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A special issue of Vaccines (ISSN 2076-393X). This special issue belongs to the section "Attenuated/Inactivated/Live and Vectored Vaccines".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 30745

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Dr. Daniel Dory

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ANSES - French Agency for Food, Environmental and Occupational Health & Safety, Ploufragan-Plouzane-Niort Laboratory, Viral Genetics and Biosafety Unit, 22440 Ploufragan, France
Interests: porcine; chicken and fish vaccinology; identification of novel vaccine antigens; reverse vaccinology; antibody repertoire; DNA vaccination; mucosal vaccination
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Dear Colleagues,

It is not always possible to identify vaccine antigens by classical strategies. Therefore, novel identification strategies based, at least partly, on bioinformatic analyses have been developed over the last years. For example, reverse vaccinology consists in researching genes of the pathogen encoding potential vaccine antigens using specific software and databases. One other example is based on high throughout sequencing and analysis of the antibody repertoire of hosts after infection with a given pathogen. This could help to identify sequences of antibodies specifically involved in the protection against this pathogen, which will then make it possible to go back to the targeted epitope, again by bioinformatics analyses. Such strategies were first applied to develop novel human vaccines. Afterwards, similar strategies were employed in the veterinary field.

The aim of this specific issue is to publish papers describing the development or optimization of human or veterinary vaccines identified through reverse vaccinology or other strategies partly based on bioinformatics (for example after antibody repertoire analysis). This issue will also contains papers describing the development of novel bioinformatics strategies enabling the research of vaccine antigens or papers showing novel methods enabling the expression of antigens identified by bioinformatics strategies. This will include B and/or T-cell antigens.

Dr. Daniel Dory
Guest Editor

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Keywords

novel reverse vaccinology tools
antibodies sequencing
human and veterinary vaccine antigens identified through bioinformatic based strategies
B-cell antigen identification
T-cell antigen identification
bacteria, viral of parasite vaccine antigens
antigen vectors
antigen expression

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Published Papers (10 papers)

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Research

23 pages, 3474 KiB

Open AccessArticle

A Customizable Suite of Methods to Sequence and Annotate Cattle Antibodies

by Kristel Ramirez Valdez, Benjamin Nzau, Daniel Dorey-Robinson, Michael Jarman, James Nyagwange, John C. Schwartz, Graham Freimanis, Angela W. Steyn, George M. Warimwe, Liam J. Morrison, William Mwangi, Bryan Charleston, Marie Bonnet-Di Placido and John A. Hammond

Vaccines 2023, 11(6), 1099; https://doi.org/10.3390/vaccines11061099 - 14 Jun 2023

Cited by 3 | Viewed by 2553

Abstract

Studying the antibody response to infection or vaccination is essential for developing more effective vaccines and therapeutics. Advances in high-throughput antibody sequencing technologies and immunoinformatic tools now allow the fast and comprehensive analysis of antibody repertoires at high resolution in any species. Here, we detail a flexible and customizable suite of methods from flow cytometry, single cell sorting, heavy and light chain amplification to antibody sequencing in cattle. These methods were used successfully, including adaptation to the 10x Genomics platform, to isolate native heavy–light chain pairs. When combined with the Ig-Sequence Multi-Species Annotation Tool, this suite represents a powerful toolkit for studying the cattle antibody response with high resolution and precision. Using three workflows, we processed 84, 96, and 8313 cattle B cells from which we sequenced 24, 31, and 4756 antibody heavy–light chain pairs, respectively. Each method has strengths and limitations in terms of the throughput, timeline, specialist equipment, and cost that are each discussed. Moreover, the principles outlined here can be applied to study antibody responses in other mammalian species. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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26 pages, 3279 KiB

Open AccessArticle

Immunoinformatics Approach to Design a Multi-Epitope Nanovaccine against Leishmania Parasite: Elicitation of Cellular Immune Responses

by Maritsa Margaroni, Maria Agallou, Evgenia Tsanaktsidou, Olga Kammona, Costas Kiparissides and Evdokia Karagouni

Vaccines 2023, 11(2), 304; https://doi.org/10.3390/vaccines11020304 - 30 Jan 2023

Cited by 12 | Viewed by 2997

Abstract

Leishmaniasis is a vector-borne disease caused by an intracellular parasite of the genus Leishmania with different clinical manifestations that affect millions of people worldwide, while the visceral form may be fatal if left untreated. Since the available chemotherapeutic agents are not satisfactory, vaccination emerges as the most promising strategy for confronting leishmaniasis. In the present study, a reverse vaccinology approach was adopted to design a pipeline starting from proteome analysis of three different Leishmania species and ending with the selection of a pool of MHCI- and MHCII-binding epitopes. Epitopes from five parasite proteins were retrieved and fused to construct a multi-epitope chimeric protein, named LeishChim. Immunoinformatics analyses indicated that LeishChim was a stable, non-allergenic and immunogenic protein that could bind strongly onto MHCI and MHCII molecules, suggesting it as a potentially safe and effective vaccine candidate. Preclinical evaluation validated the in silico prediction, since the LeishChim protein, encapsulated simultaneously with monophosphoryl lipid A (MPLA) into poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles, elicited specific cellular immune responses when administered to BALB/c mice. These were characterized by the development of memory CD4⁺ T cells, as well as IFNγ- and TNFα-producing CD4⁺ and CD8⁺ T cells, supporting the potential of LeishChim as a vaccine candidate. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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35 pages, 2704 KiB

Open AccessArticle

Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells

by Timothy Connelley, Annalisa Nicastri, Tara Sheldrake, Christina Vrettou, Andressa Fisch, Birkir Reynisson, Soren Buus, Adrian Hill, Ivan Morrison, Morten Nielsen and Nicola Ternette

Vaccines 2022, 10(11), 1907; https://doi.org/10.3390/vaccines10111907 - 11 Nov 2022

Cited by 4 | Viewed by 2551

Abstract

The apicomplexan parasite Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8⁺ T cells, with CD4⁺ T-cells thought to be important in facilitating the full maturation and development of the CD8⁺ T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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19 pages, 2930 KiB

Open AccessArticle

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method

by Sadhana Suri and Sivanesan Dakshanamurthy

Vaccines 2022, 10(10), 1678; https://doi.org/10.3390/vaccines10101678 - 8 Oct 2022

Cited by 5 | Viewed by 3072

Abstract

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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14 pages, 2925 KiB

Open AccessArticle

Immunoinformatics-Based Identification of B and T Cell Epitopes in RNA-Dependent RNA Polymerase of SARS-CoV-2

by Shabir Ahmad Mir, Mohammed Alaidarous, Bader Alshehri, Abdul Aziz Bin Dukhyil, Saeed Banawas, Yahya Madkhali, Suliman A. Alsagaby and Ayoub Al Othaim

Vaccines 2022, 10(10), 1660; https://doi.org/10.3390/vaccines10101660 - 3 Oct 2022

Cited by 6 | Viewed by 2189

Abstract

Introduction: The ongoing coronavirus disease 2019 (COVID-19), which emerged in December 2019, is a serious health concern throughout the world. Despite massive COVID-19 vaccination on a global scale, there is a rising need to develop more effective vaccines and drugs to curb the spread of coronavirus. Methodology: In this study, we screened the amino acid sequence of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 (the causative agent of COVID-19) for the identification of B and T cell epitopes using various immunoinformatic tools. These identified potent B and T cell epitopes with high antigenicity scores were linked together to design the multi-epitope vaccine construct. The physicochemical properties, overall quality, and stability of the designed vaccine construct were confirmed by suitable bioinformatic tools. Results: After proper in silico prediction and screening, we identified 3 B cell, 18 CTL, and 10 HTL epitopes from the RdRp protein sequence. The screened epitopes were non-toxic, non-allergenic, and highly antigenic in nature as revealed by appropriate servers. Molecular docking revealed stable interactions of the designed multi-epitope vaccine with human TLR3. Moreover, in silico immune simulations showed a substantial immunogenic response of the designed vaccine. Conclusions: These findings suggest that our designed multi-epitope vaccine possessing intrinsic T cell and B cell epitopes with high antigenicity scores could be considered for the ongoing development of peptide-based novel vaccines against COVID-19. However, further in vitro and in vivo studies need to be performed to confirm our in silico observations. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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18 pages, 2306 KiB

Open AccessArticle

mRNA Vaccine Designing Using Chikungunya Virus E Glycoprotein through Immunoinformatics-Guided Approaches

by Samavia Jaan, Aqal Zaman, Sarfraz Ahmed, Mohibullah Shah and Suvash Chandra Ojha

Vaccines 2022, 10(9), 1476; https://doi.org/10.3390/vaccines10091476 - 6 Sep 2022

Cited by 13 | Viewed by 3807

Abstract

Chikungunya virus is an alphavirus transmitted by mosquitos that develops into chikungunya fever and joint pain in humans. This virus’ name originated from a Makonde term used to describe an illness that changes the joints and refers to the posture of afflicted patients who are affected by excruciating joint pain. There is currently no commercially available drug or vaccine for chikungunya virus infection and the treatment is performed by symptom reduction. Herein, we have developed a computationally constructed mRNA vaccine construct featuring envelope glycoprotein as the target molecule to aid in the treatment process. We have utilized the reverse vaccinology approach to determine epitopes that would generate adaptive immune reactions. The resulting T and B lymphocytes epitopes were screened by various immunoinformatic tools and a peptide vaccine construct was designed. It was validated by proceeding to docking and MD simulation studies. The following design was then back-translated in nucleotide sequence and codons were optimized according to the expression host system (H. sapiens). Various sequences, including 3′ and 5′ UTR regions, Kozak sequence, poly (A) tail, etc., were introduced into the sequence for the construction of the final mRNA vaccine construct. The secondary structure was generated for validation of the mRNA vaccine construct sequence. Additionally, in silico cloning was also performed to design a vector for proceeding towards in vitro experimentation. The proposed designed vaccine construct may proceed with experimental testing for further efficacy verification and the final development of a vaccine against chikungunya virus infection. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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20 pages, 4180 KiB

Open AccessArticle

Immunoinformatics-Aided Analysis of RSV Fusion and Attachment Glycoproteins to Design a Potent Multi-Epitope Vaccine

by Hamza Arshad Dar, Fahad Nasser Almajhdi, Shahkaar Aziz and Yasir Waheed

Vaccines 2022, 10(9), 1381; https://doi.org/10.3390/vaccines10091381 - 24 Aug 2022

Cited by 8 | Viewed by 2687

Abstract

Respiratory syncytial virus (RSV) usually causes respiratory tract infections of upper airways in infants and young children. Despite recent medical advances, no approved vaccine is available to control RSV infections. Therefore, we conducted an immunoinformatics study to design and evaluate a potential multi-epitope vaccine against RSV. Sequence-based analyses of the glycoproteins F and G revealed a total of eight CD8 T-cell and three CD4 T-cell epitopes after considering antigenicity, binding affinity and other parameters. Molecular docking analysis confirmed that these T-cell epitopes developed strong structural associations with HLA allele(s). By integrating these prioritized epitopes with linkers and a cholera toxin-derived adjuvant, a multi-epitope vaccine was designed. The developed vaccine was found to be stable, non-allergenic, flexible and antigenic. Molecular docking analysis revealed a striking mean HADDOCK score (−143.3) of top-ranked vaccine-TLR cluster and a Gibbs free energy change (ΔG) value of −11.3 kcal mol⁻¹. As per computational immune simulation results, the vaccine generated a high titer of antibodies (especially IgM) and effector T-cells. Also, codon optimization and in silico cloning ensured the increased expression of vaccine in Escherichia coli. Altogether, we anticipate that the multi-epitope vaccine reported in this study will stimulate humoral and cellular responses against RSV infection, subject to follow-up experimental validation. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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18 pages, 7262 KiB

Open AccessArticle

Immunoinformatics-Based Proteome Mining to Develop a Next-Generation Vaccine Design against Borrelia burgdorferi: The Cause of Lyme Borreliosis

by Kashaf Khalid, Omar Ahsan, Tanwir Khaliq, Khalid Muhammad and Yasir Waheed

Vaccines 2022, 10(8), 1239; https://doi.org/10.3390/vaccines10081239 - 2 Aug 2022

Cited by 3 | Viewed by 2562

Abstract

The tick-borne bacterium, Borrelia burgdorferi has been implicated in Lyme disease—a deadly infection, formerly confined to North America, but currently widespread across Europe and Asia. Despite the severity of this disease, there is still no human Lyme disease vaccine available. A reliable immunoinformatic approach is urgently needed for designing a therapeutic vaccine against this Gram-negative pathogen. Through this research, we explored the immunodominant proteins of B. burgdorferi and developed a novel and reliable vaccine design with great immunological predictability as well as low contamination and autoimmunity risks. Our initial analysis involved proteome-wide analysis to filter out proteins on the basis of their redundancy, homology to humans, virulence, immunogenicity, and size. Following the selection of proteins, immunoinformatic tools were employed to identify MHC class I & II epitopes and B-cell epitopes, which were subsequently subjected to a rigorous screening procedure. In the final formulation, ten common MHC-I and II epitopes were used together with a suitable adjuvant. We predicted that the final chimeric multi-epitope vaccine could invoke B-cell responses and IFN-gamma-mediated immunity as well as being stable and non-allergenic. The dynamics simulations predicted the stable folding of the designed molecule, after which the molecular docking predicted the stability of the interaction between the potential antigenic epitopes and human immune receptors. Our studies have shown that the designed next-generation vaccine stimulates desirable immune responses, thus potentially providing a viable way to prevent Lyme disease. Nevertheless, further experimental studies in a wet lab are needed in order to validate the results. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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22 pages, 8472 KiB

Open AccessArticle

Design of Multi-Epitope Vaccine for Staphylococcus saprophyticus: Pan-Genome and Reverse Vaccinology Approach

by Maha Yousaf, Asad Ullah, Nida Sarosh, Sumra Wajid Abbasi, Saba Ismail, Shabana Bibi, Mohammad Mehedi Hasan, Ghadeer M. Albadrani, Nehal Ahmed Talaat Nouh, Jawaher A. Abdulhakim, Mohamed M. Abdel-Daim and Talha Bin Emran

Vaccines 2022, 10(8), 1192; https://doi.org/10.3390/vaccines10081192 - 27 Jul 2022

Cited by 10 | Viewed by 4009

Abstract

Staphylococcus saprophyticus is a Gram-positive coccus responsible for the occurrence of cystitis in sexually active, young females. While effective antibiotics against this organism exist, resistant strains are on the rise. Therefore, prevention via vaccines appears to be a viable solution to address this problem. In comparison to traditional techniques of vaccine design, computationally aided vaccine development demonstrates marked specificity, efficiency, stability, and safety. In the present study, a novel, multi-epitope vaccine construct was developed against S. saprophyticus by targeting fully sequenced proteomes of its five different strains, which were examined using a pangenome and subtractive proteomic strategy to characterize prospective vaccination targets. The three immunogenic vaccine targets which were utilized to map the probable immune epitopes were verified by annotating the entire proteome. The predicted epitopes were further screened on the basis of antigenicity, allergenicity, water solubility, toxicity, virulence, and binding affinity towards the DRB*0101 allele, resulting in 11 potential epitopes, i.e., DLKKQKEKL, NKDLKKQKE, QDKLKDKSD, NVMDNKDLE, TSGTPDSQA, NANSDGSSS, GSDSSSSNN, DSSSSNNDS, DSSSSDRNN, SSSDRNNGD, and SSDDKSKDS. All these epitopes have the efficacy to cover 99.74% of populations globally. Finally, shortlisted epitopes were joined together with linkers and three different adjuvants to find the most stable and immunogenic vaccine construct. The top-ranked vaccine construct was further scrutinized on the basis of its physicochemical characterization and immunological profile. The non-allergenic and antigenic features of modeled vaccine constructs were initially validated and then subjected to docking with immune receptor major histocompatibility complex I and II (MHC-I and II), resulting in strong contact. In silico cloning validations yielded a codon adaptation index (CAI) value of 1 and an ideal percentage of GC contents (46.717%), indicating a putative expression of the vaccine in E. coli. Furthermore, immune simulation demonstrated that, after injecting the proposed MEVC, powerful antibodies were produced, resulting in the sharpest peaks of IgM + IgG formation (>11,500) within 5 to 15 days. Experimental testing against S. saprophyticus can evaluate the safety and efficacy of these prophylactic vaccination designs. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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18 pages, 2966 KiB

Open AccessArticle

In Silico Designed Multi-Epitope Immunogen “Tpme-VAC/LGCM-2022” May Induce Both Cellular and Humoral Immunity against Treponema pallidum Infection

by Lucas Gabriel Rodrigues Gomes, Thaís Cristina Vilela Rodrigues, Arun Kumar Jaiswal, Roselane Gonçalves Santos, Rodrigo Bentes Kato, Debmalya Barh, Khalid J. Alzahrani, Hamsa Jameel Banjer, Siomar de Castro Soares, Vasco Azevedo and Sandeep Tiwari

Vaccines 2022, 10(7), 1019; https://doi.org/10.3390/vaccines10071019 - 25 Jun 2022

Cited by 7 | Viewed by 3002

Abstract

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum, has seen a resurgence over the past years. T. pallidum is capable of early dissemination and immune evasion, and the disease continues to be a global healthcare burden. The purpose of this study was to design a multi-epitope immunogen through an immunoinformatics-based approach. Multi-epitope immunogens constitute carefully selected epitopes belonging to conserved and essential bacterial proteins. Several physico-chemical characteristics, such as antigenicity, allergenicity, and stability, were determined. Further, molecular docking and dynamics simulations were performed, ensuring binding affinity and stability between the immunogen and TLR-2. An in silico cloning was performed using the pET-28a(+) vector and codon adaptation for E. coli. Finally, an in silico immune simulation was performed. The in silico predictions obtained in this work indicate that this construct would be capable of inducing the requisite immune response to elicit protection against T. pallidum. Through this methodology we have designed a promising potential vaccine candidate for syphilis, namely Tpme-VAC/LGCM-2022. However, it is necessary to validate these findings in in vitro and in vivo assays. Full article

(This article belongs to the Special Issue Reverse Vaccinology and Vaccine Antigens)

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