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Jian F, Cai H, Chen Q, Pan X, Feng W, Yuan Y. OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction. Front Immunol 2025; 16:1550252. [PMID: 40092998 PMCID: PMC11906482 DOI: 10.3389/fimmu.2025.1550252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Accepted: 02/03/2025] [Indexed: 03/19/2025] Open
Abstract
The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and II molecules. By integrating large-scale mass spectrometry data and other relevant data types, we present a prediction model OnmiMHC based on deep learning. We rigorously assessed its performance using an independent test set, OnmiMHC achieves a PR-AUC score of 0.854 and a TOP20%-PPV of 0.934 in the MHC-I task, which outperforms existing methods. Likewise, in the domain of MHC-II prediction, our model OnmiMHC exhibits a PR-AUC score of 0.606 and a TOP20%-PPV of 0.690, outperforming other baseline methods. These results demonstrate the superiority of our model OnmiMHC in accurately predicting peptide-MHC binding affinities across both MHC-I and MHC-II molecules. With its superior accuracy and predictive capability, our model not only excels in general predictive tasks but also achieves significant results in the prediction of neoantigens for specific cancer types. Particularly for Uterine Corpus Endometrial Carcinoma (UCEC), our model has successfully predicted neoantigens with a high binding probability to common human alleles. This discovery is of great significance for the development of personalized tumor vaccines targeting UCEC.
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Affiliation(s)
- Fangfang Jian
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Weiwei Feng
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Yuan
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
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2
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Chou RT, Ouattara A, Takala-Harrison S, Cummings MP. Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data. NPJ Syst Biol Appl 2024; 10:133. [PMID: 39537634 PMCID: PMC11561111 DOI: 10.1038/s41540-024-00465-y] [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: 12/03/2023] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.
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Affiliation(s)
- Renee Ti Chou
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Amed Ouattara
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shannon Takala-Harrison
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Michael P Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
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3
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Meshram R, Kolte B, Gacche R. Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept. Vaccine 2024; 42:126106. [PMID: 38971664 DOI: 10.1016/j.vaccine.2024.07.007] [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: 03/14/2024] [Revised: 06/09/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024]
Abstract
The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.
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Affiliation(s)
- Rohan Meshram
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Baban Kolte
- Department of Biotechnology, Savitribai Phule Pune University, Pune 411007, India; Department of Microbial Genome Research, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures, Braunschweig 38124, Germany; Institute of Microbiology, Technical University of Braunschweig, Braunschweig 38106, Germany
| | - Rajesh Gacche
- Department of Biotechnology, Savitribai Phule Pune University, Pune 411007, India.
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Hashempour A, Khodadad N, Akbarinia S, Ghasabi F, Ghasemi Y, Nazar MMKA, Falahi S. Reverse vaccinology approaches to design a potent multiepitope vaccine against the HIV whole genome: immunoinformatic, bioinformatics, and molecular dynamics approaches. BMC Infect Dis 2024; 24:873. [PMID: 39198721 PMCID: PMC11360854 DOI: 10.1186/s12879-024-09775-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024] Open
Abstract
Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico.
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Affiliation(s)
- Ava Hashempour
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nastaran Khodadad
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Shokufeh Akbarinia
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farzane Ghasabi
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Younes Ghasemi
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Shahab Falahi
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
- Zoonotic Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran.
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Deng Q, Wang Z, Xiang S, Wang Q, Liu Y, Hou T, Sun H. RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning. J Chem Inf Model 2024; 64:6432-6449. [PMID: 39118363 DOI: 10.1021/acs.jcim.4c01153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.
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Affiliation(s)
- Qirui Deng
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, P. R. China
| | - Sutong Xiang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Qinghua Wang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
| | - Yifei Liu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009 Jiangsu, P. R. China
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Kuri PR, Goswami P. Reverse vaccinology-based multi-epitope vaccine design against Indian group A rotavirus targeting VP7, VP4, and VP6 proteins. Microb Pathog 2024; 193:106775. [PMID: 38960216 DOI: 10.1016/j.micpath.2024.106775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/25/2024] [Accepted: 06/30/2024] [Indexed: 07/05/2024]
Abstract
Rotavirus, a primary contributor to severe cases of infantile gastroenteritis on a global scale, results in significant morbidity and mortality in the under-five population, particularly in middle to low-income countries, including India. WHO-approved live-attenuated vaccines are linked to a heightened susceptibility to intussusception and exhibit low efficacy, primarily attributed to the high genetic diversity of rotavirus, varying over time and across different geographic regions. Herein, molecular data on Indian rotavirus A (RVA) has been reviewed through phylogenetic analysis, revealing G1P[8] to be the prevalent strain of RVA in India. The conserved capsid protein sequences of VP7, VP4 and VP6 were used to examine helper T lymphocyte, cytotoxic T lymphocyte and linear B-cell epitopes. Twenty epitopes were identified after evaluation of factors such as antigenicity, non-allergenicity, non-toxicity, and stability. These epitopes were then interconnected using suitable linkers and an N-terminal beta defensin adjuvant. The in silico designed vaccine exhibited structural stability and interactions with integrins (αvβ3 and αIIbβ3) and toll-like receptors (TLR2 and TLR4) indicated by docking and normal mode analyses. The immune simulation profile of the designed RVA multiepitope vaccine exhibited its potential to trigger humoral as well as cell-mediated immunity, indicating that it is a promising immunogen. These computational findings indicate potential efficacy of the designed vaccine against rotavirus infection.
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Affiliation(s)
- Pooja Rani Kuri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
| | - Pranab Goswami
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam, 781039, India.
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7
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Chou RT, Ouattara A, Adams M, Berry AA, Takala-Harrison S, Cummings MP. Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum. NPJ Syst Biol Appl 2024; 10:44. [PMID: 38678051 PMCID: PMC11055854 DOI: 10.1038/s41540-024-00365-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.
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Affiliation(s)
- Renee Ti Chou
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Amed Ouattara
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Matthew Adams
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrea A Berry
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shannon Takala-Harrison
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Michael P Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
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8
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Barra C, Nilsson JB, Saksager A, Carri I, Deleuran S, Garcia Alvarez HM, Høie MH, Li Y, Clifford JN, Wan YTR, Moreta LS, Nielsen M. In Silico Tools for Predicting Novel Epitopes. Methods Mol Biol 2024; 2813:245-280. [PMID: 38888783 DOI: 10.1007/978-1-0716-3890-3_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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Affiliation(s)
- Carolina Barra
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
| | | | - Astrid Saksager
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Sebastian Deleuran
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Magnus Haraldson Høie
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Yuchen Li
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | | | - Yat-Tsai Richie Wan
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Lys Sanz Moreta
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
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Safitri IA, Sugijo Y, Puspasari F, Masduki FF, Ihsanawati, Giri-Rachman EA, Artarini AA, Tan MI, Natalia D. Immunogenicity studies of recombinant RBD SARS-CoV-2 as a COVID-19 vaccine candidate produced in Escherichia coli. Vaccine X 2024; 16:100443. [PMID: 38304876 PMCID: PMC10832452 DOI: 10.1016/j.jvacx.2024.100443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/03/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 -related global COVID-19 pandemic has been impacting millions of people since its outbreak in 2020. COVID-19 vaccination has proven highly efficient in reducing illness severity and preventing infection-related fatalities. The World Health Organization has granted emergency use approval to multiple, including protein subunit technology-based, COVID-19 vaccines. Foreseeably, additional COVID-19 subunit vaccine development would be essential to meet the accessible and growing demand for effective vaccines, especially for Low-Middle-Income Countries (LMIC). The SARS-CoV-2 spike protein receptor binding domain (RBD), as the primary target for neutralizing antibodies, holds significant potential for future COVID-19 subunit vaccine development. In this study, we developed a recombinant Escherichia coli-expressed RBD (rRBD) as a vaccine candidate and evaluated its immunogenicity and preliminary toxicity in BALB/c mice. The rRBD induced humoral immune response from day 7 post-vaccination and, following the booster doses, the IgG levels increased dramatically in mice. Interestingly, our vaccine candidate also significantly induced cellular immune response, indicated by the incrased IFN-ɣ-producing cell numbers. We observed no adverse effect or local reactogenicity either in control or treated mice. Taken together, our discoveries could potentially support efficient and cost-effective vaccine antigen production, from which LMICs could particularly benefit.
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Affiliation(s)
- Intan Aghniya Safitri
- Biology Department, School of Life Science and Technology, Bandung Institute of Technology, Bandung, Indonesia
| | - Yovin Sugijo
- Biochemistry Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Bandung, Indonesia
| | - Fernita Puspasari
- Biochemistry Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Bandung, Indonesia
| | - Fifi Fitriyah Masduki
- Biochemistry Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Bandung, Indonesia
- Bioscience and Biotechnology Research Centre, Bandung Institute of Technology, Bandung, Indonesia
| | - Ihsanawati
- Biochemistry Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Bandung, Indonesia
| | - Ernawati Arifin Giri-Rachman
- Biology Department, School of Life Science and Technology, Bandung Institute of Technology, Bandung, Indonesia
- Bioscience and Biotechnology Research Centre, Bandung Institute of Technology, Bandung, Indonesia
| | - Aluicia Anita Artarini
- Pharmaceutical Biotechnology Laboratory, Pharmaceutics Department, School of Pharmacy, Bandung Institute of Technology, Bandung, Indonesia
- Bioscience and Biotechnology Research Centre, Bandung Institute of Technology, Bandung, Indonesia
| | - Marselina Irasonia Tan
- Biology Department, School of Life Science and Technology, Bandung Institute of Technology, Bandung, Indonesia
- Bioscience and Biotechnology Research Centre, Bandung Institute of Technology, Bandung, Indonesia
| | - Dessy Natalia
- Biochemistry Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Bandung, Indonesia
- Bioscience and Biotechnology Research Centre, Bandung Institute of Technology, Bandung, Indonesia
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La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [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: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
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Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
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11
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Mistry S, Gouripeddi R, Facelli JC. Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology. J Autoimmun 2023; 140:103115. [PMID: 37774556 PMCID: PMC10965504 DOI: 10.1016/j.jaut.2023.103115] [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: 06/01/2023] [Revised: 07/28/2023] [Accepted: 09/14/2023] [Indexed: 10/01/2023]
Abstract
Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.
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Affiliation(s)
- Sejal Mistry
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA
| | - Ramkiran Gouripeddi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA
| | - Julio C Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA.
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Landry SJ, Mettu RR, Kolls JK, Aberle JH, Norton E, Zwezdaryk K, Robinson J. Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins. Biochemistry 2023; 62:2517-2529. [PMID: 37554055 PMCID: PMC10483696 DOI: 10.1021/acs.biochem.3c00335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/31/2023] [Indexed: 08/10/2023]
Abstract
Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.
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Affiliation(s)
- 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
| | - Jay K. Kolls
- John
W. Deming Department of Internal Medicine, Center for Translational
Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Judith H. Aberle
- Center
for Virology, Medical University of Vienna, 1090 Vienna, Austria
| | - Elizabeth Norton
- Department
of Microbiology & Immunology, Tulane
University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Kevin Zwezdaryk
- Department
of Microbiology & Immunology, Tulane
University School of Medicine, New Orleans, Louisiana 70112, United States
| | - James Robinson
- Department
of Pediatrics, Tulane University School
of Medicine, New Orleans, Louisiana 70112, United States
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13
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Hashemi N, Hao B, Ignatov M, Paschalidis IC, Vakili P, Vajda S, Kozakov D. Improved prediction of MHC-peptide binding using protein language models. FRONTIERS IN BIOINFORMATICS 2023; 3:1207380. [PMID: 37663788 PMCID: PMC10469926 DOI: 10.3389/fbinf.2023.1207380] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
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Affiliation(s)
- Nasser Hashemi
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Ioannis Ch. Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Pirooz Vakili
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Sandor Vajda
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Department of Chemistry, Boston University, Boston, MA, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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14
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Liu L, Yu W, Cai K, Ma S, Wang Y, Ma Y, Zhao H. Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach. Heliyon 2023; 9:e18623. [PMID: 37576287 PMCID: PMC10413060 DOI: 10.1016/j.heliyon.2023.e18623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/12/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Abstract
Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.
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Affiliation(s)
- Lu Liu
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Wanli Yu
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Kuojun Cai
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Siyuan Ma
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Yanfeng Wang
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
| | - Yuhui Ma
- Zhaosu Xiyu Horse Industry Co., Ltd. Zhaosu County 835699, Yili Prefecture, Xinjiang, China
| | - Hongqiong Zhao
- College of Veterinary Medicine, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
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15
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Teplensky MH, Evangelopoulos M, Dittmar JW, Forsyth CM, Sinegra AJ, Wang S, Mirkin CA. Multi-antigen spherical nucleic acid cancer vaccines. Nat Biomed Eng 2023; 7:911-927. [PMID: 36717738 PMCID: PMC10424220 DOI: 10.1038/s41551-022-01000-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/19/2022] [Indexed: 02/01/2023]
Abstract
Cancer vaccines must activate multiple immune cell types to be effective against aggressive tumours. Here we report the impact of the structural presentation of two antigenic peptides on immune responses at the transcriptomic, cellular and organismal levels. We used spherical nucleic acid (SNA) nanoparticles to investigate how the spatial distribution and placement of two antigen classes affect antigen processing, cytokine production and the induction of memory. Compared with single-antigen SNAs, a single dual-antigen SNA elicited a 30% increase in antigen-specific T cell activation and a two-fold increase in T cell proliferation. Antigen placement within dual-antigen SNAs altered the gene expression of T cells and tumour growth. Specifically, dual-antigen SNAs encapsulating antigens targeting helper T cells and with externally conjugated antigens targeting cytotoxic T cells elevated antitumour genetic pathways, stalling lymphoma tumours in mice. Additionally, when combined with the checkpoint inhibitor anti-programmed-cell-death protein-1 in a mouse model of melanoma, a specific antigen arrangement within dual-antigen SNAs suppressed tumour growth and increased the levels of circulating memory T cells. The structural design of multi-antigen vaccines substantially impacts their efficacy.
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Affiliation(s)
- Michelle H Teplensky
- Department of Chemistry and International Institute for Nanotechnology, Northwestern University, Evanston, IL, USA
| | | | - Jasper W Dittmar
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Connor M Forsyth
- Department of Chemistry and International Institute for Nanotechnology, Northwestern University, Evanston, IL, USA
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA
| | - Andrew J Sinegra
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Shuya Wang
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA
| | - Chad A Mirkin
- Department of Chemistry and International Institute for Nanotechnology, Northwestern University, Evanston, IL, USA.
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
- Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA.
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16
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Pungan D, Fan J, Dai G, Khatun MS, Dietrich ML, Zwezdaryk KJ, Robinson JE, Landry SJ, Kolls JK. Novel Pneumocystis Antigens for Seroprevalence Studies. J Fungi (Basel) 2023; 9:602. [PMID: 37367538 PMCID: PMC10300987 DOI: 10.3390/jof9060602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Pneumocystis jirovecii is the most common cause of fungal pneumonia in children under the age of 2 years. However, the inability to culture and propagate this organism has hampered the acquisition of a fungal genome as well as the development of recombinant antigens to conduct seroprevalence studies. In this study, we performed proteomics on Pneumocystis-infected mice and used the recent P. murina and P. jirovecii genomes to prioritize antigens for recombinant protein expression. We focused on a fungal glucanase due to its conservation among fungal species. We found evidence of maternal IgG to this antigen, followed by a nadir in pediatric samples between 1 and 3 months of age, followed by an increase in prevalence over time consistent with the known epidemiology of Pneumocystis exposure. Moreover, there was a strong concordance of anti-glucanase responses and IgG against another Pneumocystis antigen, PNEG_01454. Taken together, these antigens may be useful tools for Pneumocystis seroprevalence and seroconversion studies.
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Affiliation(s)
- Dora Pungan
- John W Deming Department of Internal Medicine, Center for Translational Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Jia Fan
- Department of Biochemistry, Center for Cellular & Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Guixiang Dai
- John W Deming Department of Internal Medicine, Center for Translational Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Mst Shamima Khatun
- John W Deming Department of Internal Medicine, Center for Translational Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Monika L. Dietrich
- Department of Pediatrics, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Kevin J. Zwezdaryk
- Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - James E. Robinson
- Department of Pediatrics, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Samuel J. Landry
- Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Jay K. Kolls
- John W Deming Department of Internal Medicine, Center for Translational Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Pediatrics, Tulane University School of Medicine, New Orleans, LA 70112, USA
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17
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Jahantigh HR, Shahbazi B, Gouklai H, Van der Weken H, Gharibi Z, Rezaei Z, Habibi M, Ahmadi K. Design peptide and multi-epitope protein vaccine candidates against monkeypox virus using reverse vaccinology approach: an in-silico study. J Biomol Struct Dyn 2023; 41:14398-14418. [PMID: 37154825 DOI: 10.1080/07391102.2023.2201850] [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: 10/10/2022] [Accepted: 02/11/2023] [Indexed: 05/10/2023]
Abstract
Monkeypox is a zoonotic virus that has recently affected different countries worldwide. On July 23, 2022, the WHO declared the outbreak of monkeypox as a public health emergency of international concern. Surveillance studies conducted in Central Africa in the 1980s and later during outbreaks in the same region showed smallpox vaccines to be clinically somewhat effective against Monkeypox virus. However, there is no specific vaccine against this virus. This research used bioinformatics techniques to establish a novel multi-epitope vaccine candidate against Monkeypox that can induce a strong immune response. Five well-known antigenic proteins (E8L, A30L, A35R, A29L, and B21R) of the virus were picked and assessed as possible immunogenic peptides. Two suitable peptide candidates were selected according to bio-informatics analysis. Based upon in silico evaluation, two multi-epitope vaccine candidates (ALALAR and ALAL) were built with rich-epitope domains consisting of high-ranking T and B-cell epitopes. After predicting and evaluating the 3D structure of the protein candidates, the most efficient 3D models were considered for docking studies with Toll-like receptor 4 (TLR4) and the HLA-A * 11:01, HLA-A*01:01, HLA-A*02:01, HLA-A*03:01, HLA-A*07:02, HLA-A*15:01, HLA-A*30:01 receptors. Subsequently, molecular dynamics (MD) simulation of up to 150 nanoseconds was employed to assess the durability of the interaction of the vaccine candidates with immune receptors. MD studies showed that M5-HLA-A*11:01, ALAL-TLR4, and ALALAR-TLR4 complexes were stable during simulation. Analysis of the in silico outcomes indicates that the M5 peptide and ALAL and ALALAR proteins may be suitable vaccine candidates against the Monkeypox virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hamid Reza Jahantigh
- Interdisciplinary Department of Medicine - Section of Occupational Medicine, University of Bari, Bari, Italy
- Animal Health and Zoonosis PhD Course, Department of Veterinary Medicine, University of Bari, Bari, Italy
| | - Behzad Shahbazi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Hamed Gouklai
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Hans Van der Weken
- Laboratory of Immunology, Faculty of Veterinary Medicine, Ghent University, Ghent, Belgium
| | - Zahra Gharibi
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Zahra Rezaei
- Professor Alborzi Clinical Microbiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mehri Habibi
- Department of Molecular Biology, Pasteur Institute of Iran, Pasteur Ave., Tehran, Iran
| | - Khadijeh Ahmadi
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
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18
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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19
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Zaib S, Rana N, Areeba, Hussain N, Alrbyawi H, Dera AA, Khan I, Khalid M, Khan A, Al-Harrasi A. Designing multi-epitope monkeypox virus-specific vaccine using immunoinformatics approach. J Infect Public Health 2023; 16:107-116. [PMID: 36508944 PMCID: PMC9724569 DOI: 10.1016/j.jiph.2022.11.033] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/15/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Monkeypox virus is an enveloped DNA virus that belongs to Poxviridae family. The virus is transmitted from rodents to primates via infected body fluids, skin lesions, and respiratory droplets. After being infected with virus, the patients experience fever, myalgia, maculopapular rash, and fluid-filled blisters. It is necessary to differentiate monkeypox virus from other poxviruses during diagnosis which can be appropriately envisioned via DNA analysis from swab samples. During small outbreaks, the virus is treated with therapies administered in other orthopoxviruses infections and does not have its own specific therapy and vaccine. Consequently, in this article, two potential peptides have been designed. METHODS For the purpose of designing a vaccine, protein sequences were retrieved followed by the prediction of B- and T-cell epitopes. Afterward, vaccine structures were predicted which were docked with toll-like receptors. The docked complexes were analyzed with iMODS. Moreover, vaccine constructs nucleotide sequences were optimized and expressed in silico. RESULTS COP-B7R vaccine construct (V1) has antigenicity score of 0.5400, instability index of 29.33, z-score of - 2.11-, and 42.11% GC content whereas COP-A44L vaccine construct (V2) has an antigenicity score of 0.7784, instability index of 23.33, z-score of - 0.61, and 48.63% GC content. It was also observed that COP-A44L can be expressed as a soluble protein in Escherichia coli as compared to COP-B7R which requires a different expression system. CONCLUSION The obtained results revealed that both vaccine constructs show satisfactory outcomes after in silico investigation and have significant potential to prevent the monkeypox virus. However, COP-A44L gave better results.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan.
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Areeba
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore 54590, Pakistan
| | - Nadia Hussain
- Department of Pharmaceutical Sciences, College of Pharmacy, Al Ain University, Al Ain, UAE; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, UAE
| | - Hamad Alrbyawi
- Pharmaceutics and Pharmaceutical Technology Department, College of Pharmacy, Taibah University, Medina 42353, Saudi Arabia
| | - Ayed A Dera
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom; Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Imtiaz Khan
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom.
| | - Mohammad Khalid
- Department of Pharmaceutics, College of Pharmacy, King Khalid University, Asir-Abha 61421, Saudi Arabia
| | - Ajmal Khan
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Oman
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa 616, Oman.
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20
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Dhanda SK, Malviya J, Gupta S. Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes. Brief Bioinform 2022; 23:6692551. [PMID: 36070623 DOI: 10.1093/bib/bbac382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Department of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee, USA-38015.,Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India
| | - Jitendra Malviya
- Department of Life Sciences and Biological Science, IES University Bhopal, India
| | - Sudheer Gupta
- NGS & Bioinformatics Division, 3B BlackBio Biotech India Ltd., 7-C, Industrial Area, Govindpura, Bhopal, India
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21
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Li V, Lee C, Yoo D, Cho S, Kim H. In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology. Genes Genomics 2022; 44:937-944. [PMID: 35665465 PMCID: PMC9166176 DOI: 10.1007/s13258-022-01255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/31/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 but it remains as a serious threat today. To reduce and prevent spread of the virus, multiple vaccines have been developed. Despite the efforts in developing vaccines, Omicron strain of the virus has recently been designated as a variant of concern (VOC) by the World Health Organization (WHO). OBJECTIVE To develop a vaccine candidate against Omicron strain (B.1.1.529, BA.1) of the SARS-CoV-19. METHODS We applied reverse vaccinology methods for BA.1 and BA.2 as the vaccine target and a control, respectively. First, we predicted MHC I, MHC II and B cell epitopes based on their viral genome sequences. Second, after estimation of antigenicity, allergenicity and toxicity, a vaccine construct was assembled and tested for physicochemical properties and solubility. Third, AlphaFold2, RaptorX and RoseTTAfold servers were used to predict secondary structures and 3D structures of the vaccine construct. Fourth, molecular docking analysis was performed to test binding of our construct with angiotensin converting enzyme 2 (ACE2). Lastly, we compared mutation profiles on the epitopes between BA.1, BA.2, and wild type to estimate the efficacy of the vaccine. RESULTS We collected a total of 10 MHC I, 9 MHC II and 5 B cell epitopes for the final vaccine construct for Omicron strain. All epitopes were predicted to be antigenic, non-allergenic and non-toxic. The construct was estimated to have proper stability and solubility. The best modelled tertiary structures were selected for molecular docking analysis with ACE2 receptor. CONCLUSIONS These results suggest the potential efficacy of our newly developed vaccine construct as a novel vaccine candidate against Omicron strain of the coronavirus.
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Affiliation(s)
- Vladimir Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Chul Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - DongAhn Yoo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | | | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
- eGnome, Seoul, Republic of Korea.
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22
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Trevizani R, Yan Z, Greenbaum JA, Sette A, Nielsen M, Peters B. A comprehensive analysis of the IEDB MHC class-I automated benchmark. Brief Bioinform 2022; 23:6632617. [PMID: 35794711 DOI: 10.1093/bib/bbac259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/27/2022] [Accepted: 06/05/2022] [Indexed: 11/12/2022] Open
Abstract
In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.
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Affiliation(s)
- Raphael Trevizani
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA.,Fiocruz Ceará, Fundação Oswaldo Cruz, Rua São José s/n, Precabura, Eusébio/CE, Brazil
| | - Zhen Yan
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, California 92037, USA
| | - Jason A Greenbaum
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, California 92037, USA
| | - 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
| | - 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
| | - 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
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23
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Naveed M, Ali U, Karobari MI, Ahmed N, Mohamed RN, Abullais SS, Kader MA, Marya A, Messina P, Scardina GA. A Vaccine Construction against COVID-19-Associated Mucormycosis Contrived with Immunoinformatics-Based Scavenging of Potential Mucoralean Epitopes. Vaccines (Basel) 2022; 10:664. [PMID: 35632420 PMCID: PMC9147184 DOI: 10.3390/vaccines10050664] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 01/09/2023] Open
Abstract
Mucormycosis is a group of infections, caused by multiple fungal species, which affect many human organs and is lethal in immunocompromised patients. During the COVID-19 pandemic, the current wave of mucormycosis is a challenge to medical professionals as its effects are multiplied because of the severity of COVID-19 infection. The variant of concern, Omicron, has been linked to fatal mucormycosis infections in the US and Asia. Consequently, current postdiagnostic treatments of mucormycosis have been rendered unsatisfactory. In this hour of need, a preinfection cure is needed that may prevent lethal infections in immunocompromised individuals. This study proposes a potential vaccine construct targeting mucor and rhizopus species responsible for mucormycosis infections, providing immunoprotection to immunocompromised patients. The vaccine construct, with an antigenicity score of 0.75 covering, on average, 92-98% of the world population, was designed using an immunoinformatics approach. Molecular interactions with major histocompatibility complex-1 (MHC-I), Toll-like receptors-2 (TLR2), and glucose-regulated protein 78 (GRP78), with scores of -896.0, -948.4, and -925.0, respectively, demonstrated its potential to bind with the human immune receptors. It elicited a strong predicted innate and adaptive immune response in the form of helper T (Th) cells, cytotoxic T (TC) cells, B cells, natural killer (NK) cells, and macrophages. The vaccine cloned in the pBR322 vector showed positive amplification, further solidifying its stability and potential. The proposed construct holds a promising approach as the first step towards an antimucormycosis vaccine and may contribute to minimizing postdiagnostic burdens and failures.
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Affiliation(s)
- Muhammad Naveed
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore 54000, Pakistan; (M.N.); (U.A.)
| | - Urooj Ali
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore 54000, Pakistan; (M.N.); (U.A.)
| | - Mohmed Isaqali Karobari
- Center for Transdisciplinary Research (CFTR), Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College, Saveetha University, Chennai 600077, India
- Department of Restorative Dentistry & Endodontics, Faculty of Dentistry, University of Puthisastra, Phnom Penh 12211, Cambodia
| | - Naveed Ahmed
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia;
| | - Roshan Noor Mohamed
- Department of Pediatric Dentistry, Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Shahabe Saquib Abullais
- Department of Periodontics and Community Dental Sciences, College of Dentistry, King Khalid University, Abha 61421, Saudi Arabia;
| | - Mohammed Abdul Kader
- Department Restorative Dental Science, College of Dentistry, King Khalid University, Abha 61421, Saudi Arabia;
| | - Anand Marya
- Department of Orthodontics, University of Puthisastra, Phnom Penh 12211, Cambodia;
| | - Pietro Messina
- Department of Surgical, Oncological and Stomatological Disciplines, University of Palermo, 90133 Palermo, Italy;
| | - Giuseppe Alessandro Scardina
- Department of Surgical, Oncological and Stomatological Disciplines, University of Palermo, 90133 Palermo, Italy;
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24
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Wang F, Wang H, Wang L, Lu H, Qiu S, Zang T, Zhang X, Hu Y. MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences. Brief Bioinform 2022; 23:6571528. [PMID: 35443027 DOI: 10.1093/bib/bbab595] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/14/2022] Open
Abstract
Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).
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Affiliation(s)
- Fuxu Wang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Haoyan Wang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Lizhuang Wang
- General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Haoyu Lu
- Center for Bioinformatics, school of life science and technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Shizheng Qiu
- Center for Bioinformatics, school of life science and technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Tianyi Zang
- Cisco Research, NLP team, California, United States
| | - Xinjun Zhang
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yang Hu
- Center for Bioinformatics, Faculty of computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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25
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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: 29] [Impact Index Per Article: 9.7] [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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26
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Michalik M, Djahanschiri B, Leo JC, Linke D. An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2412:45-71. [PMID: 34918241 DOI: 10.1007/978-1-0716-1892-9_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.
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Affiliation(s)
| | - Bardya Djahanschiri
- Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt, Germany
| | - Jack C Leo
- Department of Biosciences, Nottingham Trent University, Nottingham, UK
| | - Dirk Linke
- Department of Biosciences, University of Oslo, Oslo, Norway.
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27
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Ramchandani R, Hossenbaccus L, Ellis AK. Immunoregulatory T cell epitope peptides for the treatment of allergic disease. Immunotherapy 2021; 13:1283-1291. [PMID: 34558985 DOI: 10.2217/imt-2021-0133] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Allergic diseases are type 2 inflammatory reactions with an increasing worldwide prevalence, making the search for new therapeutic options pertinent. Allergen immunotherapy is the only disease-modifying approach for allergic rhinitis, though it can result in systemic reactions. Recently, peptide immunotherapy (PIT), involving T-cell epitope peptides that bind to major histocompatibility complexes, have been developed. It is speculated that they can induce T helper cell type 2 anergy, Treg cell upregulation or immune deviation. Promising results in cat dander, honeybee venom, Japanese cedar pollen, grass pollens, ragweed and house dust mite clinical trials have shown safety, efficacy and tolerability to PIT. Hence, PIT may hold the potential to change the treatment algorithm for allergic rhinitis.
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Affiliation(s)
- Rashi Ramchandani
- Department of Medicine, Queen's University, Kingston, ON, K7L 3N6, Canada.,Allergy Research Unit, Kingston Health Sciences Center - KGH Site, Kingston, on, K7L 2V7, Canada
| | - Lubnaa Hossenbaccus
- Department of Biomedical & Molecular Sciences, Queen's University, Kingston, ON, K7L 3N6, Canada.,Allergy Research Unit, Kingston Health Sciences Center - KGH Site, Kingston, on, K7L 2V7, Canada
| | - Anne K Ellis
- Department of Medicine, Queen's University, Kingston, ON, K7L 3N6, Canada.,Department of Biomedical & Molecular Sciences, Queen's University, Kingston, ON, K7L 3N6, Canada.,Allergy Research Unit, Kingston Health Sciences Center - KGH Site, Kingston, on, K7L 2V7, Canada
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28
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Cai X, Li JJ, Liu T, Brian O, Li J. Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design. Brief Funct Genomics 2021; 20:289-303. [PMID: 34089044 PMCID: PMC8194884 DOI: 10.1093/bfgp/elab027] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 12/15/2022] Open
Abstract
Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.
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Affiliation(s)
- Xinhui Cai
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Jiao Jiao Li
- School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Tao Liu
- School of Life Sciences, Faculty of Science, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Oliver Brian
- Children’s Cancer Institute Australia, University of New South Wales Sydney, Children’s Cancer Institute Australia, Randwick, Sydney, 2031, New South Wales, Australia
| | - Jinyan Li
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
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29
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Jiang L, Yu H, Li J, Tang J, Guo Y, Guo F. Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution. Brief Bioinform 2021; 22:6299205. [PMID: 34131696 DOI: 10.1093/bib/bbab216] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 01/04/2023] Open
Abstract
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.
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Affiliation(s)
- Limin Jiang
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Hui Yu
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jiawei Li
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- Department of Computer Science, University of South Carolina, SC, USA.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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30
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Adiga R. Benchmarking Datasets from Malaria Cytotoxic T-cell Epitopes Using Machine Learning Approach. Avicenna J Med Biotechnol 2021; 13:87-91. [PMID: 34012524 PMCID: PMC8112139 DOI: 10.18502/ajmb.v13i2.5527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Background: Epitope prediction remains a major challenge in malaria due to the unique parasite biology, in addition to rapidly evolving parasite sequence variation in Plasmodium species. Although several models for epitope prediction exist, they are not useful in Plasmodium specific epitope development. Hence, it was proposed to use machine learning based methods to develop a peptide sequence based epitope predictor specific for malaria. Methods: Model datasets were developed and performance was tested using various machine learning algorithms. Machine learning classifiers were trained on epitope data using sequence features and comparison of amino acid physicochemical properties was done to yield a valid prediction model. Results: The findings from the analysis reveal that the model developed using selected classifiers after preprocessing by Waikato Environment for Knowledge Analysis (WEKA) performed better than other methods. The datasets for benchmarks of performance are deposited in the repository https://github.com/githubramaadiga/epitope_dataset
. Conclusion: The study is the first in-silico study on benchmarking Plasmodium cytotoxic T cell epitope datasets using machine learning approach. The peptide based predictors have been used for the first time to classify cytotoxic T cell epitopes in malaria. Algorithms has been evaluated using real datasets from malaria to obtain the model.
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Affiliation(s)
- Rama Adiga
- Nitte (Deemed to be University), Nitte University Centre for Science Education & Research (NUCSER), Division of Bioinformatics and Computational Genomics, Deralakatte, Paneer Campus, Mangalore, India 575018
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31
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Jin J, Liu Z, Nasiri A, Cui Y, Louis SY, Zhang A, Zhao Y, Hu J. Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism. Proteins 2021; 89:866-883. [PMID: 33594723 DOI: 10.1002/prot.26065] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 01/28/2021] [Accepted: 02/08/2021] [Indexed: 11/06/2022]
Abstract
Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better prediction performance than other approaches. However, most of the top algorithms are neural networks based black box models. Here, we propose DeepAttentionPan, an improved pan-specific model, based on convolutional neural networks and attention mechanisms for more flexible, stable and interpretable MHC-I binding prediction. With the attention mechanism, our ensemble model consisting of 20 trained networks achieves high and more stabilized prediction performance. Extensive tests on IEDB's weekly benchmark dataset show that our method achieves state-of-the-art prediction performance on 21 test allele datasets. Analysis of the peptide positional attention weights learned by our model demonstrates its capability to capture critical binding positions of the peptides, which leads to mechanistic understanding of MHC-peptide binding with high alignment with experimentally verified results. Furthermore, we show that with transfer learning, our pan model can be fine-tuned for alleles with few samples to achieve additional performance improvement. DeepAttentionPan is freely available as an open-source software at https://github.com/jjin49/DeepAttentionPan.
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Affiliation(s)
- Jing Jin
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Zhonghao Liu
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Alireza Nasiri
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Yuxin Cui
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Stephen-Yves Louis
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Ansi Zhang
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Yong Zhao
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
| | - Jianjun Hu
- Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
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32
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Systematic auditing is essential to debiasing machine learning in biology. Commun Biol 2021; 4:183. [PMID: 33568741 PMCID: PMC7876113 DOI: 10.1038/s42003-021-01674-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/12/2020] [Indexed: 12/20/2022] Open
Abstract
Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications. Fatma-Elzahraa Eid et al. illustrate a principled approach for identifying biases that can inflate the performance of biological machine learning models. When applied to three biomedical prediction problems, they identify previously unrecognized biases and ultimately show that models are likely to learn primarily from data biases when there is insufficient learnable signal in the data.
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33
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Lim HX, Lim J, Poh CL. Identification and selection of immunodominant B and T cell epitopes for dengue multi-epitope-based vaccine. Med Microbiol Immunol 2021; 210:1-11. [PMID: 33515283 DOI: 10.1007/s00430-021-00700-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 01/08/2021] [Indexed: 12/27/2022]
Abstract
Dengue virus (DENV) comprises four serotypes (DENV1-4) which cause 390 million global infections with 500,000 hospitalizations and 25,000 fatalities annually. Currently, the only FDA approved DENV vaccine is the chimeric live-attenuated vaccine, Dengvaxia®, which is based on the yellow fever virus (YFV) genome that carries the prM and E genes of the respective DENV 1, 2, 3, and 4 serotypes. However, it has lower efficacies against serotypes DENV1 (51%) and DENV2 (34%) when compared with DENV3 (75%) and DENV4 (77%). The absence of T cell epitopes from non-structural (NS) and capsid (C) proteins of the yellow fever vaccine strain might have prevented Dengvaxia® to elicit robust cellular immune responses, as CD8+ T cell epitopes are mainly localized in the NS3 and NS5 regions. Multi-epitope-based peptide vaccines carrying CD4+, CD8+ T cell and B cell epitopes represent a novel approach to generate specific immune responses. Therefore, assessing and selecting epitopes that can induce robust B and T cell responses is a prerequisite for constructing an efficient multi-epitope peptide vaccine. Potent B and T cell epitopes can be identified by utilizing immunoinformatic analysis, but the immunogenicity of the epitopes have to be experimentally validated. In this review, we presented T cell epitopes that have been predicted by bioinformatic approaches as well as recent experimental validations of CD4+ and CD8+ T cell epitopes by ex-vivo stimulation of PBMCs with specific peptides. Immunoproteomic analysis could be utilized to uncover HLA-specific epitopes presented by DENV-infected cells. Based on various approaches, immunodominant epitopes capable of inducing strong immune responses could be selected and incorporated to form a universally applicable multi-epitope-based peptide dengue vaccine.
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Affiliation(s)
- Hui Xuan Lim
- Centre for Virus and Vaccine Research, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500, Kuala Lumpur, Selangor, Malaysia
| | - Jianhua Lim
- Centre for Virus and Vaccine Research, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500, Kuala Lumpur, Selangor, Malaysia
| | - Chit Laa Poh
- Centre for Virus and Vaccine Research, School of Medical and Life Sciences, Sunway University, Bandar Sunway, 47500, Kuala Lumpur, Selangor, Malaysia.
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34
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Amoroso A, Magistroni P, Vespasiano F, Bella A, Bellino S, Puoti F, Alizzi S, Vaisitti T, Boros S, Grossi PA, Trapani S, Lombardini L, Pezzotti P, Deaglio S, Brusaferro S, Cardillo M. HLA and AB0 Polymorphisms May Influence SARS-CoV-2 Infection and COVID-19 Severity. Transplantation 2021; 105:193-200. [PMID: 33141807 DOI: 10.1097/tp.0000000000003507] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND SARS-CoV-2 infection is heterogeneous in clinical presentation and disease evolution. To investigate whether immune response to the virus can be influenced by genetic factors, we compared HLA and AB0 frequencies in organ transplant recipients and waitlisted patients according to presence or absence of SARS-CoV-2 infection. METHODS A retrospective analysis was performed on an Italian cohort composed by transplanted and waitlisted patients in a January 2002 to March 2020 time frame. Data from this cohort were merged with the Italian registry of COVID+ subjects, evaluating infection status of transplanted and waitlisted patients. A total of 56 304 cases were studied with the aim of comparing HLA and AB0 frequencies according to the presence (n = 265, COVID+) or absence (n = 56 039, COVID-) of SARS-CoV-2 infection. RESULTS The cumulative incidence rate of COVID-19 was 0.112% in the Italian population and 0.462% in waitlisted/transplanted patients (OR = 4.2; 95% CI, 3.7-4.7; P < 0.0001). HLA-DRB1*08 was more frequent in COVID+ (9.7% and 5.2%: OR = 1.9, 95% CI, 1.2-3.1; P = 0.003; Pc = 0.036). In COVID+ patients, HLA-DRB1*08 was correlated to mortality (6.9% in living versus 17.5% in deceased: OR = 2.9, 95% CI, 1.15-7.21; P = 0.023). Peptide binding prediction analyses showed that these DRB1*08 alleles were unable to bind any of the viral peptides with high affinity. Finally, blood group A was more frequent in COVID+ (45.5%) than COVID- patients (39.0%; OR = 1.3; 95% CI, 1.02-1.66; P = 0.03). CONCLUSIONS Although preliminary, these results suggest that HLA antigens may influence SARS-CoV-2 infection and clinical evolution of COVID-19 and confirm that blood group A individuals are at greater risk of infection, providing clues on the spread of the disease and indications about infection prognosis and vaccination strategies.
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Affiliation(s)
- Antonio Amoroso
- Department of Medical Sciences, University of Turin, Turin, Italy
- Immunogenetics and Transplant Biology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Paola Magistroni
- Immunogenetics and Transplant Biology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | | | - Antonino Bella
- Department of Infectious Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Stefania Bellino
- Department of Infectious Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Francesca Puoti
- National Transplant Center, Istituto Superiore di Sanità, Roma, Italy
| | - Silvia Alizzi
- Immunogenetics and Transplant Biology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Tiziana Vaisitti
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Stefano Boros
- Department of Infectious Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Paolo Antonio Grossi
- Department of Medicine and Surgery, University of Insubria-ASST-Sette Laghi, Varese, Italy
| | - Silvia Trapani
- National Transplant Center, Istituto Superiore di Sanità, Roma, Italy
| | | | - Patrizio Pezzotti
- Department of Infectious Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Silvia Deaglio
- Department of Medical Sciences, University of Turin, Turin, Italy
- Immunogenetics and Transplant Biology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | | | - Massimo Cardillo
- National Transplant Center, Istituto Superiore di Sanità, Roma, Italy
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Rao AA, Madejska AA, Pfeil J, Paten B, Salama SR, Haussler D. ProTECT-Prediction of T-Cell Epitopes for Cancer Therapy. Front Immunol 2020; 11:483296. [PMID: 33244314 PMCID: PMC7683782 DOI: 10.3389/fimmu.2020.483296] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 10/13/2020] [Indexed: 12/21/2022] Open
Abstract
Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patient’s HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of potential immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC binding prediction, and ranking of final candidates. We demonstrate the scalability, efficiency, and utility of ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, identifying recurrent potential neoepitopes from TMPRSS2-ERG fusions, and from SNVs in SPOP. We also compare ProTECT with results from published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 min per sample (on average) when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect.
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Affiliation(s)
- Arjun A Rao
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.,Computational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Ada A Madejska
- Computational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United States.,Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, Santa Cruz, CA, United States
| | - Jacob Pfeil
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.,Computational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Benedict Paten
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.,Computational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Sofie R Salama
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States.,Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - David Haussler
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.,Computational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United States.,UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States.,Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
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Chauhan S, Kumar R, Khan N, Verma S, Sehgal R, Tripathi PK, Farooq U. Designing peptide-based vaccine candidates for Plasmodium falciparum erythrocyte binding antigen 175. Biologicals 2020; 67:42-48. [PMID: 32718776 DOI: 10.1016/j.biologicals.2020.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 11/30/2022] Open
Abstract
Plasmodium falciparum leads to a virulent form of malaria. Progress has been achieved in understanding the mechanisms involved in the malarial infection, still there is no effective vaccine to prevent severe infection. An effective vaccine against malaria should be one which can induce immune responses against multiple epitopes in the context of predominantly occurring HLA alleles. In this study, an integrated approach was employed to identify promiscuous peptides of a well-defined sequence of erythrocyte binding antigen-175 and promiscuous peptides for HLA alleles were designed using bioinformatics tools. A peptide with 15 amino acids (ILAIAIYESRILKRK) was selected based on its high binding affinity score and synthesized. This promiscuous peptide was used as stimulating antigen in lymphoproliferative responses to evaluate the cellular immune response. It was observed this peptide evokes lymphoproliferative and cytokine responses in individuals naturally exposed to the malaria parasite. The intensity of PBMCs proliferation was observed to be higher in sera obtained from P. falciparum exposed as compared to unexposed healthy individuals, suggesting earlier recognition of peptide of this region by T cells. Furthermore, the binding mode of HLA-peptide complex and their interaction may lead to a rational and selective peptide-based vaccine candidate design approach which can be used as a malaria prophylaxis.
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Affiliation(s)
- Shakti Chauhan
- Molecular and Immune-parasitology Laboratory, Shoolini University, Solan, India
| | - Rajender Kumar
- Department of Clinical Microbiology, Umeå University, SE-90185, Umeå, Sweden
| | - Nazam Khan
- Molecular and Immune-parasitology Laboratory, Shoolini University, Solan, India
| | - Swati Verma
- Department of Microbiology, Maharaja Ganga Singh University, Bikaner, India
| | - Rakesh Sehgal
- Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Umar Farooq
- Molecular and Immune-parasitology Laboratory, Shoolini University, Solan, India.
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Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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Affiliation(s)
- Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - 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
| | - Søren Buus
- Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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Paul S, Croft NP, Purcell AW, Tscharke DC, Sette A, Nielsen M, Peters B. Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system. PLoS Comput Biol 2020; 16:e1007757. [PMID: 32453790 PMCID: PMC7274474 DOI: 10.1371/journal.pcbi.1007757] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 06/05/2020] [Accepted: 03/02/2020] [Indexed: 12/13/2022] Open
Abstract
T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools. Computational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes identified in the context of H-2Db and H-2Kb. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.
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Affiliation(s)
- Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California, United States of America
| | - Nathan P. Croft
- Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia
| | - Anthony W. Purcell
- Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia
| | - David C. Tscharke
- John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California, United States of America
- Department of Medicine, University of California, San Diego, La Jolla, California, United States of America
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, DK Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP San Martín, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California, United States of America
- Department of Medicine, University of California, San Diego, La Jolla, California, United States of America
- * E-mail:
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da Silva ES, Aglas L, Pinheiro CS, de Andrade Belitardo EMM, Silveira EF, Huber S, Torres RT, Wallner M, Briza P, Lackner P, Laimer J, Pacheco LGC, Cruz ÁA, Alcântara-Neves NM, Ferreira F. A hybrid of two major Blomia tropicalis allergens as an allergy vaccine candidate. Clin Exp Allergy 2020; 50:835-847. [PMID: 32314444 PMCID: PMC7384089 DOI: 10.1111/cea.13611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/27/2020] [Accepted: 04/07/2020] [Indexed: 12/21/2022]
Abstract
Introduction Allergen‐specific immunotherapy (AIT) represents a curative approach for treating allergies. In the tropical and subtropical regions of the world, Blomia tropicalis (Blo t 5 and Blo t 21) is the likely dominant source of indoor allergens. Aim To generate a hypoallergenic Blo t 5/Blo t 21 hybrid molecule that can treat allergies caused by B tropicalis. Methods Using in silico design of B tropicalis hybrid proteins, we chose two hybrid proteins for heterologous expression. Wild‐type Blo t 5/Blo t 21 hybrid molecule and a hypoallergenic version, termed BTH1 and BTH2, respectively, were purified by ion exchange and size exclusion chromatography and characterized by physicochemical, as well as in vitro and in vivo immunological, experiments. Results BTH1, BTH2 and the parental allergens were purified to homogeneity and characterized in detail. BTH2 displayed the lowest IgE reactivity that induced basophil degranulation using sera from allergic rhinitis and asthmatic patients. BTH2 essentially presented the same endolysosomal degradation pattern as the shortened rBlo t 5 and showed a higher resistance towards degradation than the full‐length Blo t 5. In vivo immunization of mice with BTH2 led to the production of IgG antibodies that competed with human IgE for allergen binding. Stimulation of splenocytes from BTH2‐immunized mice produced higher levels of IL‐10 and decreased secretion of IL‐4 and IL‐5. In addition, BTH2 stimulated T‐cell proliferation in PBMCs isolated from allergic patients, with secretion of higher levels of IL‐10 and lower levels of IL‐5 and IL‐13, when compared to parental allergens. Conclusions and Clinical Relevance BTH2 is a promising hybrid vaccine candidate for immunotherapy of Blomia allergy. However, further pre‐clinical studies addressing its efficacy and safety are needed.
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Affiliation(s)
- Eduardo Santos da Silva
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil.,Programa de Pós-Graduação em Biotecnologia da Rede Nordeste de Biotecnologia (RENORBIO), Natal, Brazil.,Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Lorenz Aglas
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Carina Silva Pinheiro
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil
| | - Emília M M de Andrade Belitardo
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil.,Programa de Pós-Graduação em Imunologia da Universidade Federal da Bahia, Salvador, Brazil
| | - Elisânia Fontes Silveira
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil
| | - Sara Huber
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Rogério Tanan Torres
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil
| | - Michael Wallner
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Peter Briza
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Peter Lackner
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Josef Laimer
- Department of Biosciences, University of Salzburg, Salzburg, Austria
| | - Luis Gustavo C Pacheco
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil
| | - Álvaro A Cruz
- Núcleo de Excelência de Asma da, Universidade Federal da Bahia, Salvador, Brazil
| | - Neuza Maria Alcântara-Neves
- Laboratório de Alergia e Acarologia, Departamento de Ciências da Biointeração, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Brazil.,Programa de Pós-Graduação em Biotecnologia da Rede Nordeste de Biotecnologia (RENORBIO), Natal, Brazil.,Programa de Pós-Graduação em Imunologia da Universidade Federal da Bahia, Salvador, Brazil
| | - Fatima Ferreira
- Department of Biosciences, University of Salzburg, Salzburg, Austria
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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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Gong JJ, Margolis DJ, Monos DS. Predictive in silico binding algorithms reveal HLA specificities and autoallergen peptides associated with atopic dermatitis. Arch Dermatol Res 2020; 312:647-656. [PMID: 32152724 DOI: 10.1007/s00403-020-02059-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 02/26/2020] [Indexed: 12/18/2022]
Abstract
Atopic dermatitis (AD) is a skin disease that results from a combination of skin barrier dysfunction and immune dysregulation. The immune dysregulation is often associated with IgE sensitivity. There is also evidence that autoallergens Hom s 1, 2, 3, and 4 play a role in AD; it is possible that patients with specific HLA subtypes are predisposed to autoreactivity due to increased presentation of autoallergen peptides. The goal of our study was to use in silico epitope prediction platforms as an approach to identify HLA subtypes that may preferentially bind autoallergen peptides and are thus candidates for further study. Considering the previously described association of DRB1 alleles with AD and progression of disease, emphasis was placed on DRB1. Certain DRB1 alleles (08:04, 11:01, and 11:04) were identified by both algorithms to bind a significant percent of the generated autoallergen peptides. Conversely, autoallergen core peptide sequences FRQLSHRFH and IRAKLRLQA (Hom s 1), IRKSKNILF (Hom s 2), FKWVPVTDS and MAAIEKVRK (Hom s 3), and FRYFATLKV (Hom s 4) were predicted to bind many DRB1 alleles and, thus, may play a role in the pathogenesis of AD. Our findings provide candidate DRB1 alleles and autoallergen epitopes that will guide future studies exploring the relationship between DRB1 subtype and autoreactivity in AD. A similar approach can be used for any antigen that has been associated with an IgE response and AD.
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Affiliation(s)
- Jan J Gong
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David J Margolis
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. .,Department of Dermatology, University of Pennsylvania, Philadelphia, PA, USA. .,Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Dimitrios S Monos
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Ritari J, Hyvärinen K, Koskela S, Niittyvuopio R, Nihtinen A, Salmenniemi U, Putkonen M, Volin L, Kwan T, Pastinen T, Itälä-Remes M, Partanen J. Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease After Allogeneic HSCT. Front Immunol 2019; 10:1625. [PMID: 31379830 PMCID: PMC6646417 DOI: 10.3389/fimmu.2019.01625] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 07/01/2019] [Indexed: 12/20/2022] Open
Abstract
Genetic mismatches in protein coding genes between allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipient and donor can elicit an alloimmunity response via peptides presented by the recipient HLA receptors as minor histocompatibility antigens (mHAs). While the impact of individual mHAs on allo-HSCT outcome such as graft-vs.-host and graft-vs.-leukemia effects has been demonstrated, it is likely that established mHAs constitute only a small fraction of all immunogenic non-synonymous variants. In the present study, we have analyzed the genetic mismatching in 157 exome-sequenced sibling allo-HSCT pairs to evaluate the significance of polymorphic HLA class I associated peptides on clinical outcome. We applied computational mismatch estimation approaches based on experimentally verified HLA ligands available in public repositories, published mHAs, and predicted HLA-peptide affinites, and analyzed their associations with chronic graft-vs.-host disease (cGvHD) grades. We found that higher estimated recipient mismatching consistently increased the risk of severe cGvHD, suggesting that HLA-presented mismatching influences the likelihood of long-term complications in the patient. Furthermore, computational approaches focusing on estimation of HLA-presentation instead of all non-synonymous mismatches indiscriminately may be beneficial for analysis sensitivity and could help identify novel mHAs.
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Affiliation(s)
- Jarmo Ritari
- Finnish Red Cross Blood Service, Helsinki, Finland
| | | | - Satu Koskela
- Finnish Red Cross Blood Service, Helsinki, Finland
| | - Riitta Niittyvuopio
- Stem Cell Transplantation Unit, Department of Hematology, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Anne Nihtinen
- Stem Cell Transplantation Unit, Department of Hematology, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Urpu Salmenniemi
- Stem Cell Transplantation Unit, Division of Medicine, Department of Hematology, Turku University Hospital, Turku, Finland
| | - Mervi Putkonen
- Stem Cell Transplantation Unit, Division of Medicine, Department of Hematology, Turku University Hospital, Turku, Finland
| | - Liisa Volin
- Stem Cell Transplantation Unit, Department of Hematology, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Tony Kwan
- Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada
| | - Tomi Pastinen
- Department of Human Genetics, McGill University and Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada.,Center for Pediatric Genomic Medicine, Children's Mercy, Kansas City, MO, United States
| | - Maija Itälä-Remes
- Stem Cell Transplantation Unit, Division of Medicine, Department of Hematology, Turku University Hospital, Turku, Finland
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Brodzik K, Krysztopa-Grzybowska K, Polak M, Lach J, Strapagiel D, Zasada AA. Analysis of the Amino Acid Sequence Variation of the 67-72p Protein and the Structural Pili Proteins of Corynebacterium diphtheriae for their Suitability as Potential Vaccine Antigens. Pol J Microbiol 2019; 68:233-246. [PMID: 31250594 PMCID: PMC7256701 DOI: 10.33073/pjm-2019-025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 03/08/2019] [Accepted: 03/09/2019] [Indexed: 11/05/2022] Open
Abstract
The aim of this study was to identify the potential vaccine antigens in Corynebacterium diphtheriae strains by in silico analysis of the amino acid variation in the 67-72p surface protein that is involved in the colonization and induction of epithelial cell apoptosis in the early stages of infection. The analysis of pili structural proteins involved in bacterial adherence to host cells and related to various types of infections was also performed. A polymerase chain reaction (PCR) was carried out to amplify the genes encoding the 67-72p protein and three pili structural proteins (SpaC, SpaI, SapD) and the products obtained were sequenced. The nucleotide sequences of the particular genes were translated into amino acid sequences, which were then matched among all the tested strains using bioinformatics tools. In the last step, the affinity of the tested proteins to major histocompatibility complex (MHC) classes I and II, and linear B-cell epitopes was analyzed. The variations in the nucleotide sequence of the 67-72p protein and pili structural proteins among C. diphtheriae strains isolated from various infections were noted. A transposition of the insertion sequence within the gene encoding the SpaC pili structural proteins was also detected. In addition, the bioinformatics analyses enabled the identification of epitopes for B-cells and T-cells in the conserved regions of the proteins, thus, demonstrating that these proteins could be used as antigens in the potential vaccine development. The results identified the most conserved regions in all tested proteins that are exposed on the surface of C. diphtheriae cells. The aim of this study was to identify the potential vaccine antigens in Corynebacterium diphtheriae strains by in silico analysis of the amino acid variation in the 67–72p surface protein that is involved in the colonization and induction of epithelial cell apoptosis in the early stages of infection. The analysis of pili structural proteins involved in bacterial adherence to host cells and related to various types of infections was also performed. A polymerase chain reaction (PCR) was carried out to amplify the genes encoding the 67–72p protein and three pili structural proteins (SpaC, SpaI, SapD) and the products obtained were sequenced. The nucleotide sequences of the particular genes were translated into amino acid sequences, which were then matched among all the tested strains using bioinformatics tools. In the last step, the affinity of the tested proteins to major histocompatibility complex (MHC) classes I and II, and linear B-cell epitopes was analyzed. The variations in the nucleotide sequence of the 67–72p protein and pili structural proteins among C. diphtheriae strains isolated from various infections were noted. A transposition of the insertion sequence within the gene encoding the SpaC pili structural proteins was also detected. In addition, the bioinformatics analyses enabled the identification of epitopes for B-cells and T-cells in the conserved regions of the proteins, thus, demonstrating that these proteins could be used as antigens in the potential vaccine development. The results identified the most conserved regions in all tested proteins that are exposed on the surface of C. diphtheriae cells.
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Affiliation(s)
- Klaudia Brodzik
- Department of Sera and Vaccines Evaluation, National Institute of Public Health - National Institute of Hygiene , Warsaw , Poland
| | - Katarzyna Krysztopa-Grzybowska
- Department of Sera and Vaccines Evaluation, National Institute of Public Health - National Institute of Hygiene , Warsaw , Poland
| | - Maciej Polak
- Department of Sera and Vaccines Evaluation, National Institute of Public Health - National Institute of Hygiene , Warsaw , Poland
| | - Jakub Lach
- Biobank Lab, Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz , Lodz , Poland
| | - Dominik Strapagiel
- Biobank Lab, Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz , Lodz , Poland ; BBMRI.pl Consortium, Wroclaw Research Center EIT+ , Wroclaw , Poland
| | - Aleksandra Anna Zasada
- Department of Sera and Vaccines Evaluation, National Institute of Public Health - National Institute of Hygiene , Warsaw , Poland
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D'Alise AM, Leoni G, Cotugno G, Troise F, Langone F, Fichera I, De Lucia M, Avalle L, Vitale R, Leuzzi A, Bignone V, Di Matteo E, Tucci FG, Poli V, Lahm A, Catanese MT, Folgori A, Colloca S, Nicosia A, Scarselli E. Adenoviral vaccine targeting multiple neoantigens as strategy to eradicate large tumors combined with checkpoint blockade. Nat Commun 2019; 10:2688. [PMID: 31217437 PMCID: PMC6584502 DOI: 10.1038/s41467-019-10594-2] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 05/21/2019] [Indexed: 12/20/2022] Open
Abstract
Neoantigens (nAgs) are promising tumor antigens for cancer vaccination with the potential of inducing robust and selective T cell responses. Genetic vaccines based on Adenoviruses derived from non-human Great Apes (GAd) elicit strong and effective T cell-mediated immunity in humans. Here, we investigate for the first time the potency and efficacy of a novel GAd encoding multiple neoantigens. Prophylactic or early therapeutic vaccination with GAd efficiently control tumor growth in mice. In contrast, combination of the vaccine with checkpoint inhibitors is required to eradicate large tumors. Gene expression profile of tumors in regression shows abundance of activated tumor infiltrating T cells with a more diversified TCR repertoire in animals treated with GAd and anti-PD1 compared to anti-PD1. Data suggest that effectiveness of vaccination in the presence of high tumor burden correlates with the breadth of nAgs-specific T cells and requires concomitant reversal of tumor suppression by checkpoint blockade.
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Affiliation(s)
| | - Guido Leoni
- Nouscom Srl, Via Castel Romano 100, 00128, Rome, Italy
| | | | - Fulvia Troise
- Nouscom Srl, Via Castel Romano 100, 00128, Rome, Italy
| | | | - Imma Fichera
- Nouscom Srl, Via Castel Romano 100, 00128, Rome, Italy
| | | | - Lidia Avalle
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126, Turin, Italy
| | - Rosa Vitale
- Nouscom Srl, Via Castel Romano 100, 00128, Rome, Italy
| | | | | | | | | | - Valeria Poli
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126, Turin, Italy
| | - Armin Lahm
- Nouscom Srl, Via Castel Romano 100, 00128, Rome, Italy
| | | | | | | | - Alfredo Nicosia
- Nouscom AG, Bäumleingasse, 18 CH-4051, Basel, Switzerland.,Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy.,CEINGE, Via Comunale Margherita, 484-538, 80131, Naples, Italy
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Li Y, Niu M, Zou Q. ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm. J Proteome Res 2019; 18:1392-1401. [DOI: 10.1021/acs.jproteome.9b00012] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Yanjuan Li
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Mengting Niu
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes. PLoS Comput Biol 2018; 14:e1006457. [PMID: 30408041 PMCID: PMC6224037 DOI: 10.1371/journal.pcbi.1006457] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 08/22/2018] [Indexed: 12/19/2022] Open
Abstract
A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.
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Khan N, Kumar R, Chauhan S, Farooq U. An immunoinformatics approach to promiscuous peptide design for the Plasmodium falciparum erythrocyte membrane protein-1. MOLECULAR BIOSYSTEMS 2018; 13:2160-2167. [PMID: 28856362 DOI: 10.1039/c7mb00332c] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Plasmodium falciparum erythrocyte membrane protein-1 (Pfemp-1), a variant adhesion molecule, can act as a key component of immunity against malaria. In the current selection of malaria vaccines, no efficient vaccines are available that can be employed for its proper treatment. Unfortunately, resistance to post-infection treatments is increasing and therefore there is a pressing need to develop an efficient vaccine. Peptide-based vaccines can be effective tools against malaria but HLA restriction is a major hindrance which can be conquered by using promiscuous peptides. In this work, we employed a combined in silico and experimental approach to identify promiscuous peptides for the treatment of malaria. At first, using the immunoinformatics approach, promiscuous peptides were predicted from two conserved domains, CIDR-1 and DBL-3γ, of the Pfemp-1 antigen. These peptides were selected on the basis of their predicted binding affinity with different HLA class-I & class-II alleles. A total of 13 peptides were selected based on their predicted IFN-γ and IL-4 induction ability as well as their hydrophobicity. Out of these 13, the peptide C6 was synthesised and experimentally evaluated for further rationalization, HLA-peptide complex modelling and binding interaction analysis. Interestingly, the peptide C6 (SFIHIYLYRNIRIQL) showed an encouraging immunological response and T-cell proliferation in the immunological assay. This valuable content can aid the better design of more potent and selective vaccine candidates against infectious diseases.
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Affiliation(s)
- Nazam Khan
- Department of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan, HP, India.
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Andreatta M, Trolle T, Yan Z, Greenbaum JA, Peters B, Nielsen M. An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics 2018; 34:1522-1528. [PMID: 29281002 PMCID: PMC5925780 DOI: 10.1093/bioinformatics/btx820] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 11/30/2017] [Accepted: 12/20/2017] [Indexed: 12/12/2022] Open
Abstract
Motivation Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. Results With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method. Availability and implementation Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/. Contact mniel@bioinformatics.dtu.dk. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | | | - Zhen Yan
- Bioinformatics Core Facility, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA
| | - Jason A Greenbaum
- Bioinformatics Core Facility, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA
| | - Morten Nielsen
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
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Moghram BA, Nabil E, Badr A. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:161-170. [PMID: 29157448 DOI: 10.1016/j.cmpb.2017.10.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 09/24/2017] [Accepted: 10/10/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. METHODS In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. RESULTS The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. CONCLUSIONS The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines.
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Affiliation(s)
- Basem Ameen Moghram
- Department of Computer Science, Faculty of Computers and Information, Cairo University, Cairo, 12613, Egypt.
| | - Emad Nabil
- Department of Computer Science, Faculty of Computers and Information, Cairo University, Cairo, 12613, Egypt.
| | - Amr Badr
- Department of Computer Science, Faculty of Computers and Information, Cairo University, Cairo, 12613, Egypt.
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Fundamentals and Methods for T- and B-Cell Epitope Prediction. J Immunol Res 2017; 2017:2680160. [PMID: 29445754 PMCID: PMC5763123 DOI: 10.1155/2017/2680160] [Citation(s) in RCA: 355] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/22/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022] Open
Abstract
Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.
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