Immunoinformatics prediction of overlapping CD8+ T-cell, IFN-γ and IL-4 inducer CD4+ T-cell and linear B-cell epitopes based vaccines against COVID-19 (SARS-CoV-2)

Peptide vaccine design against glioblastoma by applying immunoinformatics approach

Brain tumors are considered to be one of the most fatal forms of cancer owing to their highly aggressive attributes, diverse characteristics, and notably low rate of survival. Among these tumors, glioblastoma stands out as the prevalent and perilous variant Despite the present advancements in surgical procedures, pharmacological treatment, and radiation therapy, the overall prognosis remains notably unfavorable, as merely 4.3 % of individuals manage to attain a five-year survival rate; For this reason, it has emerged as a challenge for cancer researchers. Therefore, among several immunotherapy methods, using peptide-based vaccines for cancer treatment is considered promising due to their ability to generate a focused immune response with minimal damage. This study endeavors to devise a multi-epitope vaccine utilizing an immunoinformatics methodology to address the challenge posed by glioblastoma disease. Through this approach, it is anticipated that the duration and expenses associated with vaccine manufacturing can be diminished, while simultaneously enhancing the characteristics of the vaccine. The target gene in this research is ITGA5, which was achieved through TCGA analysis by targeting the PI3K-Akt pathway as a significant association with patient survival. Subsequently, the suitable epitopes of T and B cells were selected through various immunoinformatics tools by analyzing their sequence. Then, nine epitopes were merged with GM-CSF as an adjuvant to enhance immunogenicity. The outcomes encompass molecular docking, molecular dynamics (MD) simulation, simulation of the immune response, prognosis and confirmation of the secondary and tertiary structure, Chemical and physical characteristics, toxicity, as well as antigenicity and allergenicity of the potential vaccine candidate against glioblastoma.

Identification of immune-related hub genes and potential molecular mechanisms involved in COVID-19 via integrated bioinformatics analysis

COVID-19, caused by the SARS-CoV-2 virus, poses significant health challenges worldwide, particularly due to severe immune-related complications. Understanding the molecular mechanisms and identifying key immune-related genes (IRGs) involved in COVID-19 pathogenesis is critical for developing effective prevention and treatment strategies. This study employed computational tools to analyze biological data (bioinformatics) and a method for inferring causal relationships based on genetic variations, known as Mendelian randomization (MR), to explore the roles of IRGs in COVID-19. We identified differentially expressed genes (DEGs) from datasets available in the Gene Expression Omnibus (GEO), comparing COVID-19 patients with healthy controls. IRGs were sourced from the ImmPort database. We conducted functional enrichment analysis, pathway analysis, and immune infiltration assessments to determine the biological significance of the identified IRGs. A total of 360 common differential IRGs were identified. Among these genes, CD1C, IL1B, and SLP1 have emerged as key IRGs with potential protective effects against COVID-19. Pathway enrichment analysis revealed that CD1C is involved in terpenoid backbone biosynthesis and Th17 cell differentiation, while IL1B is linked to B-cell receptor signaling and the NF-kappa B signaling pathway. Significant correlations were observed between key genes and various immune cells, suggesting that they influence immune cell modulation in COVID-19. This study provides new insights into the immune mechanisms underlying COVID-19, highlighting the crucial role of IRGs in disease progression. These findings suggest that CD1C and IL1B could be potential therapeutic targets. The integrated bioinformatics and MR analysis approach offers a robust framework for further exploring immune responses in COVID-19 patients, as well as for targeted therapy and vaccine development.

Prediction of antigenic peptides of SARS- CoV-2 pathogen using machine learning

Antigenic peptides (APs), also known as T-cell epitopes (TCEs), represent the immunogenic segment of pathogens capable of inducing an immune response, making them potential candidates for epitope-based vaccine (EBV) design. Traditional wet lab methods for identifying TCEs are expensive, challenging, and time-consuming. Alternatively, computational approaches employing machine learning (ML) techniques offer a faster and more cost-effective solution. In this study, we present a robust XGBoost ML model for predicting TCEs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus as potential vaccine candidates. The peptide sequences comprising TCEs and non-TCEs retrieved from Immune Epitope Database Repository (IEDB) were subjected to feature extraction process to extract their physicochemical properties for model training. Upon evaluation using a test dataset, the model achieved an impressive accuracy of 97.6%, outperforming other ML classifiers. Employing a five-fold cross-validation a mean accuracy of 97.58% was recorded, indicating consistent and linear performance across all iterations. While the predicted epitopes show promise as vaccine candidates for SARS-CoV-2, further scientific examination through in vivo and in vitro studies is essential to validate their suitability.

Effects of the induction of humoral and cellular immunity by third vaccination for SARS-CoV-2

INTRODUCTION

To control the spread of severe disease caused by mutant strains of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), it is necessary to determine whether continued vaccination enhances humoral and cellular immunity.

AIM

In this study, we examined the changes in humoral and cellular immunity to SARS-CoV-2 after administration of the third vaccination in Japanese adults who had received the second dose of messenger ribonucleic acid (mRNA)-1273 vaccine and the third vaccination (BNT162b2 or mRNA-1273).

METHODS

We measured anti-spike antibodies in immunoglobulin G (IgG) and anti-nucleocapsid IgG titers in the serum of the vaccinated subjects. To evaluate cellular immunity, the peripheral blood mononuclear cells of inoculated individuals were cultured with spiked proteins, including those of the SARS-CoV-2 conventional strain and Omicron strain, and then subjected to enzyme-linked immunospot (ELISPOT).

RESULTS

The results revealed that the anti-SARS-CoV-2 spike protein antibody titer increased after the third vaccination and was maintained; however, a decrease was observed at 6 months after vaccination. SARS-CoV-2 antigen-specific T helper (Th)1 and Th2 cell responses were also induced after the third vaccination and were maintained for 6 months after vaccination. Furthermore, induction of cellular immunity against Omicron strains by the omicron non-compliant vaccines, BNT162b2 or mRNA-1273, was observed.

CONCLUSION

These findings demonstrate the effectiveness of vaccination against unknown mutant strains that may occur in the future and provide important insights into vaccination strategies.

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

Mosaic vaccine design targeting mutational spike protein of SAR-SCoV-2: An immunoinformatics approach

INTRODUCTION

Presently, the development of effective vaccines against SARS-CoV-2 is absolutely necessary, especially regarding the emergence of new variants that cause increasing morbidity and fatalities.

METHODS

In the present study we designed a mosaic vaccine targeting the mutational spike protein of COVID-19 using a bioinformatics approach. Various immunoinformatics tools were utilized to provide the highest potential for a mosaic vaccine that could activate immune responses against COVID-19.

RESULTS

The evaluation of the constructed vaccine revealed that it is antigenic and immunogenic as well as nonallergenic. The physicochemical properties also show promising characteristics, including being highly stable and hydrophilic. As expected, the vaccine shows strong interactions with several important receptors including angiotensin-converting enzyme 2 (ACE2), Toll-like receptor 3 (TLR3) and TLR8 by the lowest energy level, docking score and binding free energy. The vaccine binds to receptors via certain amino acids using various types of binding including salt bridges, hydrogen bonds, and other means. As shown in computationally derived models, the interactions promote activation of the immune response by eliciting the release of various cytokines, antibodies, memory B and T cells, as well as increasing of natural killer cell and dendrite cell counts.

CONCLUSION

Therefore, the novel designed mosaic vaccine could be considered as a potential vaccine candidate for immediate production to stem the continuing and tragic effects of the COVID-19 pandemic. However, several advanced experimental studies should be conducted to ensure and verify the effectivity and safety against SARS‑CoV‑2 in vivo.

Immunoinformatics-Based Identification of the Conserved Immunogenic Peptides Targeting of Zika Virus Precursor Membrane Protein

Zika virus infections lead to neurological complications such as congenital Zika syndrome and Guillain-Barré syndrome. Rising Zika infections in newborns and adults have triggered the need for vaccine development. In the current study, the precursor membrane (prM) protein of the Zika virus is explored for its functional importance and design of epitopes enriched conserved peptides with the usage of different immunoinformatics approach. Phylogenetic and mutational analyses inferred that the prM protein is highly conserved. Three conserved peptides containing multiple T and B cell epitopes were designed by employing different epitope prediction algorithms. IEDB population coverage analysis of selected peptides in six different continents has shown the population coverage of 60-99.8% (class I HLA) and 80-100% (class II HLA). Molecular docking of selected peptides/epitopes was carried out with each of class I and II HLA alleles using HADDOCK. A majority of peptide-HLA complex (pHLA) have HADDOCK scores found to be comparable and more than native-HLA complex representing the good binding interaction of peptides to HLA. Molecular dynamics simulation with best docked pHLA complexes revealed that pHLA complexes are stable with RMSD <5.5Å. Current work highlights the importance of prM as a strong antigenic protein and selected peptides have the potential to elicit humoral and cell-mediated immune responses.

Immunoinformatic prediction of potential immunodominant epitopes from cagW in order to investigate protection against Helicobacter pylori infection based on experimental consequences

Helicobacter pylori is a leading cause of stomach cancer and peptic ulcers. Thus, identifying epitopes in H. pylori antigens is important for disease etiology, immunological surveillance, enhancing early detection tests, and developing optimal epitope-based vaccines. We used immunoinformatic and computational methods to create a potential CagW epitope candidate for H. pylori protection. The cagW gene of H. pylori was amplified and cloned into pcDNA3.1 (+) for injection into the muscles of healthy BALB/c mice to assess the impact of the DNA vaccine on interleukin levels. The results will be compared to a control group of mice that received PBS or cagW-pcDNA3.1 (+) vaccinations. An analysis of CagW protein antigens revealed 8 CTL and 7 HTL epitopes linked with AYY and GPGPG, which were enhanced by adding B-defensins to the N-terminus. The vaccine's immunogenicity, allergenicity, and physiochemistry were validated, and its strong activation of TLRs (1, 2, 3, 4, and 10) suggests it is antigenic. An in-silico cloning and immune response model confirmed the vaccine's expression efficiency and predicted its impact on the immune system. An immunofluorescence experiment showed stable and bioactive cagW gene expression in HDF cells after cloning the whole genome into pcDNA3.1 (+). In vivo vaccination showed that pcDNA3.1 (+)-cagW-immunized mice had stronger immune responses and a longer plasmid DNA release window than control-plasmid-immunized mice. After that, bioinformatics methods predicted, developed, and validated the three-dimensional structure. Many online services docked it with Toll-like receptors. The vaccine was refined using allergenicity, antigenicity, solubility, physicochemical properties, and molecular docking scores. Virtual-reality immune system simulations showed an impressive reaction. Codon optimization and in-silico cloning produced E. coli-expressed vaccines. This study suggests a CagW epitopes-protected H. pylori infection. These studies show that the proposed immunization may elicit particular immune responses against H. pylori, but laboratory confirmation is needed to verify its safety and immunogenicity.

Designing multi-epitope vaccine against important colorectal cancer (CRC) associated pathogens based on immunoinformatics approach

BACKGROUND

It seems that several members of intestinal gut microbiota like Streptococcus bovis, Bacteroides fragilis, Helicobacter pylori, Fusobacterium nucleatum, Enterococcus faecalis, Escherichia coli, Peptostreptococcus anaerobius may be considered as the causative agents of Colorectal Cancer (CRC). The present study used bioinformatics and immunoinformatics approaches to design a potential epitope-based multi-epitope vaccine to prevent CRC with optimal population coverage.

METHODS

In this study, ten amino acid sequences of CRC-related pathogens were retrieved from the NCBI database. Three ABCpred, BCPREDS and LBtope online servers were considered for B cells prediction and the IEDB server for T cells (CD4⁺ and CD8⁺) prediction. Then, validation, allergenicity, toxicity and physicochemical analysis of all sequences were performed using web servers. A total of three linkers, AAY, GPGPG, and KK were used to bind CTL, HTL and BCL epitopes, respectively. In addition, the final construct was subjected to disulfide engineering, molecular docking, immune simulation and codon adaptation to design an effective vaccine production strategy.

RESULTS

A total of 19 sequences of different lengths for linear B-cell epitopes, 19 and 18 sequences were considered as epitopes of CD4⁺ T and CD8⁺ cells, respectively. The predicted epitopes were joined by appropriate linkers because they play an important role in producing an extended conformation and protein folding. The final multi-epitope construct and Toll-like receptor 4 (TLR4) were evaluated by molecular docking, which revealed stable and strong binding interactions. Immunity simulation of the vaccine showed significantly high levels of immunoglobulins, helper T cells, cytotoxic T cells and INF-γ.

CONCLUSION

Finally, the results showed that the designed multi-epitope vaccine could serve as an excellent prophylactic candidate against CRC-associated pathogens, but in vitro and animal studies are needed to justify our findings for its use as a possible preventive measure.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Study of potentiality of dexamethasone and its derivatives against Covid-19

In December 2019, COVID-19 epidemic was reported in Wuhan, China, and subsequently the infection has spread all over the world and became pandemic. The death toll associated with the pandemic is increasing day by day in a high rate. Herein, an effort has been made to identify the potentiality of commercially available drugs and also their probable derivatives for creation of better opportunity to make more powerful drugs against coronavirus. This study involves the in-silico interactions of dexamethasone and its derivatives against the multiple proteins of SARS-CoV-2 with the help of various computational methods. Descriptor parameters revealed their non-toxic effect in the human body. Ultimately docking studies and molecular dynamic simulation on those target protein by dexamethasone and its derivatives showed a high binding energy. Dexamethasone showed -9.8 kcal/mol and its derivative D5 showed -12.1 kcal/mol binding energy. Those scores indicate that dexamethasone has more therapeutic effect on SARS CoV-2 than other currently used drugs. Derivatives give the clue for the synthesis of a novel drug to remove SARS CoV-2. Until then, dexamethasone will be used as a potential inhibitor of SARS CoV-2.Communicated by Ramaswamy H. Sarma.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

Identification of vaccine candidate against Omicron variant of SARS-CoV-2 using immunoinformatic approaches

Despite the availability of COVID-19 vaccines, additional more potent vaccines are still required against the emerging variations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the present investigation, we have identified a promising vaccine candidate against the Omicron (B.1.1.529) using immunoinformatics approaches. Various available tools like, the Immune Epitope Database server resource, and NetCTL-1.2, have been used for the identification of the promising T-cell and B-cell epitopes. The molecular docking was performed to check the interaction of TLR-3 receptors and validated 3D model of vaccine candidate. The codon optimization was done followed by cloning using SnapGene. Finally, In-silico immune simulation profile was also checked. The identified T-cell and B-cell epitopes have been selected based on their antigenicity (VaxiJen v2.0) and, allergenicity (AllerTOP v2.0). The identified epitopes with antigenic and non-allergenic properties were fused with the specific peptide linkers. In addition, the 3D model was constructed by the PHYRE2 server and validated using ProSA-web. The validated 3D model was further docked with the Toll-like receptor 3 (TLR3) and showed good interaction with the amino acids which indicate a promising vaccine candidate against the Omicron variant of SARS-CoV-2. Finally, the codon optimization, In-silico cloning and immune simulation profile was found to be satisfactory. Overall, the designed vaccine candidate has a potential against variant of SARS-Cov-2. However, further experimental studies are required to confirm.

Nanotechnology-facilitated vaccine development during the coronavirus disease 2019 (COVID-19) pandemic

Coronavirus disease 2019 (COVID-19) continually poses a significant threat to the human race, and prophylactic vaccination is the most potent approach to end this pandemic. Nanotechnology is widely adopted during COVID-19 vaccine development, and the engineering of nanostructured materials such as nanoparticles has opened new possibilities in innovative vaccine development by improving the design and accelerating the development process. This review aims to comprehensively understand the current situation and prospects of nanotechnology-enabled vaccine development against the COVID-19 pandemic, with an emphasis on the interplay between nanotechnology and the host immune system.

Longitudinal Assessment of SARS-CoV-2-Specific T Cell Cytokine-Producing Responses for 1 Year Reveals Persistence of Multicytokine Proliferative Responses, with Greater Immunity Associated with Disease Severity

Cell-mediated immunity is critical for long-term protection against most viral infections, including coronaviruses. We studied 23 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected survivors over a 1-year post-symptom onset (PSO) interval by ex vivo cytokine enzyme-linked immunosorbent spot assay (ELISpot) assay. All subjects demonstrated SARS-CoV-2-specific gamma interferon (IFN-γ), interleukin 2 (IL-2), and granzyme B (GzmB) T cell responses at presentation, with greater frequencies in severe disease. Cytokines, mainly produced by CD4⁺ T cells, targeted all structural proteins (nucleocapsid, membrane, and spike) except envelope, with GzmB and IL-2 greater than IFN-γ. Mathematical modeling predicted that (i) cytokine responses peaked at 6 days for IFN-γ, 36 days for IL-2, and 7 days for GzmB, (ii) severe illness was associated with reduced IFN-γ and GzmB but increased IL-2 production rates, and (iii) males displayed greater production of IFN-γ, whereas females produced more GzmB. Ex vivo responses declined over time, with persistence of IL-2 in 86% and of IFN-γ and GzmB in 70% of subjects at a median of 336 days PSO. The average half-life of SARS-CoV-2-specific cytokine-producing cells was modeled to be 139 days (~4.6 months). Potent T cell proliferative responses persisted throughout observation, were CD4 dominant, and were capable of producing all 3 cytokines. Several immunodominant CD4 and CD8 epitopes identified in this study were shared by seasonal coronaviruses or SARS-CoV-1 in the nucleocapsid and membrane regions. Both SARS-CoV-2-specific CD4⁺ and CD8⁺ T cell clones were able to kill target cells, though CD8 tended to be more potent. IMPORTANCE Our findings highlight the relative importance of SARS-CoV-2-specific GzmB-producing T cell responses in SARS-CoV-2 control and shared CD4 and CD8 immunodominant epitopes in seasonal coronaviruses or SARS-CoV-1, and they indicate robust persistence of T cell memory at least 1 year after infection. Our findings should inform future strategies to induce T cell vaccines against SARS-CoV-2 and other coronaviruses.

Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms

The outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause for the ongoing global public health emergency. It is more commonly known as coronavirus disease 2019 (COVID-19); the pandemic threat continues to spread aroundthe world with the fluctuating emergence of its new variants. The severity of COVID-19 ranges from asymptomatic to serious acute respiratory distress syndrome (ARDS), which has led to a high human mortality rate and disruption of socioeconomic well-being. For the restoration of pre-pandemic normalcy, the international scientific community has been conducting research on a war footing to limit extremely pathogenic COVID-19 through diagnosis, treatment, and immunization. Since the first report of COVID-19 viral infection, an array of laboratory-based and point-of-care (POC) approaches have emerged for diagnosing and understanding its status of outbreak. The RT-PCR-based viral nucleic acid test (NAT) is one of the rapidly developed and most used COVID-19 detection approaches. Notably, the current forbidding status of COVID-19 requires the development of safe, targeted vaccines/vaccine injections (shots) that can reduce its associated morbidity and mortality. Massive and accelerated vaccination campaigns would be the most effective and ultimate hope to end the COVID-19 pandemic. Since the SARS-CoV-2 virus outbreak, emerging biotechnologies and their multidisciplinary approaches have accelerated the understanding of molecular details as well as the development of a wide range of diagnostics and potential vaccine candidates, which are indispensable to combating the highly contagious COVID-19. Several vaccine candidates have completed phase III clinical studies and are reported to be effective in immunizing against COVID-19 after their rollout via emergency use authorization (EUA). However, optimizing the type of vaccine candidates and its route of delivery that works best to control viral spread is crucial to face the threatening variants expected to emerge over time. In conclusion, the insights of this review would facilitate the development of more likely diagnostics and ideal vaccines for the global control of COVID-19.

Possibility of COVID-19 eradication with evolution of a new omicron variant

COVID-19 has been a global health concern since 2019 until date. Global concerted efforts to combat this pandemic has resulted in a number of vaccines distributed across the globe. Although the presence of these vaccines produced quick interventions, dynamic mutation in the causal virus and the continuous evolution of new stains that defy available vaccines has given rise to pertinent questions. The most recent emergence of a new COVID-19 virus variant (omicron), the rapid spread and overwhelming rate of morbidity and mortality has reopened these questions for debate. This commentary summarizes major view on these questions and concludes that multiple approach including social behavioural measures, vaccination and antiviral drug would speed up elimination process.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

A multi-epitope chimeric protein elicited a strong antibody response and partial protection against Edwardsiella ictaluri in Nile tilapia

Edwardsiella ictaluri infects several fish species and protection of the all the susceptible fish hosts from the pathogen using a monovalent vaccine is impossible because the species is composed of host-based genotypes that are genetic, serological and antigenic heterogenous. Here, immunoinformatic approach was employed to design a cross-immunogenic chimeric EiCh protein containing multi-epitopes. The chimeric EiCh protein is composed of 11 B-cell epitopes and 7 major histocompatibility complex class II epitopes identified from E. ictaluri immunogenic proteins previously reported. The 49.32 kDa recombinant EiCh protein was expressed in vitro in Escherichia coli BL-21 (DE3) after which inclusion bodies were successfully solubilized and refolded. Ab initio protein modelling revealed secondary and tertiary structures. Secondary structure was confirmed by circular dichroism spectroscopy. Antigenicity of the chimeric EiCh protein was exhibited by strong reactivity with serum from striped catfish and Nile tilapia experimentally infected with E. ictaluri. Furthermore, immunogenicity of the chimeric EiCh protein was investigated in vivo in Nile tilapia juveniles and it was found that the protein could strongly induce production of specific antibodies conferring agglutination activity and partially protected Nile tilapia juveniles with a relative survival percentage (RPS) of 42%. This study explored immunoinformatics as reverse vaccinology approach in vaccine design for aquaculture to manage E. ictaluri infections.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Genome-Wide Profiling Reveals Alternative Polyadenylation of Innate Immune-Related mRNA in Patients With COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has caused many deaths worldwide. To date, the mechanism of viral immune escape remains unclear, which is a great obstacle to developing effective clinical treatment. RNA processing mechanisms, including alternative polyadenylation (APA) and alternative splicing (AS), are crucial in the regulation of most human genes in many types of infectious diseases. Because the role of APA and AS in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains unknown, we performed de novo identification of dynamic APA sites using a public dataset of human peripheral blood mononuclear cell (PBMC) RNA-Seq data in COVID-19 patients. We found that genes with APA were enriched in innate immunity -related gene ontology categories such as neutrophil activation, regulation of the MAPK cascade and cytokine production, response to interferon-gamma and the innate immune response. We also reported genome-wide AS events and enriched viral transcription-related categories upon SARS-CoV-2 infection. Interestingly, we found that APA events may give better predictions than AS in COVID-19 patients, suggesting that APA could act as a potential therapeutic target and novel biomarker in those patients. Our study is the first to annotate genes with APA and AS in COVID-19 patients and highlights the roles of APA variation in SARS-CoV-2 infection.

Computational Design and Analysis of a Multi-epitope Against Influenza A virus

Influenza A viruses are among the most studied viruses, however no effective prevention against influenza infection has been developed. So, designing an effective vaccine against Influenza A virus is a critical issue in the field of medical biotechnology. For this reason, to combat this disease, we have designed a novel multi-epitope vaccine candidate based on the several conserved and potential linear B-cell and T-cell binding epitopes by using the in silico approach. This vaccine consists of an ER signal conserved sequence, the PADRE conserved epitope and two conserved epitopes of Influenza matrix protein 2. T-cell binding epitopes from Matrix protein 2 were predicted by in silico tools of epitope prediction. The selected epitopes were joined by flexible linkers and physicochemical properties, toxicity, and allergenecity were investigated. The designed vaccine was antigenic, immunogenic, and non-allergenic with suitable physicochemical properties and has higher solubility. The final multi-epitope construct was modeled, confirmed by different programs and the molecular interactions with immune receptors were considered. The molecular docking assay indicated the interactions with immune-stimulatory toll-like receptor 3 (TLR3) and major histocompatibility complex class I (MHCI). The HADDOCK and H DOCK servers were used to make docking analysis, respectively. The docking analysis indicated a strong and stable binding interaction between the vaccine construct with major histocompatibility complex (MHC) class I and toll-like receptor 3. Overall, the findings suggest that the current vaccine may be a promising vaccine to prevent Influenza infection.

Towards an Ensemble Vaccine against the Pegivirus Using Computational Modelling Approaches and Its Validation through In Silico Cloning and Immune Simulation

Pegivirus, HPgV, which was earlier known as Gb virus and hepatitis G virus, is an enveloped, positive-stranded RNA and lymphotropic virus classified into the Flaviviridae family. The transmission routes primarily involve blood products, and infections are worldwide, leading up to 25% of persistent infections. To date, no effective therapeutic means are available to clear Pegivirus infections. Effective vaccine therapeutics is the best alternative to manage this disease and any associated potential pandemic. Thus, whole proteome-based mining of immunogenic peptides, i.e., CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), and B cell epitopes, was mapped to design a vaccine ensemble. Our investigation revealed that 29 different epitopes impart a critical role in immune response induction, which was also validated by exploring its physiochemical properties and experimental feasibility. In silico expression and host immune simulation were examined using an agent-based modeling approach and confirmed the induction of both primary and secondary immune factors such as IL, cytokines, and antibodies. The current study warrants further lab experiments to demonstrate its efficacy and safety.

Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research

Presently, immunoinformatics and bioinformatics approaches are contributing actively to COVID-19 vaccine research. The first immunoinformatics-based vaccine construct against SARS-CoV-2 was published in February 2020. Following this, immunoinformatics and bioinformatics approaches have created a new direction in COVID-19 vaccine research. Several researchers have designed the next-generation COVID-19 vaccines using these approaches. Presently, immunoinformatics has accelerated immunology research immensely in the area of COVID-19. Hence, we have tried to depict the current scenario of immunoinformatics and bioinformatics in COVID-19 vaccine research.

Nano-Enabled COVID-19 Vaccines: Meeting the Challenges of Durable Antibody Plus Cellular Immunity and Immune Escape

At the time of preparing this Perspective, large-scale vaccination for COVID-19 is in progress, aiming to bring the pandemic under control through vaccine-induced herd immunity. Not only does this vaccination effort represent an unprecedented scientific and technological breakthrough, moving us from the rapid analysis of viral genomes to design, manufacture, clinical trial testing, and use authorization within the time frame of less than a year, but it also highlights rapid progress in the implementation of nanotechnology to assist vaccine development. These advances enable us to deliver nucleic acid and conformation-stabilized subunit vaccines to regional lymph nodes, with the ability to trigger effective humoral and cellular immunity that prevents viral infection or controls disease severity. In addition to a brief description of the design features of unique cationic lipid and virus-mimicking nanoparticles for accomplishing spike protein delivery and presentation by the cognate immune system, we also discuss the importance of adjuvancy and design features to promote cooperative B- and T-cell interactions in lymph node germinal centers, including the use of epitope-based vaccines. Although current vaccine efforts have demonstrated short-term efficacy and vaccine safety, key issues are now vaccine durability and adaptability against viral variants. We present a forward-looking perspective of how vaccine design can be adapted to improve durability of the immune response and vaccine adaptation to overcome immune escape by viral variants. Finally, we consider the impact of nano-enabled approaches in the development of COVID-19 vaccines for improved vaccine design against other infectious agents, including pathogens that may lead to future pandemics.

Immunoinformatics approach for multi-epitope vaccine design against P. falciparum malaria

Plasmodium falciparum (P. falciparum) is a leading causative agent of malaria, an infectious disease that can be fatal. Unfortunately, control measures are becoming less effective over time. A vaccine is needed to effectively control malaria and lead towards the total elimination of the disease. There have been multiple attempts to develop a vaccine, but to date, none have been certified as appropriate for wide-scale use. In this study, an immunoinformatics method is presented to design a multi-epitope vaccine construct predicted to be effective against P. falciparum malaria. This was done through the prediction of 12 CD4+ T-cell, 10 CD8+ T-cell epitopes and, 1 B-cell epitope which were assessed for predicted high antigenicity, immunogenicity, and non-allergenicity through in silico methods. The Human Leukocyte Antigen (HLA) population coverage showed that the alleles associated with the epitopes accounted for 78.48% of the global population. The CD4+ and CD8+ T-cell epitopes were docked to HLA-DRB1*07:01 and HLA-A*32:01 successfully. Therefore, the epitopes were deemed to be suitable as components of a multi-epitope vaccine construct. Adjuvant RS09 was added to the construct to generate a stronger immune response, as confirmed by an immune system simulation. Finally, the structural stability of the predicted multi-epitope vaccine was assessed using molecular dynamics simulations. The results show a promising vaccine design that should be further synthesised and assessed for its efficacy in an experimental laboratory setting.