysis resource in 2019. Nucleic Acids Res 2020; 47:W502-W506. [PMID: 31114900 PMCID: PMC6602498 DOI: 10.1093/nar/gkz452] [Citation(s) in RCA: 210] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/01/2019] [Accepted: 05/10/2019] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Haeuk Kim
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | | | - Vanessa Jurtz
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Massimo Andreatta
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Jason A Greenbaum
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
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32
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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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Abstract
Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.
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Affiliation(s)
- Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark;
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
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34
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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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35
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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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36
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Gbedande K, Carpio VH, Stephens R. Using two phases of the CD4 T cell response to blood-stage murine malaria to understand regulation of systemic immunity and placental pathology in Plasmodium falciparum infection. Immunol Rev 2020; 293:88-114. [PMID: 31903675 PMCID: PMC7540220 DOI: 10.1111/imr.12835] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 12/08/2019] [Accepted: 12/09/2019] [Indexed: 02/06/2023]
Abstract
Plasmodium falciparum infection and malaria remain a risk for millions of children and pregnant women. Here, we seek to integrate knowledge of mouse and human T helper cell (Th) responses to blood-stage Plasmodium infection to understand their contribution to protection and pathology. Although there is no complete Th subset differentiation, the adaptive response occurs in two phases in non-lethal rodent Plasmodium infection, coordinated by Th cells. In short, cellular immune responses limit the peak of parasitemia during the first phase; in the second phase, humoral immunity from T cell-dependent germinal centers is critical for complete clearance of rapidly changing parasite. A strong IFN-γ response kills parasite, but an excess of TNF compared with regulatory cytokines (IL-10, TGF-β) can cause immunopathology. This common pathway for pathology is associated with anemia, cerebral malaria, and placental malaria. These two phases can be used to both understand how the host responds to rapidly growing parasite and how it attempts to control immunopathology and variation. This dual nature of T cell immunity to Plasmodium is discussed, with particular reference to the protective nature of the continuous generation of effector T cells, and the unique contribution of effector memory T cells.
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Affiliation(s)
- Komi Gbedande
- Division of Infectious Diseases, Department of Internal Medicine, University of Texas Medical Branch, Galveston, Texas
| | - Victor H Carpio
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, Texas
| | - Robin Stephens
- Division of Infectious Diseases, Department of Internal Medicine, University of Texas Medical Branch, Galveston, Texas
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, Texas
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37
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Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, Muftuoglu Y, Sworder BJ, Diehn M, Levy R, Davis MM, Elias JE, Altman RB, Alizadeh AA. Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol 2019; 37:1332-1343. [PMID: 31611695 PMCID: PMC7075463 DOI: 10.1038/s41587-019-0280-2] [Citation(s) in RCA: 187] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 09/09/2019] [Indexed: 12/21/2022]
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4+ T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.
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Affiliation(s)
- Binbin Chen
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Michael S Khodadoust
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Niclas Olsson
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Lisa E Wagar
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Ethan Fast
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Nash, Vaduz, Liechtenstein
| | - Chih Long Liu
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Yagmur Muftuoglu
- Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Brian J Sworder
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Maximilian Diehn
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Ronald Levy
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Mark M Davis
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Joshua E Elias
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.
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38
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Abelin JG, Harjanto D, Malloy M, Suri P, Colson T, Goulding SP, Creech AL, Serrano LR, Nasir G, Nasrullah Y, McGann CD, Velez D, Ting YS, Poran A, Rothenberg DA, Chhangawala S, Rubinsteyn A, Hammerbacher J, Gaynor RB, Fritsch EF, Greshock J, Oslund RC, Barthelme D, Addona TA, Arieta CM, Rooney MS. Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. Immunity 2019; 51:766-779.e17. [PMID: 31495665 DOI: 10.1016/j.immuni.2019.08.012] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/19/2019] [Accepted: 08/15/2019] [Indexed: 12/30/2022]
Abstract
Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Asaf Poran
- Neon Therapeutics, Cambridge, MA 02139, USA
| | | | | | - Alex Rubinsteyn
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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39
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Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL, Griffith M. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med 2019; 11:56. [PMID: 31462330 PMCID: PMC6714459 DOI: 10.1186/s13073-019-0666-2] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.
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Affiliation(s)
- Megan M Richters
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Huiming Xia
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Katie M Campbell
- Division of Hematology and Oncology, Medical Plaza Driveway, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - William E Gillanders
- Department of Surgery, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Obi L Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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40
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Bioinformatic methods for cancer neoantigen prediction. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 164:25-60. [PMID: 31383407 DOI: 10.1016/bs.pmbts.2019.06.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.
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41
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Garde C, Ramarathinam SH, Jappe EC, Nielsen M, Kringelum JV, Trolle T, Purcell AW. Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data. Immunogenetics 2019; 71:445-454. [PMID: 31183519 DOI: 10.1007/s00251-019-01122-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 05/31/2019] [Indexed: 01/17/2023]
Abstract
Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.
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Affiliation(s)
- Christian Garde
- Evaxion Biotech, Bredgade 34E, DK-1260, Copenhagen, Denmark.
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology & Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia
| | - Emma C Jappe
- Evaxion Biotech, Bredgade 34E, DK-1260, Copenhagen, Denmark.,Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | | | - Thomas Trolle
- Evaxion Biotech, Bredgade 34E, DK-1260, Copenhagen, Denmark
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology & Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia.
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42
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Targeting the MHC Ligandome by Use of TCR-Like Antibodies. Antibodies (Basel) 2019; 8:antib8020032. [PMID: 31544838 PMCID: PMC6640717 DOI: 10.3390/antib8020032] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/06/2019] [Accepted: 05/07/2019] [Indexed: 12/11/2022] Open
Abstract
Monoclonal antibodies (mAbs) are valuable as research reagents, in diagnosis and in therapy. Their high specificity, the ease in production, favorable biophysical properties and the opportunity to engineer different properties make mAbs a versatile class of biologics. mAbs targeting peptide–major histocompatibility molecule (pMHC) complexes are often referred to as “TCR-like” mAbs, as pMHC complexes are generally recognized by T-cell receptors (TCRs). Presentation of self- and non-self-derived peptide fragments on MHC molecules and subsequent activation of T cells dictate immune responses in health and disease. This includes responses to infectious agents or cancer but also aberrant responses against harmless self-peptides in autoimmune diseases. The ability of TCR-like mAbs to target specific peptides presented on MHC allows for their use to study peptide presentation or for diagnosis and therapy. This extends the scope of conventional mAbs, which are generally limited to cell-surface or soluble antigens. Herein, we review the strategies used to generate TCR-like mAbs and provide a structural comparison with the analogous TCR in pMHC binding. We further discuss their applications as research tools and therapeutic reagents in preclinical models as well as challenges and limitations associated with their use.
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