Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

An immuno-informatics approach for annotation of hypothetical proteins and multi-epitope vaccine designed against the Mpox virus

A worrying new outbreak of Monkeypox (Mpox) in humans is caused by the Mpox virus (MpoxV). The pathogen has roughly 28 hypothetical proteins of unknown structure, function, and pathogenicity. Using reliable bioinformatics tools, we attempted to analyze the MpoxV genome, identify the role of hypothetical proteins (HPs), and design a potential candidate vaccine. Out of 28, we identified seven hypothetical proteins using multi-server validation with high confidence for the occurrence of conserved domains. Their physical, chemical, and functional characterizations, including molecular weight, theoretical isoelectric point, 3D structures, GRAVY value, subcellular localization, functional motifs, antigenicity, and virulence factors, were performed. We predicted possible cytotoxic T cell (CTL), helper T cell (HTL) and linear and conformational B cell epitopes, which were combined in a 219 amino acid multiepitope vaccine with human β defensin as a linker. This multi-epitopic vaccine was structurally modelled and docked with toll-like receptor-3 (TLR-3). The dynamical stability of the vaccine-TLR-3 docked complexes exhibited stable interactions based on RMSD and RMSF tests. Additionally, the modelled vaccine was cloned in-silico in an E. coli host to check the appropriate expression of the final vaccine built. Our results might conform to an immunogenic and safe vaccine, which would require further experimental validation.Communicated by Ramaswamy H. Sarma.

In silico design and evaluation of a multiepitope vaccine targeting the nucleoprotein of Puumala orthohantavirus

The Puumala orthohantavirus is present in the body of the bank vole (Myodes glareolus). Humans infected with this virus may develop hemorrhagic fever accompanying renal syndrome. In addition, the infection may further lead to the failure of an immune system completely. The present study aimed to propose a possible vaccine by employing bioinformatics techniques to identify B and T-cell antigens. The best multi-epitope of potential immunogenicity was generated by combining epitopes. Additionally, the linkers EAAAK, AAY, and GPGPG were utilized in order to link the epitopes successfully. Further, C-ImmSim was used to perform in silico immunological simulations upon the vaccine. For the purpose of conducting expression tests in Escherichia coli, the chimeric protein construct was cloned using Snapgene into the pET-9c vector. The designed vaccine showed adequate results, evidenced by the global population coverage and favorable immune response. The developed vaccine was found to be highly effective and to have excellent population coverage in a number of computer-based assessments. This work is fully dependent on the development of nucleoprotein-based vaccines, which would constitute a significant step forward if our findings were used in developing a global vaccination to combat the Puumala virus.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

A bioinformatic analysis: Previous allergen exposure may support anti- SARS-CoV-2 immune response

COVID-19, caused by infection with the SARS-CoV-2 has become a global health problem due to significant mortality rates; the exact pathophysiological mechanism remains uncertain. Articles reporting patient data are quite heterogeneous and have several limitations. Surviving patients develop a CD4 and CD8 T-cell response to the virus SARS-CoV-2 during COVID-19. Interestingly, pre-existing virus-reactive T-cells have been found in patients that were not infected before, suggesting some form of cross-reactivity or immunological mimicry. To better understand this phenomenon, we performed a bioinformatic study, which was aimed to identify antigenic structures that may explain the presence of such "reactive" T-cells, which may support or modulate the immune response to SARS-CoV-2 infections. Seven different common environmental allergen epitopes identical to the SARS-CoV-2 S-protein were identified that share affinity to 8 MHCI-specific epitope regions. Pollen showed the greatest similarity with the S protein epitope. In the epitope similarity analysis between the S protein and MHC-II / T helper epitopes, the highest similarity was determined for mites. When S-protein that stimulates B cells and identical epitope antigens are examined, the most common allergens were hornbeam and wheat. The high epitope similarity observed for the allergens examined and S protein epitopes suggest that these allergens may be a reason for pre-existing SARS-CoV-2 - reactive T-cells in previously non-infected subjects and such a previous exposure may affect the course of the disease in COVID-19 infection. It remains to be determined whether such a previous existence of SARS-CoV-2 reactive cells can support the clearance of the virus or if they, in contrast, may even aggravate the disease course. (Table 4, Ref 54).

Designing a novel and combinatorial multi-antigenic epitope-based vaccine "MarVax" against Marburg virus-a reverse vaccinology and immunoinformatics approach

CONTEXT

Marburg virus (MARV) is a member of the Filoviridae family and causes Marburg virus disease (MVD) among humans and primates. With fatality rates going up to 88%, there is currently no commercialized cure or vaccine to combat the infection. The National Institute of Allergy and Infectious Diseases (NIAID) classified MARV as priority pathogen A, which presages the need for a vaccine candidate which can provide stable, long-term adaptive immunity. The surface glycoprotein (GP) and fusion protein (FP) mediate the adherence, fusion, and entry of the virus into the host cell via the TIM-I receptor. Being important antigenic determinants, studies reveal that GP and FP are prone to evolutionary mutations, underscoring the requirement of a vaccine construct capable of eliciting a robust and sustained immune response. In this computational study, a reverse vaccinology approach was employed to design a combinatorial vaccine from conserved and antigenic epitopes of essential viral proteins of MARV, namely GP, VP24, VP30, VP35, and VP40 along with an endogenous protein large polymerase (L).

METHODS

Epitopes for T-cell and B-cell were predicted using TepiTool and ElliPro, respectively. The surface-exposed TLRs like TLR2, TLR4, and TLR5 were used to screen high-binding affinity epitopes using the protein-peptide docking platform MdockPeP. The best binding epitopes were selected and assembled with linkers to design a recombinant multi-epitope vaccine construct which was then modeled in Robetta. The in silico biophysical and biochemical analyses of the recombinant vaccine were performed. The docking and MD simulation of the vaccine using WebGro and CABS-Flex against TLRs support the stable binding of vaccine candidates. A virtual immune simulation to check the immediate and long-term immunogenicity was carried out using the C-ImmSim server.

RESULTS

The biochemical characteristics and docking studies with MD simulation establish the recombinant protein vaccine construct MarVax as a stable, antigenic, and potent vaccine molecule. Immune simulation studies reveal 1-year passive immunity which needs to be validated by in vivo studies.

Cross-reactive MHC class I T cell epitopes may dictate heterologous immune responses between respiratory viruses and food allergens

Respiratory virus infections play a major role in asthma, while there is a close correlation between asthma and food allergy. We hypothesized that T cell-mediated heterologous immunity may induce asthma symptoms among sensitized individuals and used two independent in silico pipelines for the identification of cross-reactive virus- and food allergen- derived T cell epitopes, considering individual peptide sequence similarity, MHC binding affinity and immunogenicity. We assessed the proteomes of human rhinovirus (RV1b), respiratory syncytial virus (RSVA2) and influenza-strains contained in the seasonal quadrivalent influenza vaccine 2019/2020 (QIV 2019/2020), as well as SARS-CoV-2 for human HLA alleles, in addition to more than 200 most common food allergen protein sequences. All resulting allergen-derived peptide candidates were subjected to an elaborate scoring system considering multiple criteria, including clinical relevance. In both bioinformatics approaches, we found that shortlisted peptide pairs that are potentially binding to MHC class II molecules scored up to 10 × lower compared to MHC class I candidate epitopes. For MHC class I food allergen epitopes, several potentially cross-reactive peptides from shrimp, kiwi, apple, soybean and chicken were identified. The shortlisted set of peptide pairs may be implicated in heterologous immune responses and translated to peptide immunization strategies with immunomodulatory properties.

Design of a multi-epitope Zika virus vaccine candidate - an in-silico study

Zika virus (ZIKV), an RNA virus, rapidly spreads Aedes mosquito-borne sickness. Currently, there are neither effective vaccines nor therapeutics available to prevent or treat ZIKV infection. In this study, to address these unmet medical needs, we aimed to design B- and T-cell candidate multi-epitope-based subunit against ZIKV using an in silico approach. In this study we applied immunoinformatics, molecular docking, and dynamic simulation assessments targeting the most immunogenic proteins; the capsid (C), envelope (E) proteins and the non-stuctural protein (NS1), described in our previous study, and which predicted immunodominant B and T cell epitopes. The final non-allergenic and highly antigenic multi-epitope was constituted of immunogenic screened-epitopes (3 CTL and 3 HTL) and the β-defensin as an adjuvant that have been linked using EAAAK, AAY, and GPGPG linkers, respectively. The final construct containing 143 amino acids was characterized for its allergenicity, antigenicity, and physiochemical properties; and found to be safe and immunogenic with a good prediction of solubility. The existence of IFN-γ epitopes asserts the capacity to trigger strong immune responses. Subsequently, the molecular docking among vaccine and immune receptors (TLR2/TLR4) was revealed with a good binding affinity with and stable molecular interactions. Molecular dynamics simulation confirmed the stability of the complexes. Finally, the construct was subjected to in silico cloning demonstrating the efficiently of its expression in E.coli. However, this study needs the experimental validation to demonstrate vaccine safety and efficacy.Communicated by Ramaswamy H. Sarma.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster as a Model Disease

For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.

Multi-Epitope Vaccine Design against Monkeypox Virus via Reverse Vaccinology Method Exploiting Immunoinformatic and Bioinformatic Approaches

(1) Background: The monkeypox virus is a zoonotic orthopox DNA virus that is closely linked to the virus. In light of the growing concern about this virus, the current research set out to use bioinformatics and immunoinformatics to develop a potential vaccine against the virus. (2) Methods: A multiepitope vaccine was constructed from the B-cell and T-cell epitopes of the MPXVgp181 strain using adjuvant and different linkers. The constructed vaccine was predicted for antigenicity, allergenicity, toxicity, and population coverage. In silico immune simulation studies were also carried out. Expression analysis and cloning of the constructed vaccine was carried out in the pET-28a(+) vector using snapgene. (3) Results: The constructed vaccine was predicted to be antigenic, non-allergenic, and non-toxic. It was predicted to have excellent global population coverage and produced satisfactory immune response. The in silico expression and cloning studies were successful in E. coli, which makes the vaccine construct suitable for mass production in the pharmaceutical industry. (4) Conclusion: The constructed vaccine is based on the B-cell and T-cell epitopes obtained from the MPXVgp181 strain. This research can be useful in developing a vaccine to combat the monkeypox virus globally after performing in-depth in vitro and in vivo studies.

Proteome Exploration of Legionella pneumophila To Identify Novel Therapeutics: a Hierarchical Subtractive Genomics and Reverse Vaccinology Approach

Legionella pneumophila is a human pathogen distributed worldwide, causing Legionnaires’ disease (LD), a severe form of pneumonia and respiratory tract infection. L. pneumophila is emerging as an antibiotic-resistant strain, and controlling LD is now difficult. Hence, developing novel drugs and vaccines against L. pneumophila is a major research priority.

Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization

Active learning, a subfield of machine learning, can train a good model by selecting a minimum number of labeled samples. In many machine learning scenarios, needed information (such as the best value in unlabeled datasets) is acquired by prediction. When there is too little data in the training model, the prediction accuracy would obviously affect the accuracy of the results. To establish a high-performance regression model for a small dataset while accelerating the search for the best sample, a new active learning query strategy, EGO-ALR, that combines efficient global optimization (EGO) and active learning for regression (ALR) was proposed. It was found that the performance of EGO-ALR was significantly better than the original ALR query strategies in terms of the root mean square error (RMSE), correlation coefficient (CC), and opportunity cost (Oppo Cost). Specifically, EGO-ALR increased the Oppo Cost by an average of 25.27% when the RMSE and CC values were not more than 1.07% different from the original ALR. This study validated the efficiency and robustness of EGO-ALR approaches using 19 datasets from various domains and three distinct linear regression models (Ridge regression, Lasso, and Elastic network).

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant

Spike glycoprotein of SARS-CoV-2 is mainly responsible for the recognition and membrane fusion within the host and this protein has an ability to mutate. Hence, T cell and B cell epitopes were derived from the spike glycoprotein sequence of wild SARS-CoV-2. The proposed T cell and B cell epitopes were found to be antigenic and conserved in the sequence of SARS-CoV-2 mutant (B.1.1.7). Thus, the proposed epitopes are effective against SARS-CoV-2 and its B.1.1.7 mutant. MHC-I that best interacts with the proposed T cell epitopes were found, using immune epitope database. Molecular docking and molecular dynamic simulations were done for ensuring a good binding between the proposed MHC-I and T cell epitopes. The finally proposed T cell epitope was found to be antigenic, non-allergenic, non-toxic and stable. Further, the finally proposed B cell epitopes were also found to be antigenic. The population conservation analysis has ensured the presence of MHC-I molecule (respective to the finally proposed T cell) in human population of most affected countries with SARS-CoV-2. Thus the proposed T and B cell epitope could be effective in designing an epitope-based vaccine, which is effective on SARS-CoV-2 and its B.1.1.7mutant.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13721-021-00348-w.

Immunoinformatic identification of the epitope-based vaccine candidates from Maltoporin, FepA and OmpW of Shigella Spp, with molecular docking confirmation

Shigella is a bacterial pathogen that causes shigellosis, fatal bacillary dysentery, responsible for a higher level of mortality worldwide. We adopted a number of computational approaches to predict potential epitope-based vaccine candidates of immunogenic proteins of Shigella spp. We selected three cell surface proteins of the bacterium according to their antigenicity using the VaxiJen server, including, FepA, Maltoporin, and OmpW. The sequence analyses by the IEDB server resulted in three 15-mer peptides of the core epitope, FTAEHTQSV, FLVNQTLTL, and MRAGSATVR from FepA, Maltoporin, and OmpW, respectively, as the most potential epitopes that have an affinity with both cytotoxic and helper T-cells. Moreover, the epitopes showed 73.76%, 99.0%, and 93.07% world population coverage, along with 100% conservancy among the Shigella subspecies. The molecular docking simulation studies were performed to verify the interactions between the peptides and the respective HLAs. Docking analyses showed that the Epitope-MHC complexes had a higher level of global energy score dictating strong binding. We have also predicted B-cell epitopes from the sequences of these three proteins. In vivo study of the proposed epitope might contribute to the development of a functional and efficient vaccine, which might be an effective way to elude dysentery from the world.

Personalized Neoantigen-Pulsed DC Vaccines: Advances in Clinical Applications

In the past few decades, great progress has been made in the clinical application of dendritic cell (DC) vaccines loaded with personalized neoantigens. Personalized neoantigens are antigens arising from somatic mutations in cancers, with specificity to each patient. DC vaccines work based on the fundamental characteristics of DCs, which are professional antigen-presenting cells (APCs), responsible for the uptake, processing, and presentation of antigens to T cells to activate immune responses. Neoantigens can exert their antitumor effects only after they are taken up by APCs and presented to T cells. In recent years, neoantigen-based personalized tumor therapeutic vaccines have proven to be safe, immunogenic and feasible treatment strategies in patients with melanoma and glioblastoma that provide new hope in the treatment of cancer patients and a new approach to cure cancer. In addition, according to ClinicalTrials.gov, hundreds of registered DC vaccine trials are either completed or ongoing worldwide, of which 9 are in early phase I, 191 in phase I, 166 in phase II and 8 in phase III. Hundreds of clinical studies on therapeutic tumor vaccines globally have proven that DC vaccines are stable, reliable and very safe. However, in this process, many other factors still limit the effectiveness of the vaccine. This review will focus on the current research progress on personalized neoantigen-pulsed DC vaccines, their limitations and future research directions of DC vaccines loaded with neoantigens. This review aims to provide a better understanding of DCs biology and manipulation of activated DCs for DCs researchers to produce the next generation of highly efficient cancer vaccines for patients.

Deciphering Human Leukocyte Antigen Susceptibility Maps From Immunopeptidomics Characterization in Oncology and Infections

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen [HLA] A, B, and C) may affect susceptibility to many diseases such as cancer, auto-immune or infectious diseases. Individual genetic variation may help to explain different immune responses to microorganisms across a population. HLA typing can be fast and inexpensive; however, deciphering peptides loaded on MHC-I and II which are presented to T cells, require the design and development of high-sensitivity methodological approaches and subsequently databases. Hence, these novel strategies and databases could help in the generation of vaccines using these potential immunogenic peptides and in identifying high-risk HLA types to be prioritized for vaccination programs. Herein, the recent developments and approaches, in this field, focusing on the identification of immunogenic peptides have been reviewed and the next steps to promote their translation into biomedical and clinical practice are discussed.

Comparative proteomic analysis to annotate the structural and functional association of the hypothetical proteins of S. maltophilia k279a and predict potential T and B cell targets for vaccination

Stenotrophomonas maltophilia is a multidrug-resistant bacterium with no precise clinical treatment. This bacterium can be a vital cause for death and different organ failures in immune-compromised, immune-competent, and long-time hospitalized patients. Extensive quorum sensing capability has become a challenge to develop new drugs against this pathogen. Moreover, the organism possesses about 789 proteins which function, structure, and pathogenesis remain obscured. In this piece of work, we tried to enlighten the aforementioned sectors using highly reliable bioinformatics tools validated by the scientific community. At first, the whole proteome sequence of the organism was retrieved and stored. Then we separated the hypothetical proteins and searched for the conserved domain with a high confidence level and multi-server validation, which resulted in 24 such proteins. Furthermore, all of their physical and chemical characterizations were performed, such as theoretical isoelectric point, molecular weight, GRAVY value, and many more. Besides, the subcellular localization, protein-protein interactions, functional motifs, 3D structures, antigenicity, and virulence factors were also evaluated. As an extension of this work, 'RTFAMSSER' and 'PAAPQPSAS' were predicted as potential T and B cell epitopes, respectively. We hope our findings will help in better understating the pathogenesis and smoothen the way to the cure.

Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida glabrata: An Immunoinformatics Approach

Background

Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although there are multiple effective vaccines against fungal infections and some of these vaccines are engaged in different stages of clinical trials, none of them have yet been approved by the FDA.

Aim

Using immunoinformatics approach to predict the most conserved and immunogenic B- and T-cell epitopes from the fructose bisphosphate aldolase (Fba1) protein of C. glabrata. Material and Method. 13 C. glabrata fructose bisphosphate aldolase protein sequences (361 amino acids) were retrieved from NCBI and presented in several tools on the IEDB server for prediction of the most promising epitopes. Homology modeling and molecular docking were performed.

Result

The promising B-cell epitopes were AYFKEH, VDKESLYTK, and HVDKESLYTK, while the promising peptides which have high affinity to MHC I binding were AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL, and YFKEHGEPL. Two peptides, LFSSHMLDL and YIRSIAPAY, were noted to have the highest affinity to MHC class II that interact with 9 alleles. The molecular docking revealed that the epitopes QTSNGGAAY and LFSSHMLDL have the lowest binding energy to MHC molecules.

Conclusion

The epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines in that they are more specific, less time consuming, safe, less allergic, and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidate's epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cell epitopes from the Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. glabrata.

Distinguishing Signal From Noise in Immunopeptidome Studies of Limiting-Abundance Biological Samples: Peptides Presented by I-Ab in C57BL/6 Mouse Thymus

Antigen presentation by MHC-II proteins in the thymus is central to selection of CD4 T cells, but analysis of the full repertoire of presented peptides responsible for positive and negative selection is complicated by the low abundance of antigen presenting cells. A key challenge in analysis of limiting abundance immunopeptidomes by mass spectrometry is distinguishing true MHC-binding peptides from co-eluting non-specifically bound peptides present in the mixture eluted from immunoaffinity-purified MHC molecules. Herein we tested several approaches to minimize the impact of non-specific background peptides, including analyzing eluates from isotype-control antibody-conjugated beads, considering only peptides present in nested sets, and using predicted binding motif analysis to identify core epitopes. We evaluated these methods using well-understood human cell line samples, and then applied them to analysis of the I-Ab presented immunopeptidome of the thymus of C57BL/6 mice, comparing this to the more easily characterized splenic B cell and dendritic cell populations. We identified a total of 3473 unique peptides eluted from the various tissues, using a data dependent acquisition strategy with a false-discovery rate of <1%. The immunopeptidomes presented in thymus as compared to splenic B cells and DCs identified shared and tissue-specific epitopes. A broader length distribution was observed for peptides presented in the thymus as compared to splenic B cells or DCs. Detailed analysis of 61 differentially presented peptides indicated a wider distribution of I-Ab binding affinities in thymus as compared to splenic B cells. These results suggest different constraints on antigen processing and presentation pathways in central versus peripheral tissues.

Epitope-based universal vaccine for Human T-lymphotropic virus-1 (HTLV-1)

Human T-cell leukemia virus type 1 (HTLV-1) was the first oncogenic human retrovirus identified in humans which infects at least 10-15 million people worldwide. Large HTLV-1 endemic areas exist in Southern Japan, the Caribbean, Central and South America, the Middle East, Melanesia, and equatorial regions of Africa. HTLV-1 TAX viral protein is thought to play a critical role in HTLV-1 associated diseases. We have used numerous bio-informatics and immuno-informatics implements comprising sequence and construction tools for the construction of a 3D model and epitope prediction for HTLV-1 Tax viral protein. The conformational linear B-cell and T-cell epitopes for HTLV-1 TAX viral protein have been predicted for their possible collective use as vaccine candidates. Based on in silico investigation two B cell epitopes, KEADDNDHEPQISPGGLEPPSEKHFR and DGTPMISGPCPKDGQPS spanning from 324-349 and 252-268 respectively; and T cell epitopes, LLFGYPVYV, ITWPLLPHV and GLLPFHSTL ranging from 11-19, 163-171 and 233-241 were found most antigenic and immunogenic epitopes. Among different vaccine constructs generated by different combinations of these epitopes our predicted vaccine construct was found to be most antigenic with a score of 0.57. T cell epitopes interacted strongly with HLA-A*0201 suggesting a significant immune response evoked by these epitopes. Molecular docking study also showed a high binding affinity of the vaccine construct for TLR4. The study was carried out to predict antigenic determinants of the Tax protein along with the 3D protein modeling. The study revealed a potential multi epitope vaccine that can raise the desired immune response against HTLV-1 and be useful in developing effective vaccines against Human T-lymphotropic virus.

Vaccinomics approach for scheming potential epitope-based peptide vaccine by targeting l-protein of Marburg virus

Marburg virus is one of the world’s most threatening diseases, causing extreme hemorrhagic fever, with a death rate of up to 90%. The Food and Drug Administration (FDA) currently not authorized any treatments or vaccinations for the hindrance and post-exposure of the Marburg virus. In the present study, the vaccinomics methodology was adopted to design a potential novel peptide vaccine against the Marburg virus, targeting RNA-directed RNA polymerase (l). A total of 48 l-proteins from diverse variants of the Marburg virus were collected from the NCBI GenBank server and used to classify the best antigenic protein leading to predict equally T and B-cell epitopes. Initially, the top 26 epitopes were evaluated for the attraction with major histocompatibility complex (MHC) class I and II alleles. Finally, four prospective central epitopes NLSDLTFLI, FRYEFTRHF, YRLRNSTAL, and YRVRNVQTL were carefully chosen. Among these, FRYEFTRHF and YRVRNVQTL peptides showed 100% conservancy. Though YRLRNSTAL showed 95.74% conservancy, it demonstrated the highest combined score as T cell epitope (2.5461) and population coverage of 94.42% among the whole world population. The epitope was found non-allergenic, and docking interactions with human leukocyte antigens (HLAs) also verified. Finally, in vivo analysis of the recommended peptides might contribute to the advancement of an efficient and exclusively prevalent vaccine that would be an active route to impede the virus spreading.

Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses

The outbreak of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a public health emergency. Asthma does not represent a risk factor for COVID-19 in several published cohorts. We hypothesized that the SARS-CoV-2 proteome contains T cell epitopes, which are potentially cross-reactive to allergen epitopes. We aimed at identifying homologous peptide sequences by means of two distinct complementary bioinformatics approaches. Pipeline 1 included prediction of MHC Class I and Class II epitopes contained in the SARS-CoV-2 proteome and allergens along with alignment and elaborate ranking approaches. Pipeline 2 involved alignment of SARS-CoV-2 overlapping peptides with known allergen-derived T cell epitopes. Our results indicate a large number of MHC Class I epitope pairs including known as well as de novo predicted allergen T cell epitopes with high probability for cross-reactivity. Allergen sources, such as Aspergillus fumigatus, Phleum pratense and Dermatophagoides species are of particular interest due to their association with multiple cross-reactive candidate peptides, independently of the applied bioinformatic approach. In contrast, peptides derived from food allergens, as well as MHC class II epitopes did not achieve high in silico ranking and were therefore not further investigated. Our findings warrant further experimental confirmation along with examination of the functional importance of such cross-reactive responses.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation

Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correctness of delineation. Consequently most algorithm development is based on a tiny fraction of the vast amount of imaging data collected at a medical center. Thus, selection of a subset of images from hospital databases for manual delineation - so that algorithms trained on such data are accurate and tolerant to variation, becomes an important challenge. We address this challenge using a novel algorithm. The proposed algorithm named 'Eigenrank by Committee' (EBC) first computes the degree of disagreement between segmentations generated by each DL model in a committee. Then, it iteratively adds to the committee, a DL model trained on cases where the disagreement is maximal. The disagreement between segmentations is quantified by the maximum eigenvalue of a Dice coefficient disagreement matrix a measure closely related to the Von Neumann entropy. We use EBC for selecting data subsets for manual labeling from a larger database of spinal canal segmentations as well as intervertebral disk segmentations. U-Nets trained on these subsets are used to generate segmentations on the remaining data. Similar sized data subsets are also randomly sampled from the respective databases, and U-Nets are trained on these random subsets as well. We found that U-Nets trained using data subsets selected by EBC, generate segmentations with higher average Dice coefficients on the rest of the database than U-Nets trained using random sampling (p < 0.05 using t-tests comparing averages). Furthermore, U-Nets trained using data subsets selected by EBC generate segmentations with a distribution of Dice coefficients that demonstrate significantly (p < 0.05 using Bartlett's test) lower variance in comparison to U-Nets trained using random sampling for all datasets. We believe that this lower variance indicates that U-Nets trained with EBC are more robust than U-Nets trained with random sampling.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

A conserved multi-epitope-based vaccine designed by targeting hemagglutinin protein of highly pathogenic avian H5 influenza viruses

The highly pathogenic avian H5N1 influenza viruses have been recognized as a potential pandemic threat to humans, and to the poultry industry since 1997. H5 viruses consist of a high mutation rate, so universal vaccine designing is very challenging. Here, we describe a vaccinomics approach to design a novel multi-epitope influenza vaccine, based on the highly conserved regions of surface glycoprotein, Hemagglutinin (HA). Initially, the HA protein sequences from Bangladeshi origin were retrieved and aligned by ClustalW. The sequences of 100% conserved regions extracted and analyzed to select the highest potential T-cell and B-cell epitope. The HTL and CTL analyses using IEDB tools showed that DVWTYNAELLVLMEN possesses the highest affinity with MHC class I and II alleles, and it has the highest population coverage. The docking simulation study suggests that this epitope has the potential to interact with both MHC class I and MHC class II. The B-cell epitope prediction provides a potential peptide, GAIAGFIEGGWQGM. We further retrieved HA sequences of 3950 avian and 250 human H5 isolates from several populations of the world, where H5 was an epidemic. Surprisingly, these epitopes are more than 98% conserved in those regions which indicate their potentiality as a conserved vaccine. We have proposed a multi-epitope vaccine using these sequences and assess its stability and potentiality to induce B-cell immunity. In vivo study is necessary to corroborate this epitope as a vaccine, however, setting forth groundwork for wet-lab studies essential to mitigate pandemic threats and provide cross-protection of both avian and humans against H5 influenza viruses.

An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2

COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.

Design of Epitope-Based Peptide Vaccine against Pseudomonas aeruginosa Fructose Bisphosphate Aldolase Protein Using Immunoinformatics

Pseudomonas aeruginosa is a common pathogen that is responsible for serious hospital-acquired infections, ventilator-associated pneumonia, and various sepsis syndromes. Also, it is a multidrug-resistant pathogen recognized for its ubiquity and its intrinsically advanced antibiotic-resistant mechanisms. It usually affects immunocompromised individuals but can also infect immunocompetent individuals. There is no vaccine against it available till now. This study predicts an effective epitope-based vaccine against fructose bisphosphate aldolase (FBA) of Pseudomonas aeruginosa using immunoinformatics tools. The protein sequences were obtained from NCBI, and prediction tests were undertaken to analyze possible epitopes for B and T cells. Three B cell epitopes passed the antigenicity, accessibility, and hydrophilicity tests. Six MHC I epitopes were found to be promising, while four MHC II epitopes were found promising from the result set. Nineteen epitopes were shared between MHC I and II results. For the population coverage, the epitopes covered 95.62% worldwide excluding certain MHC II alleles. We recommend in vivo and in vitro studies to prove its effectiveness.

Epitope-Based Immunoinformatics Approach on Nucleocapsid Protein of Severe Acute Respiratory Syndrome-Coronavirus-2

With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019), as a global pandemic. Additionally, the positive cases are still following an upward trend worldwide and as a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication and interferes with host immune responses. We comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we predicted the most immunogenic epitope for the T-cell and B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis

BACKGROUND

To date, no specific vaccine or drug has been proven to be effective against SARS-CoV-2 infection. Therefore, we implemented an immunoinformatic approach to design an efficient multi-epitopes vaccine against SARS-CoV-2.

RESULTS

The designed-vaccine construct consists of several immunodominant epitopes from structural proteins of spike, nucleocapsid, membrane, and envelope. These peptides promote cellular and humoral immunity and interferon-gamma responses. Also, these epitopes have a high antigenic capacity and are not likely to cause allergies. To enhance the vaccine immunogenicity, we used three potent adjuvants: Flagellin of Salmonella enterica subsp. enterica serovar Dublin, a driven peptide from high mobility group box 1 as HP-91, and human beta-defensin 3 protein. The physicochemical and immunological properties of the vaccine structure were evaluated. The tertiary structure of the vaccine protein was predicted and refined by Phyre2 and Galaxi refine and validated using RAMPAGE and ERRAT. Results of ElliPro showed 246 sresidues from vaccine might be conformational B-cell epitopes. Docking of the vaccine with toll-like receptors (TLR) 3, 5, 8, and angiotensin-converting enzyme 2 approved an appropriate interaction between the vaccine and receptors. Prediction of mRNA secondary structure and in silico cloning demonstrated that the vaccine can be efficiently expressed in Escherichia coli.

CONCLUSION

Our results demonstrated that the multi-epitope vaccine might be potentially antigenic and induce humoral and cellular immune responses against SARS-CoV-2. This vaccine can interact appropriately with the TLR3, 5, and 8. Also, it has a high-quality structure and suitable characteristics such as high stability and potential for expression in Escherichia coli .

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions

The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4⁺ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.

Chimeric Vaccines Designed by Immunoinformatics-Activated Polyfunctional and Memory T Cells That Trigger Protection against Experimental Visceral Leishmaniasis

Many vaccine candidates against visceral leishmaniasis (VL) have been proposed; however, to date, none of them have been efficacious for the human or canine disease. On this basis, the design of leishmaniasis vaccines has been constantly changing, and the use of approaches to select specific epitopes seems to be crucial in this scenario. The ability to predict T cell-specific epitopes makes immunoinformatics an even more necessary approach, as in VL an efficient immune response against the parasite is triggered by T lymphocytes in response to Leishmania spp. immunogenic antigens. Moreover, the success of vaccines depends on the capacity to generate long-lasting memory and polyfunctional cells that are able to eliminate the parasite. In this sense, our study used a combination of different approaches to develop potential chimera candidate vaccines against VL. The first point was to identify the most immunogenic epitopes of Leishmania infantum proteins and construct chimeras composed of Major histocompatibility complex (MHC) class I and II epitopes. For this, we used immunoinformatics features. Following this, we validated these chimeras in a murine model in a thorough memory study and multifunctionality of T cells that contribute to a better elucidation of the immunological protective mechanisms of polyepitope vaccines (chimera A and B) using multicolor flow cytometry. Our results showed that in silico-designed chimeras can elicit polyfunctional T cells producing T helper (Th)1 cytokines, a strong immune response against Leishmania antigen, and the generation of central and effector memory T cells in the spleen cells of vaccinated animals that was able to reduce the parasite burden in this organ. These findings contribute two potential candidate vaccines against VL that can be used in further studies, and help in this complex field of vaccine development against this challenging parasite.

Epitope-Based Peptide Vaccine Against Severe Acute Respiratory Syndrome-Coronavirus-2 Nucleocapsid Protein: An in silico Approach.. [DOI: 10.1101/2020.05.16.100206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. SARS-CoV-2 is a member of the Coronaviridae family, which is transmitted from animal to human and because of being contagious, further it transmitted human to human. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019) as a global pandemic. But, no specific medications are available for the treatment of COVID-19 so far. As a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication as well as interferes with host immune responses. We have comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we have predicted the most immunogenic epitope for T-cell as well as B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual (≥ 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability ≤ 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach

Background

A new endemic disease has spread across Wuhan City, China, in December 2019. Within few weeks, the World Health Organization (WHO) announced a novel coronavirus designated as coronavirus disease 2019 (COVID-19). In late January 2020, WHO declared the outbreak of a “public-health emergency of international concern” due to the rapid and increasing spread of the disease worldwide. Currently, there is no vaccine or approved treatment for this emerging infection; thus, the objective of this study is to design a multiepitope peptide vaccine against COVID-19 using an immunoinformatics approach.

Method

Several techniques facilitating the combination of the immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T-cell epitope-based peptide vaccine using the envelope protein of 2019-nCoV as a target.

Results

Extensive mutations, insertion, and deletion were discovered with comparative sequencing in the COVID-19 strain. Additionally, ten peptides binding to MHC class I and MHC class II were found to be promising candidates for vaccine design with adequate world population coverage of 88.5% and 99.99%, respectively.

Conclusion

The T-cell epitope-based peptide vaccine was designed for COVID-19 using the envelope protein as an immunogenic target. Nevertheless, the proposed vaccine rapidly needs to be validated clinically in order to ensure its safety and immunogenic profile to help stop this epidemic before it leads to devastating global outbreaks.

Analysis of the Role of TpUB05 Antigen from Theileria parva in Immune Responses to Malaria in Humans Compared to Its Homologue in Plasmodium falciparum the UB05 Antigen

Despite the amount of resources deployed and the technological advancements in molecular biology, vaccinology, immunology, genetics, and biotechnology, there are still no effective vaccines against malaria. Immunity to malaria is usually seen to be species- and/or strain-specific. However, there is a growing body of evidence suggesting the possibility of the existence of cross-strain, cross-species, and cross-genus immune responses in apicomplexans. The principle of gene conservation indicates that homologues play a similar role in closely related organisms. The homologue of UB05 in Theileria parva is TpUB05 (XP_763711.1), which has been tested and shown to be associated with protective immunity in East Coast fever. In a bid to identify potent markers of protective immunity to aid malaria vaccine development, TpUB05 was tested in malaria caused by Plasmodium falciparum. It was observed that TpUB05 was better at detecting antigen-specific antibodies in plasma compared to UB05 when tested by ELISA. The total IgG raised against TpUB05 was able to block parasitic growth in vitro more effectively than that raised against UB05. However, there was no significant difference between the two study antigens in recalling peripheral blood mononuclear cell (PBMC) memory through IFN-γ production. This study suggests, for the first time, that TpUB05 from T. parva cross-reacts with UB05 from P. falciparum and is a marker of protective immunity in malaria. Hence, TpUB05 should be considered for possible development as a potential subunit vaccine candidate against malaria.

An exploratory study on the simultaneous screening for residues of chloramphenicol, ciprofloxacin and sulphadimidine using recombinant antibodies

A recombinant gene CCS consisting of three single genes (CAP-ScFv, CPFX-ScFv and SM₂-ScFv) were constructed by PCR with three pairs of primers. The length of CCS gene fragment was 2,260 bp. A recombinant plasmid (pGEX-CCS) was obtained in pGEX-6p-1. pGEX-CCS was induced in E.coli BL21(DE3), and the molecular weight of recombinant fusion protein (GST-CCS) was 108.87 kDa. GST-CCS was successfully applied to analysis of CAP-OVA (CAP-ovalbumin conjugate), CPFX-OVA and SM₂-OVA simultaneously. The sensitivity of GST-CCS against three veterinary drugs was tested by indirect ELISA at dilution ratio 1:8000. These findings provide a foundation for the construction of fusion genes with linkers and for the potential development of a rapid screening method for the simultaneous detection of veterinary drug residues.

Induction of HLA-A2 restricted CD8 T cell responses against ApoB100 peptides does not affect atherosclerosis in a humanized mouse model

Cardiovascular diseases form the most common cause of death worldwide, with atherosclerosis as main etiology. Atherosclerosis is marked by cholesterol rich lipoprotein deposition in the artery wall, evoking a pathogenic immune response. Characteristic for the disease is the pathogenic accumulation of macrophages in the atherosclerotic lesion, which become foam cells after ingestion of large quantities of lipoproteins. We hypothesized that, by inducing a CD8 T cell response towards lipoprotein derived apolipoprotein-B100 (ApoB100), lesional macrophages, that are likely to cross-present lipoprotein constituents, can specifically be eliminated. Based on in silico models for protein processing and MHC-I binding, 6 putative CD8 T cell epitopes derived from ApoB100 were synthesized. HLA-A2 binding was confirmed for all peptides by T2 cell binding assays and recall responses after vaccination with the peptides proved that 5 of 6 peptides could induce CD8 T cell responses. Induction of ApoB100 specific CD8 T cells did not impact plaque size and cellular composition in HLA-A2 and human ApoB100 transgenic LDLr^−/− mice. No recall response could be detected in cultures of cells isolated from the aortic arch, which were observed in cell cultures of splenocytes and mesenteric lymph nodes, suggesting that the atherosclerotic environment impairs CD8 T cell activation.

A comprehensive analysis of amino-peptidase N1 protein (APN) from Anopheles culicifacies for epitope design using Immuno-informatics models

Analysis of the Amino-peptidase N (APN) protein from Anopheles culicifacies as a vector based Transmission Blocking Vaccines (TBV) target has been considered for malaria vaccine development. Short peptides as potential epitopes for B cells and cytotoxic T cells and/or helper T cells were identified using prediction models provided by NetCTL and IEDB servers. Antigenicity determination, allergenicity, immunogenicity, epitope conservancy analysis, atomic interaction with HLA allele specific structure models and population coverage were investigated in this study. The analysis of the target protein helped to identify conserved regions as potential epitopes of APN in various Anopheles species. The T cell epitopes like peptides were further analyzed by using molecular docking to check interactions against the allele specific HLA models. Thus, we report the predicted B cell (VDERYRL) and T cell (RRYLATTQF for HLA class I and LKATFTVSI for HLA class II) epitopes like peptides from APN protein of Anopheles culicifacies (Diptera: Culicidae) for further consideration as vaccine candidates subsequent to in vitro and in vivo analysis.

Structure to function analysis with antigenic characterization of a hypothetical protein,HPAG1_0576 from Helicobacter pylori HPAG1

Helicobacter pylori, a unique gastric pathogen causing chronic inflammation in the gastric mucosa with a possibility to develop gastric cancer has one-third of its proteins still uncharacterized. In this study, a hypothetical protein (HP) namely HPAG1_0576 from H. pylori HPAG1 was chosen for detailed computational analysis of its structural, functional and epitopic properties. The primary, secondary and 3D structure/model of the selected HP was constructed. Then refinement and structure validation were done, which indicated a good quality of the newly constructed model. ProFunc and STRING suggested that HPAG1_0576 shares 98% identity with a carcinogenic factor, TNF-α inducing protein (Tip-α ) of H. pylori. IEDB immunoinformatics tool predicted VLMLQACTCPNTSQRNS from position 19-35 as most potential B-cell linear epitope and SFLKSKQL from position 5-12 as most potent conformational epitope. Alternatively, FALVRARGF and FLCGLGVLM were predicted as most immunogenic CD8+ and CD4+ T-cell epitopes respectively. At the same time findings of IFN epitope tool suggests that, HPAG1_0576 had a great potential to evoke interferon-gamma (IFN-γ) mediated immune response. However, this experiment is a primary approach for in silico vaccine designing from a HP, findings of this study will provide significant insights in further investigations and will assist in identifying new drug targets/vaccine candidates.

Low mutational load in pediatric medulloblastoma still translates into neoantigens as targets for specific T-cell immunotherapy

BACKGROUND

Medulloblastoma is the most common malignant brain tumor in childhood and adolescence. Although some patients present with distinct genetic alterations, such as mutated TP53 or MYC amplification, pediatric medulloblastoma is a tumor entity with minimal mutational load and low immunogenicity.

METHODS

We identified tumor-specific mutations using next-generation sequencing of medulloblastoma DNA and RNA derived from primary tumor samples from pediatric patients. Tumor-specific mutations were confirmed using deep sequencing and in silico analyses predicted high binding affinity of the neoantigen-derived peptides to the patients' human leukocyte antigen molecules. Tumor-specific peptides were synthesized and used to induce a de novo T-cell response characterized by interferon gamma and tumor necrosis factor alpha release of CD8⁺ cytotoxic T cells in vitro.

RESULTS

Despite low mutational tumor burden, at least two immunogenic tumor-specific peptides were identified in each patient. T cells showed a balanced CD4/CD8 ratio and mostly effector memory phenotype. Induction of a CD8-specific T-cell response was achieved for the neoepitopes derived from Histidine Ammonia-Lyase (HAL), Neuraminidase 2 (NEU2), Proprotein Convertase Subtilisin (PCSK9), Programmed Cell Death 10 (PDCD10), Supervillin (SVIL) and tRNA Splicing Endonuclease Subunit 54 (TSEN54) variants.

CONCLUSION

Detection of patient-specific, tumor-derived neoantigens confirms that even in tumors with low mutational load a molecular design of targets for specific T-cell immunotherapy is possible. The identified neoantigens may guide future approaches of adoptive T-cell transfer, transgenic T-cell receptor transfer or tumor vaccination.

Genomic characterization of mumps viruses from a large-scale mumps outbreak in Arkansas, 2016

In 2016, a year-long large-scale mumps outbreak occurred in Arkansas among a highly-vaccinated population. A total of 2954 mumps cases were identified during this outbreak. The majority of cases (1676 (57%)) were school-aged children (5-17 years), 1536 (92%) of these children had completed the mumps vaccination schedule. To weigh the possibility that the mumps virus evaded vaccine-induced immunity in the affected Arkansas population, we established a pipeline for genomic characterization of the outbreak strains. Our pipeline produces whole-genome sequences along with phylogenetic analysis of the outbreak mumps virus strains. We collected buccal swab samples of patients who tested positive for the mumps virus during the 2016 Arkansas outbreak, and used the portable Oxford Nanopore Technology to sequence the extracted strains. Our pipeline identified the genotype of the Arkansas mumps strains as genotype G and presented a genome-based phylogenetic tree with superior resolution to a standard small hydrophobic (SH) gene-based tree. We phylogenetically compared the Arkansas whole-genome sequences to all publicly available mumps strains. While these analyses show that the Arkansas mumps strains are evolutionarily distinct from the vaccine strains, we observed no correlation between vaccination history and phylogenetic grouping. Furthermore, we predicted potential B-cell epitopes encoded by the Arkansas mumps strains using a random forest prediction model trained on antibody-antigen protein structures. Over half of the predicted epitopes of the Jeryl-Lynn vaccine strains in the Hemagglutinin-Neuraminidase (HN) surface glycoprotein (a major target of neutralizing antibodies) region are missing in the Arkansas mumps strains. In-silico analyses of potential epitopes may indicate that the Arkansas mumps strains display antigens with reduced immunogenicity, which may contribute to reduced vaccine effectiveness. However, our in-silico findings should be assessed by robust experiments such as cross neutralization assays. Metadata analysis showed that vaccination history had no effect on the evolution of the Arkansas mumps strains during this outbreak. We conclude that the driving force behind the spread of the mumps virus in the 2016 Arkansas outbreak remains undetermined.