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Schulte SC, Dilthey AT, Klau GW. HOGVAX: Exploiting epitope overlaps to maximize population coverage in vaccine design with application to SARS-CoV-2. Cell Syst 2023; 14:1122-1130.e3. [PMID: 38128484 DOI: 10.1016/j.cels.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/19/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023]
Abstract
The efficacy of epitope vaccines depends on the included epitopes as well as the probability that the selected epitopes are presented by the major histocompatibility complex (MHC) proteins of a vaccinated individual. Designing vaccines that effectively immunize a high proportion of the population is challenging because of high MHC polymorphism, diverging MHC-peptide binding affinities, and physical constraints on epitope vaccine constructs. Here, we present HOGVAX, a combinatorial optimization approach for epitope vaccine design. To optimize population coverage within the constraint of limited vaccine construct space, HOGVAX employs a hierarchical overlap graph (HOG) to identify and exploit overlaps between selected peptides and explicitly models the structure of linkage disequilibrium in the MHC. In a SARS-CoV-2 case study, we demonstrate that HOGVAX-designed vaccines contain substantially more epitopes than vaccines built from concatenated peptides and predict vaccine efficacy in over 98% of the population with high numbers of presented peptides in vaccinated individuals.
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Affiliation(s)
- Sara C Schulte
- Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Alexander T Dilthey
- Institute of Medical Microbiology and Hospital Hygiene, University Clinic Düsseldorf, Düsseldorf, Germany.
| | - Gunnar W Klau
- Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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2
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State of the art in epitope mapping and opportunities in COVID-19. Future Sci OA 2023; 16:FSO832. [PMID: 36897962 PMCID: PMC9987558 DOI: 10.2144/fsoa-2022-0048] [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: 07/29/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.
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3
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Nilsson JB, Grifoni A, Tarke A, Sette A, Nielsen M. PopCover-2.0. Improved Selection of Peptide Sets With Optimal HLA and Pathogen Diversity Coverage. Front Immunol 2021; 12:728936. [PMID: 34484239 PMCID: PMC8416060 DOI: 10.3389/fimmu.2021.728936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 07/30/2021] [Indexed: 12/30/2022] Open
Abstract
The use of minimal peptide sets offers an appealing alternative for design of vaccines and T cell diagnostics compared to conventional whole protein approaches. T cell immunogenicity towards peptides is contingent on binding to human leukocyte antigen (HLA) molecules of the given individual. HLA is highly polymorphic, and each variant typically presents a different repertoire of peptides. This polymorphism combined with pathogen diversity challenges the rational selection of peptide sets with broad immunogenic potential and population coverage. Here we propose PopCover-2.0, a simple yet highly effective method, for resolving this challenge. The method takes as input a set of (predicted) CD8 and/or CD4 T cell epitopes with associated HLA restriction and pathogen strain annotation together with information on HLA allele frequencies, and identifies peptide sets with optimal pathogen and HLA (class I and II) coverage. PopCover-2.0 was benchmarked on historic data in the context of HIV and SARS-CoV-2. Further, the immunogenicity of the selected SARS-CoV-2 peptides was confirmed by experimentally validating the peptide pools for T cell responses in a panel of SARS-CoV-2 infected individuals. In summary, PopCover-2.0 is an effective method for rational selection of peptide subsets with broad HLA and pathogen coverage. The tool is available at https://services.healthtech.dtu.dk/service.php?PopCover-2.0.
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Affiliation(s)
- Jonas Birkelund Nilsson
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark
| | - Alba Grifoni
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Alison Tarke
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Internal Medicine, University of Genoa, Genoa, Italy
- Department of Experimental Medicine, University of Genoa, Genoa, Italy
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark
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4
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Boniolo F, Dorigatti E, Ohnmacht AJ, Saur D, Schubert B, Menden MP. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin Drug Discov 2021; 16:991-1007. [PMID: 34075855 DOI: 10.1080/17460441.2021.1918096] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
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Affiliation(s)
- Fabio Boniolo
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Emilio Dorigatti
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Statistical Learning and Data Science, Department of Statistics, Ludwig Maximilian Universität München, Munich, Germany
| | - Alexander J Ohnmacht
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany
| | - Dieter Saur
- School of Medicine, Chair of Translational Cancer Research and Institute for Experimental Cancer Therapy, Klinikum Rechts Der Isar, Technische Universität München, Munich, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Centre for Environmental Health, Munich, Germany.,Department of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany.,German Centre for Diabetes Research (DZD e.V.), Neuherberg, Germany
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5
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Dorigatti E, Schubert B. Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLoS Comput Biol 2020; 16:e1008237. [PMID: 33095790 PMCID: PMC7652351 DOI: 10.1371/journal.pcbi.1008237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 11/09/2020] [Accepted: 08/11/2020] [Indexed: 12/13/2022] Open
Abstract
Epitope-based vaccines have revolutionized vaccine research in the last decades. Due to their complex nature, bioinformatics plays a pivotal role in their development. However, existing algorithms address only specific parts of the design process or are unable to provide formal guarantees on the quality of the solution. We present a unifying formalism of the general epitope vaccine design problem that tackles all phases of the design process simultaneously and combines all prevalent design principles. We then demonstrate how to formulate the developed formalism as an integer linear program, which guarantees optimality of the designs. This makes it possible to explore new regions of the vaccine design space, analyze the trade-offs between the design phases, and balance the many requirements of vaccines.
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Affiliation(s)
- Emilio Dorigatti
- Department of Statistics, Ludwig Maximilian Universität, München, Germany
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching bei München, Germany
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6
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Estrada E. COVID-19 and SARS-CoV-2. Modeling the present, looking at the future. PHYSICS REPORTS 2020; 869:1-51. [PMID: 32834430 PMCID: PMC7386394 DOI: 10.1016/j.physrep.2020.07.005] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 07/27/2020] [Indexed: 05/21/2023]
Abstract
Since December 2019 the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an outbreak of pulmonary disease which has soon become a global pandemic, known as COronaVIrus Disease-19 (COVID-19). The new coronavirus shares about 82% of its genome with the one which produced the 2003 outbreak (SARS CoV-1). Both coronaviruses also share the same cellular receptor, which is the angiotensin-converting enzyme 2 (ACE2) one. In spite of these similarities, the new coronavirus has expanded more widely, more faster and more lethally than the previous one. Many researchers across the disciplines have used diverse modeling tools to analyze the impact of this pandemic at global and local scales. This includes a wide range of approaches - deterministic, data-driven, stochastic, agent-based, and their combinations - to forecast the progression of the epidemic as well as the effects of non-pharmaceutical interventions to stop or mitigate its impact on the world population. The physical complexities of modern society need to be captured by these models. This includes the many ways of social contacts - (multiplex) social contact networks, (multilayers) transport systems, metapopulations, etc. - that may act as a framework for the virus propagation. But modeling not only plays a fundamental role in analyzing and forecasting epidemiological variables, but it also plays an important role in helping to find cures for the disease and in preventing contagion by means of new vaccines. The necessity for answering swiftly and effectively the questions: could existing drugs work against SARS CoV-2? and can new vaccines be developed in time? demands the use of physical modeling of proteins, protein-inhibitors interactions, virtual screening of drugs against virus targets, predicting immunogenicity of small peptides, modeling vaccinomics and vaccine design, to mention just a few. Here, we review these three main areas of modeling research against SARS CoV-2 and COVID-19: (1) epidemiology; (2) drug repurposing; and (3) vaccine design. Therefore, we compile the most relevant existing literature about modeling strategies against the virus to help modelers to navigate this fast-growing literature. We also keep an eye on future outbreaks, where the modelers can find the most relevant strategies used in an emergency situation as the current one to help in fighting future pandemics.
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Affiliation(s)
- Ernesto Estrada
- Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza, 50009 Zaragoza, Spain
- ARAID Foundation, Government of Aragón, 50018 Zaragoza, Spain
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7
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Structural Features of a Conformation-dependent Antigen Epitope on ORFV-B2L Recognized by the 2E4 mAb. Sci Rep 2019; 9:16094. [PMID: 31695071 PMCID: PMC6834619 DOI: 10.1038/s41598-019-52446-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/16/2019] [Indexed: 12/03/2022] Open
Abstract
Previously, we successfully prepared a monoclonal antibody (mAb) named 2E4, that directly recognizes the major envelope protein B2L of the orf virus (ORFV), but there is little information about its epitope. Here, we meticulously mapped the 2E4 epitope through combinatorial programs and identified the functional binding domain and a key amino acid residue. Briefly, the simulated epitope peptide closely resembles 84VDVQSKDKDADELR97 located at the N-terminus of B2L, strongly suggesting that the epitope is conformationally or spatially structure-dependent. Subsequently, we combined these findings with the results from the antigenicity prediction of B2L to design three truncated fragments of B2L (F1, F2 and F3) selected using 2E4, and only the F1 fragment was found to be eligible for the advanced stage. Alanine-scanning mutagenesis suggested that the D94 residue is structurally crucial for the 2E4 epitope. The other participating residues, including K61, E62, and D92, together with D94 were responsible for enabling 2E4 binding and served as factors that synergistically enabled binding to the whole 2E4 epitope. In this paper, we describe, for the first time, the architecture of an ORFV conformational epitope, and it is also expected that mAb 2E4 and its epitope can be used for applications relating to orf control.
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8
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Oyarzún P, Kobe B. Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production. Hum Vaccin Immunother 2017; 12:763-7. [PMID: 26430814 DOI: 10.1080/21645515.2015.1094595] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Novel vaccination approaches based on rational design of B- and T-cell epitopes - epitope-based vaccines - are making progress in the clinical trial pipeline. The epitope-focused recombinant protein-based malaria vaccine (termed RTS,S) is a next-generation approach that successfully reached phase-III trials, and will potentially become the first commercial vaccine against a human parasitic disease. Progress made on methods such as recombinant DNA technology, advanced cell-culture techniques, immunoinformatics and rational design of immunogens are driving the development of these novel concepts. Synthetic recombinant proteins comprising both B- and T-cell epitopes can be efficiently produced through modern biotechnology and bioprocessing methods, and can enable the induction of large repertoires of immune specificities. In particular, the inclusion of appropriate CD4+ T-cell epitopes is increasingly considered a key vaccine component to elicit robust immune responses, as suggested by results coming from HIV-1 clinical trials. In silico strategies for vaccine design are under active development to address genetic variation in pathogens and several broadly protective "universal" influenza and HIV-1 vaccines are currently at different stages of clinical trials. Other methods focus on improving population coverage in target populations by rationally considering specificity and prevalence of the HLA proteins, though a proof-of-concept in humans has not been demonstrated yet. Overall, we expect immunoinformatics and bioprocessing methods to become a central part of the next-generation epitope-based vaccine development and production process.
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Affiliation(s)
- Patricio Oyarzún
- a Biotechnology Center, Facultad de Ingeniería y Tecnología, Universidad San Sebastián , Concepción , Chile
| | - Bostjan Kobe
- b School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Center, University of Queensland , Brisbane , Australia
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9
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Abstract
String-of-beads polypeptides allow convenient delivery of epitope-based vaccines. The success of a polypeptide relies on efficient processing: constituent epitopes need to be recovered while avoiding neo-epitopes from epitope junctions. Spacers between epitopes are employed to ensure this, but spacer selection is non-trivial. We present a framework to determine optimally the length and sequence of a spacer through multi-objective optimization for human leukocyte antigen class I restricted polypeptides. The method yields string-of-bead vaccines with flexible spacer lengths that increase the predicted epitope recovery rate fivefold while reducing the immunogenicity from neo-epitopes by 44 % compared to designs without spacers.
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10
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Abstract
Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.
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Affiliation(s)
- Linus Backert
- Applied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany.
| | - Oliver Kohlbacher
- Applied Bioinformatics, Center of Bioinformatics and Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany.,Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 10, 72076, Tübingen, Germany.,Biomolecular Interactions, Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076, Tübingen, Germany
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11
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Oyarzun P, Kobe B. Computer-aided design of T-cell epitope-based vaccines: addressing population coverage. Int J Immunogenet 2015. [PMID: 26211755 DOI: 10.1111/iji.12214] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.
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Affiliation(s)
- P Oyarzun
- Biotechnology Centre, Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Concepción, Chile
| | - B Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD, Australia
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12
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Gededzha MP, Mphahlele MJ, Selabe SG. Prediction of T-cell epitopes of hepatitis C virus genotype 5a. Virol J 2014; 11:187. [PMID: 25380768 PMCID: PMC4289306 DOI: 10.1186/1743-422x-11-187] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Accepted: 10/14/2014] [Indexed: 12/26/2022] Open
Abstract
Background Hepatitis C virus (HCV) is a public health problem with almost 185 million people estimated to be infected worldwide and is one of the leading causes of hepatocellular carcinoma. Currently, there is no vaccine for HCV infection and the current treatment does not clear the infection in all patients. Because of the high diversity of HCV, protective vaccines will have to overcome significant viral antigenic diversities. The objective of this study was to predict T-cell epitopes from HCV genotype 5a sequences. Methods HCV near full-length protein sequences were analyzed to predict T-cell epitopes that bind human leukocyte antigen (HLA) class I and HLA class II in HCV genotype 5a using Propred I and Propred, respectively. The Antigenicity score of all the predicted epitopes were analysed using VaxiJen v2.0. All antigenic predicted epitopes were analysed for conservation using the IEDB database in comparison with 406, 221, 98, 33, 45, 45 randomly selected sequences from each of the HCV genotypes 1a, 1b, 2, 3, 4 and 6 respectively, downloaded from the GenBank. For epitope prediction binding to common HLA alleles found in South Africa, the IEDB epitope analysis tool was used. Results A total of 24 and 77 antigenic epitopes that bind HLA class I and HLA class II respectively were predicted. The highest number of HLA class I binding epitopes were predicted within the NS3 (63%), followed by NS5B (21%). For the HLA class II, the highest number of epitopes were predicted in the NS3 (30%) followed by the NS4B (23%) proteins. For conservation analysis, 8 and 31 predicted epitopes were conserved in different genotypes for HLA class I and HLA class II alleles respectively. Several epitopes bind with high affinity for both HLA class I alleles and HLA class II common in South Africa. Conclusion The predicted conserved T-cell epitopes analysed in this study will contribute towards the future design of HCV vaccine candidates which will avoid variation in genotypes, which in turn will be capable of inducing broad HCV specific immune responses.
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Affiliation(s)
| | | | - Selokela G Selabe
- HIV and Hepatitis Research Unit, Department of Virology, University of Limpopo, Medunsa Campus/National Health Laboratory Service, Pretoria, South Africa.
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13
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Schubert B, Lund O, Nielsen M. Evaluation of peptide selection approaches for epitope-based vaccine design. ACTA ACUST UNITED AC 2013; 82:243-51. [DOI: 10.1111/tan.12199] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Revised: 07/11/2013] [Accepted: 08/14/2013] [Indexed: 12/01/2022]
Affiliation(s)
- B. Schubert
- Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science; University of Tübingen; 72076 Tübingen Germany
| | - O. Lund
- CBS, Department of Systems Biology; Technical University of Denmark DTU; 2800 Lyngby Denmark
| | - M. Nielsen
- CBS, Department of Systems Biology; Technical University of Denmark DTU; 2800 Lyngby Denmark
- Instituto de Investigaciones Biotecnológicas; Universidad Nacional de San Martín; San Martín Argentina
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14
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HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection. Proc Natl Acad Sci U S A 2013; 110:13492-7. [PMID: 23878211 DOI: 10.1073/pnas.1221555110] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Experimental and computational evidence suggests that HLAs preferentially bind conserved regions of viral proteins, a concept we term "targeting efficiency," and that this preference may provide improved clearance of infection in several viral systems. To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study performed during the 2009-2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression). A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24, were associated with pH1N1 mortality (r = 0.37, P = 0.031) and are common in certain indigenous populations in which increased pH1N1 morbidity has been reported. HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection. The computational tools used in this study may be useful predictors of potential morbidity and identify immunologic differences of new variant influenza strains more accurately than evolutionary sequence comparisons. Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases might advance clinical practices for severe H1N1 infections among genetically susceptible populations.
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15
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Identification of B cell epitopes of dengue virus 2 NS3 protein by monoclonal antibody. Appl Microbiol Biotechnol 2012; 97:1553-60. [PMID: 23081772 DOI: 10.1007/s00253-012-4419-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2012] [Revised: 08/28/2012] [Accepted: 09/04/2012] [Indexed: 10/27/2022]
Abstract
Dengue virus is a major international public health concern, and there is a lack of available effective vaccines. Virus-specific epitopes could help in developing epitope peptide vaccine. Previously, a neutralizing monoclonal antibody (mAb) 4F5 against nonstructural protein 3 (NS3) of dengue virus 2 (DV2) was developed in our lab. In this work, the B cell epitope recognized by mAb 4F5 was identified using the phage-displayed peptide library. The results of the binding assay and competitive inhibition assay indicated that the peptides, residues 460-469 (U460-469 RVGRNPKNEN) of DV2 NS3 protein, were the B cell epitopes recognized by mAb 4F5. Furthermore, the epitope peptides and a control peptide were synthesized and then immunized female BALB/c mice. ELISA analysis showed that immunization with synthesized epitope peptide elicited a high level of antibody in mice, and immunofluorescent staining showed that the antisera from fusion epitope-immunized mice also responded to DV2 NS3 protein, which further characterized the specific response of the present epitope peptide. Therefore, the present work revealed the specificity of the newly identified epitope (U460-469) of DV2 NS3 protein, which may shed light on dengue virus (DV) vaccine design, DV pathogenesis study, and even DV diagnostic reagent development.
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16
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Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, Nielsen M, Peters B. Immune epitope database analysis resource. Nucleic Acids Res 2012; 40:W525-30. [PMID: 22610854 PMCID: PMC3394288 DOI: 10.1093/nar/gks438] [Citation(s) in RCA: 341] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Revised: 04/19/2012] [Accepted: 04/25/2012] [Indexed: 11/12/2022] Open
Abstract
The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.
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Affiliation(s)
- Yohan Kim
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Julia Ponomarenko
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Zhanyang Zhu
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Dorjee Tamang
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Peng Wang
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Jason Greenbaum
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Claus Lundegaard
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Ole Lund
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Philip E. Bourne
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Morten Nielsen
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, San Diego Supercomputer Center, MC 0505, 10100 Hopkins Drive, La Jolla, CA 92093, Sequenom, 3595 John Hopkins Court, San Diego, CA 92121, USA, Shanghai Advanced Research Institute, No.99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai, 201210, China and Technical University of Denmark, Building 208, 2800, Lyngby, Denmark
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17
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Tung CW, Ziehm M, Kämper A, Kohlbacher O, Ho SY. POPISK: T-cell reactivity prediction using support vector machines and string kernels. BMC Bioinformatics 2011; 12:446. [PMID: 22085524 PMCID: PMC3228774 DOI: 10.1186/1471-2105-12-446] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2011] [Accepted: 11/15/2011] [Indexed: 02/03/2023] Open
Abstract
Background Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. Results This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. Conclusions A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.
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Affiliation(s)
- Chun-Wei Tung
- School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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18
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Maman Y, Nir-Paz R, Louzoun Y. Bacteria modulate the CD8+ T cell epitope repertoire of host cytosol-exposed proteins to manipulate the host immune response. PLoS Comput Biol 2011; 7:e1002220. [PMID: 22022257 PMCID: PMC3192822 DOI: 10.1371/journal.pcbi.1002220] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Accepted: 08/20/2011] [Indexed: 01/09/2023] Open
Abstract
The main adaptive immune response to bacteria is mediated by B cells and CD4+ T-cells. However, some bacterial proteins reach the cytosol of host cells and are exposed to the host CD8+ T-cells response. Both gram-negative and gram-positive bacteria can translocate proteins to the cytosol through type III and IV secretion and ESX-1 systems, respectively. The translocated proteins are often essential for the bacterium survival. Once injected, these proteins can be degraded and presented on MHC-I molecules to CD8+ T-cells. The CD8+ T-cells, in turn, can induce cell death and destroy the bacteria's habitat. In viruses, escape mutations arise to avoid this detection. The accumulation of escape mutations in bacteria has never been systematically studied. We show for the first time that such mutations are systematically present in most bacteria tested. We combine multiple bioinformatic algorithms to compute CD8+ T-cell epitope libraries of bacteria with secretion systems that translocate proteins to the host cytosol. In all bacteria tested, proteins not translocated to the cytosol show no escape mutations in their CD8+ T-cell epitopes. However, proteins translocated to the cytosol show clear escape mutations and have low epitope densities for most tested HLA alleles. The low epitope densities suggest that bacteria, like viruses, are evolutionarily selected to ensure their survival in the presence of CD8+ T-cells. In contrast with most other translocated proteins examined, Pseudomonas aeruginosa's ExoU, which ultimately induces host cell death, was found to have high epitope density. This finding suggests a novel mechanism for the manipulation of CD8+ T-cells by pathogens. The ExoU effector may have evolved to maintain high epitope density enabling it to efficiently induce CD8+ T-cell mediated cell death. These results were tested using multiple epitope prediction algorithms, and were found to be consistent for most proteins tested.
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Affiliation(s)
- Yaakov Maman
- Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Ran Nir-Paz
- Department of Clinical Microbiology and Infectious Diseases, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Yoram Louzoun
- Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
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19
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Agranovich A, Vider-Shalit T, Louzoun Y. Optimal viral immune surveillance evasion strategies. Theor Popul Biol 2011; 80:233-43. [PMID: 21925527 DOI: 10.1016/j.tpb.2011.08.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2010] [Revised: 08/23/2011] [Accepted: 08/24/2011] [Indexed: 12/12/2022]
Abstract
Following cell entry, viruses can be detected by cytotoxic T lymphocytes. These cytotoxic T lymphocytes can induce host cell apoptosis and prevent the propagation of the virus. Viruses with fewer epitopes have a higher survival probability, and are selected through evolution. However, mutations have a fitness cost and on evolutionary periods viruses maintain some epitopes. The number of epitopes in each viral protein is a balance between the selective advantage of having fewer epitopes and the reduced fitness following the epitope removing mutations. We discuss a bioinformatic analysis of the number of epitopes in various viral proteins and propose an optimization framework to explain these numbers. We show, using a genomic analysis and a theoretical optimization framework, that a critical factor affecting the number of presented epitopes is the expression stage in the viral life cycle of the gene coding for the protein. The early expression of epitopes can lead to the destruction of the host cell before budding can take place. We show that a lower number of epitopes is expected in early proteins even if late proteins have a much higher copy number.
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Affiliation(s)
- Alexandra Agranovich
- Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 52900, Israel
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20
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Universal peptide vaccines - optimal peptide vaccine design based on viral sequence conservation. Vaccine 2011; 29:8745-53. [PMID: 21875632 DOI: 10.1016/j.vaccine.2011.07.132] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2011] [Revised: 07/28/2011] [Accepted: 07/28/2011] [Indexed: 01/06/2023]
Abstract
Rapidly mutating viruses such as the hepatitis C virus (HCV), the human immunodeficiency virus (HIV), or influenza viruses (Flu) call for highly effective universal peptide vaccines, i.e. vaccines that do not only yield broad population coverage but also broad coverage of various viral strains. The efficacy of such vaccines is determined by multiple properties of the epitopes they comprise. Beyond the specific properties of each epitope, properties of the corresponding source antigens are of great importance. If a response is mounted against viral proteins with a low copy number within the cell or against proteins expressed very late, this response may fail to induce lysis of the infected cells before budding can take place. We here propose a novel methodology to optimize the epitope composition and assembly in order to induce maximum protection. In order for a peptide vaccine to yield the best possible universal protection, several conditions should be met: (a) an optimal choice of target antigens, (b) an optimal choice of highly conserved epitopes, (c) maximum coverage of the target population, and (d) the proper ordering of the epitopes in the final vaccine to ensure favorable cleavage. We propose a mathematical formalism for epitope selection and ordering that balances the constraints imposed by these different conditions. Focusing on HCV, HIV, and Flu, we show that not all of the conditions can be satisfied for all viruses. Depending on the virus, different constraints are harder to fulfill: for Flu, the conservation constraint is violated first, while for HIV, it is difficult to focus the response at the optimal target antigens. The proposed methodology can be applied to any virus to assess the feasibility of optimally combining the above-mentioned constraints.
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21
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Kovjazin R, Volovitz I, Daon Y, Vider-Shalit T, Azran R, Tsaban L, Carmon L, Louzoun Y. Signal peptides and trans-membrane regions are broadly immunogenic and have high CD8+ T cell epitope densities: Implications for vaccine development. Mol Immunol 2011; 48:1009-18. [PMID: 21316766 DOI: 10.1016/j.molimm.2011.01.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2010] [Revised: 12/28/2010] [Accepted: 01/09/2011] [Indexed: 12/22/2022]
Abstract
Cell mediated immune response has a major role in controlling the elimination of infectious agents. The rational design of sub-unit peptide vaccines against intracellular pathogens or cancer requires the use of antigenic sequence/s that can induce highly potent, long lasting and antigen-specific responses in the majority of the population. A promising peptide selection strategy is the detection of multi-epitope peptide sequences with an ability to bind multiple MHC alleles. While past research sought the best epitopes based on their specific antigenicity, we ask whether specific defined domains have high epitope densities. Signal peptides and trans-membrane domains were found to have exceptionally high epitope densities. The improved MHC binding of these domains relies on their hydrophobic nature and, in signal peptides, also on their specific sequence. The high epitope density of SP was computed using in-silico methods and corroborated by the high percentage of identified SP epitope in the IEDB (immune epitope database). The enhanced immunogenicity of SP was then experimentally confirmed using a panel of nine peptides derived from Mycobacterium tuberculosis (MTb) proteins used in human PBMC proliferation assays and T cell lines functional assays. Our results show the exceptionally high antigen specific response rates and population coverage to SP sequences compared with non-SP peptide antigens derived from the same proteins. The results suggest a novel scheme for the rational design of T cell vaccines using a domain based rather than an epitope based approach.
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Affiliation(s)
- Riva Kovjazin
- Vaxil BioTherapeutics Ltd. 13A, WIS Science Park, Nes-Ziona 74036, Israel
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22
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Immune-induced evolutionary selection focused on a single reading frame in overlapping hepatitis B virus proteins. J Virol 2011; 85:4558-66. [PMID: 21307195 DOI: 10.1128/jvi.02142-10] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Viruses employ various means to evade immune detection. Reduction of CD8(+) T cell epitopes is one of the common strategies used for this purpose. Hepatitis B virus (HBV), a member of the Hepadnaviridae family, has four open reading frames, with about 50% overlap between the genes they encode. We computed the CD8(+) T cell epitope density within HBV proteins and the mutations within the epitopes. Our results suggest that HBV accumulates escape mutations that reduce the number of epitopes. These mutations are not equally distributed among genes and reading frames. While the highly expressed core and X proteins are selected to have low epitope density, polymerase, which is expressed at low levels, does not undergo the same selection. In overlapping regions, mutations in one protein-coding sequence also affect the other protein-coding sequence. We show that mutations lead to the removal of epitopes in X and surface proteins even at the expense of the addition of epitopes in polymerase. The total escape mutation rate for overlapping regions is lower than that for nonoverlapping regions. The lower epitope replacement rate for overlapping regions slows the evolutionary escape rate of these regions but leads to the accumulation of mutations more robust in the transfer between hosts, such as mutations preventing proteasomal cleavage into epitopes.
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23
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Innovative bioinformatic approaches for developing peptide-based vaccines against hypervariable viruses. Immunol Cell Biol 2010; 89:81-9. [PMID: 20458336 DOI: 10.1038/icb.2010.65] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The application of the fields of pharmacogenomics and pharmacogenetics to vaccine design has been recently labeled 'vaccinomics'. This newly named area of vaccine research, heavily intertwined with bioinformatics, seems to be leading the charge in developing novel vaccines for currently unmet medical needs against hypervariable viruses such as human immunodeficiency virus (HIV), hepatitis C and emerging avian and swine influenza. Some of the more recent bioinformatic approaches in the area of vaccine research include the use of epitope determination and prediction algorithms for exploring the use of peptide epitopes as vaccine immunogens. This paper briefly discusses and explores some current uses of bioinformatics in vaccine design toward the pursuit of peptide vaccines for hypervariable viruses. The various informatics and vaccine design strategies attempted by other groups toward hypervariable viruses will also be briefly examined, along with the strategy used by our group in the design and synthesis of peptide immunogens for candidate HIV and influenza vaccines.
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24
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Toussaint NC, Kohlbacher O. Towards in silico design of epitope-based vaccines. Expert Opin Drug Discov 2009; 4:1047-60. [PMID: 23480396 DOI: 10.1517/17460440903242283] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered. METHODS Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs. CONCLUSION State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.
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Affiliation(s)
- Nora C Toussaint
- Eberhard Karls University, Center for Bioinformatics Tübingen, Division for Simulation of Biological Systems, 72076 Tübingen, Germany +49 7071 2970458 ; +49 7071 295152 ;
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25
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Toussaint NC, Kohlbacher O. OptiTope--a web server for the selection of an optimal set of peptides for epitope-based vaccines. Nucleic Acids Res 2009; 37:W617-22. [PMID: 19420066 PMCID: PMC2703925 DOI: 10.1093/nar/gkp293] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Epitope-based vaccines (EVs) have recently been attracting significant interest. They trigger an immune response by confronting the immune system with immunogenic peptides derived from, e.g. viral- or cancer-related proteins. Binding of these peptides to proteins from the major histocompatibility complex (MHC) is crucial for immune system activation. However, since the MHC is highly polymorphic, different patients typically bind different repertoires of peptides. Furthermore, economical and regulatory issues impose strong limitations on the number of peptides that can be included in an EV. Hence, it is crucial to identify the optimal set of peptides for a vaccine, given constraints such as MHC allele probabilities in the target population, peptide mutation rates and maximum number of selected peptides. OptiTope aims at assisting immunologists in this critical task. With OptiTope, we provide an easy-to-use tool to determine a provably optimal set of epitopes with respect to overall immunogenicity in a specific individual (personalized medicine) or a target population (e.g. a certain ethnic group). OptiTope is available at http://www.epitoolkit.org/optitope.
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Affiliation(s)
- Nora C Toussaint
- Division for Simulation of Biological Systems, Wilhelm Schickard Institute for Computer Science, Center for Bioinformatics Tübingen, Eberhard-Karls-Universität Tübingen, Germany.
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26
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Toussaint NC, Dönnes P, Kohlbacher O. A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines. PLoS Comput Biol 2008; 4:e1000246. [PMID: 19112482 PMCID: PMC2588662 DOI: 10.1371/journal.pcbi.1000246] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2008] [Accepted: 11/06/2008] [Indexed: 01/28/2023] Open
Abstract
Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic
to prophylactic approaches, from infectious diseases to cancer. The development
of an EV is based on the knowledge of target-specific antigens from which
immunogenic peptides, so-called epitopes, are derived. Such epitopes form the
key components of the EV. Due to regulatory, economic, and practical concerns
the number of epitopes that can be included in an EV is limited. Furthermore, as
the major histocompatibility complex (MHC) binding these epitopes is highly
polymorphic, every patient possesses a set of MHC class I and class II molecules
of differing specificities. A peptide combination effective for one person can
thus be completely ineffective for another. This renders the optimal selection
of these epitopes an important and interesting optimization problem. In this
work we present a mathematical framework based on integer linear programming
(ILP) that allows the formulation of various flavors of the vaccine design
problem and the efficient identification of optimal sets of epitopes. Out of a
user-defined set of predicted or experimentally determined epitopes, the
framework selects the set with the maximum likelihood of eliciting a broad and
potent immune response. Our ILP approach allows an elegant and flexible
formulation of numerous variants of the EV design problem. In order to
demonstrate this, we show how common immunological requirements for a good EV
(e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a
set, or avoidance of epitopes with high mutation rates) can be translated into
constraints or modifications of the objective function within the ILP framework.
An implementation of the algorithm outperforms a simple greedy strategy as well
as a previously suggested evolutionary algorithm and has runtimes on the order
of seconds for typical problem sizes. Over the last decade the design of tailor-made vaccines for prophylactic
applications (e.g., prevention of infection) and therapeutic applications (e.g.,
cancer therapy) has attracted significant interest. Epitope-based vaccines are
good candidates for such tailor-made approaches. They trigger an immune response
by confronting the immune system with immunogenic peptides derived from, e.g.,
viral- or cancer-specific proteins. These peptides bind to major
histocompatibility complex (MHC) molecules in a specific manner. The resulting
complex is crucial for immune system activation. However, there are many allelic
variants of MHC molecules, meaning that different patients typically bind
different repertoires of peptides. Nevertheless, due to economical and
regulatory issues one cannot simply add all immunogenic peptides to such a
peptide mix. Hence, it is crucial to identify the optimal set of peptides for a
vaccine, given constraints such as MHC allele frequencies in the target
population, peptide mutation rates, and maximum number of selected peptides. In
this work we formalize this problem, and variants thereof, in a mathematical
framework. The resulting optimization problem can be solved efficiently and
yields a provably optimal peptide combination. We can show that the method
performs better than existing solutions. Furthermore, the framework is highly
flexible and can easily handle additional criteria.
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Affiliation(s)
- Nora C Toussaint
- Simulation of Biological Systems, Center for Bioinformatics, Eberhard Karls University Tübingen, Tübingen, Germany.
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27
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Radhakrishnan ML, Tidor B. Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems. J Chem Inf Model 2008; 48:1055-73. [PMID: 18505239 DOI: 10.1021/ci700452r] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity required. We also treated cases in which a subset of target variants was to be avoided, modeling the common challenge of closely related host molecules that may be implicated in drug toxicity. Such decoys generally increased the size of the required cocktail and more often resulted in infeasible optimizations. Taken together, this work provides practical optimization methods for the design of drug cocktails and a theoretical, physics-based framework through which useful insights can be achieved.
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Affiliation(s)
- Mala L Radhakrishnan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, USA
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28
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Sommerfelt MA, Sørensen B. Prospects for HIV-1 therapeutic immunisation and vaccination: the potential contribution of peptide immunogens. Expert Opin Biol Ther 2008; 8:745-57. [DOI: 10.1517/14712598.8.6.745] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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29
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Abstract
Synthetic peptide vaccines have potential to control viral infections. Successful experimental models using this approach include the protection of mice against the lethal Sendai virus infection by MHC class I binding CTL peptide epitope. The main benefit of vaccination with peptide epitopes is the ability to minimize the amount and complexity of a well-defined antigen. An appropriate peptide immunogen would also decrease the chance of stimulating a response against self-antigens, thereby providing a safer vaccine by avoiding autoimmunity. In general, the peptide vaccine strategy needs to dissect the specificity of antigen processing, the presence of B-and T-cell epitopes and the MHC restriction of the T-cell responses. This article briefly reviews the implications in the design of peptide vaccines and discusses the various approaches that are applied to improve their immunogenicity.
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Affiliation(s)
- Ali Azizi
- Variation Biotechnologies Inc., 22 de Varennes, Suite 210, Gatineau, QC J8T 8R1, Canada
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30
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Parida R, Shaila MS, Mukherjee S, Chandra NR, Nayak R. Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential. Vaccine 2007; 25:7530-9. [PMID: 17900763 DOI: 10.1016/j.vaccine.2007.08.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2007] [Revised: 08/16/2007] [Accepted: 08/19/2007] [Indexed: 01/10/2023]
Abstract
The existing vaccines against influenza are based on the generation of neutralizing antibody primarily directed against surface proteins - hemagglutinin and neuraminidase. In this work, we have computationally defined conserved T cell epitopes of proteins of influenza virus H5N1 to help in the design of a vaccine with haplotype specificity for a target population. The peptides from the proteome of H5N1 virus which are predicted to bind to different HLAs, do not show similarity with peptides of human proteome and are also identified to be generated by proteolytic cleavage. These peptides could be made use of in the design of either a DNA vaccine or a subunit vaccine against influenza.
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Affiliation(s)
- R Parida
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore 560012, India
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