Designing Multi-Epitope Vaccines to Combat Emerging Coronavirus Disease 2019 (COVID-19) by Employing Immuno-Informatics Approach

Formulation of next-generation polyvalent vaccine candidates against three important poxviruses by targeting DNA-dependent RNA polymerase using an integrated immunoinformatics and molecular modeling approach

BACKGROUND

Poxviruses comprise a group of large double-stranded DNA viruses and are known to cause diseases in humans, livestock animals, and other animal species. The Mpox virus (MPXV; formerly Monkeypox), variola virus (VARV), and volepox virus (VPXV) are among the prevalent poxviruses of the Orthopoxviridae genera. The ongoing Mpox infectious disease pandemic caused by the Mpox virus has had a major impact on public health across the globe. To date, only limited repurposed antivirals and vaccines are available for the effective treatment of Mpox and other poxviruses that cause contagious diseases.

METHODS

The present study was conducted with the primary goal of formulating multi-epitope vaccines against three evolutionary closed poxviruses i.e., MPXV, VARV, and VPXV using an integrated immunoinformatics and molecular modeling approach. DNA-dependent RNA polymerase (DdRp), a potential vaccine target of poxviruses, has been used to determine immunodominant B and T-cell epitopes followed by interactions analysis with Toll-like receptor 2 at the atomic level.

RESULTS

Three multi-epitope vaccine constructs, namely DdRp_MPXV (V1), DdRp_VARV (V2), and DdRp_VPXV (V3) were designed. These vaccine constructs were found to be antigenic, non-allergenic, non-toxic, and soluble with desired physicochemical properties. Protein-protein docking and interaction profiling analysis depicts a strong binding pattern between the targeted immune receptor TLR2 and the structural models of the designed vaccine constructs, and manifested a number of biochemical bonds (hydrogen bonds, salt bridges, and non-bonded contacts). State-of-the-art all-atoms molecular dynamics simulations revealed highly stable interactions of vaccine constructs with TLR2 at the atomic level throughout the simulations on 300 nanoseconds. Additionally, the outcome of the immune simulation analysis suggested that designed vaccines have the potential to induce protective immunity against targeted poxviruses.

CONCLUSIONS

Taken together, formulated next-generation polyvalent vaccines were found to have good efficacy against closely related poxviruses (MPXV, VARV, and VPXV) as demonstrated by our extensive immunoinformatics and molecular modeling evaluations; however, further experimental investigations are still needed.

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Development and assessment of a multiepitope synthetic antigen for the diagnosis of Dengue virus infection

Immunodiagnostic tests for detecting dengue virus infections encounter challenges related to cross-reactivity with other related flaviviruses. Our research focuses on the development of a synthetic multiepitope antigen tailored for dengue immunodiagnostics. Selected dengue epitopes involved structural linearity and dissimilarity from the proteomes of Zika and Yellow fever viruses which served for computationally modeling the three-dimensional protein structure, resulting in the design of two proteins: rDME-C and rDME-BR. Both proteins consist of seven epitopes, separated by the GPGPG linker, and a carboxy-terminal 6 × -histidine tag. The molecular weights of the final proteins rDME-C and rDME-BR are 16.83 kDa and 16.80 kDa, respectively, both with an isoelectric point of 6.35. The distinguishing factor between the two proteins lies in the origin of their epitope sequences, where rDME-C is based on the reference dengue proteome, while rDME-BR utilizes sequences from prevalent Dengue genotypes in Brazil from 2008 to 2019. PyMol analysis revealed exposure of epitopes in the secondary structure. Successful expression of the antigens was achieved in soluble form and fluorescence experiments indicated a disordered structure. In subsequent testing, rDME-BR and rDME-C antigens were assessed using an indirect Elisa protocol against Dengue infected serum, previously examined with a commercial diagnostic test. Optimal concentrations for antigens were determined at 10 µg/mL for rDME-BR and 30 µg/mL for rDME-C, with serum dilutions ranging from 1:50 to 1:100. Both antigens effectively detected IgM and IgG antibodies in Dengue fever patients, with rDME-BR exhibiting higher sensitivity. Our in-house test showed a sensitivity of 77.3 % and 82.6 % and a specificity of 89.4 % and 71.4 % for rDME-C and rDEM-BR antigens. No cross-reactivity was observed with serum from Zika-infected mice but with COVID-19 serum samples. Our findings underscore the utility of synthetic biology in crafting Dengue-specific multiepitope proteins and hold promise for precise clinical diagnosis and monitoring responses to emerging Dengue vaccines.

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.

Designing a Multiple-Epitope Vaccine Candidate against Leishmania major and Leishmania infantum for Monocyte-Derived Exosome Preparation

Background

Leishmania is a vector-borne protozoon, which causes visceral, cutaneous and mucocutaneous leishmaniosis in human and animals. Monocyte-derived exosome vaccines can be used as prophylaxis and immunotherapy strategies. The aim of this study was to design a multiple-epitope candidate vaccine using leishmaniolysin (GP63) and rK39 proteins against Leishmania major and L. infantum for monocyte-derived exosome preparation.

Methods

This study was carried out in Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran, 2023-2024. Effective immunodominant epitopes were selected from two antigenic proteins of GP63 and rK39 using various immunoinformatics and bioinformatics approaches. Vibrio cholerae β-subunit was used as an adjuvant to stimulate immune responses. Then, appropriate linkers were selected for the fusion of epitopes. The 3D model of candidate vaccine was predicted and validated.

Results

This designed candidate vaccine could effectively be used as a prophylaxis strategy against leishmaniosis.

Conclusion

A candidate vaccine was designed using bioinformatic and immunoinformatic studies with virtual acceptable quality; however, effectiveness of this vaccine should be verified through further in-vitro and in-vivo studies.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Immunoinformatics-aided rational design of multiepitope-based peptide vaccine (MEBV) targeting human parainfluenza virus 3 (HPIV-3) stable proteins

BACKGROUND

Human parainfluenza viruses (HPIVs) are common RNA viruses responsible for respiratory tract infections. Human parainfluenza virus 3 (HPIV-3) is particularly pathogenic, causing severe illnesses with no effective vaccine or therapy available.

RESULTS

The current study employed a systematic immunoinformatic/reverse vaccinology approach to design a multiple epitope-based peptide vaccine against HPIV-3 by analyzing the virus proteome. On the basis of a number of therapeutic features, all three stable and antigenic proteins with greater immunological relevance, namely matrix protein, hemagglutinin neuraminidase, and RNA-directed RNA polymerase L, were chosen for predicting and screening suitable T-cell and B-cell epitopes. All of our desired epitopes exhibited no homology with human proteins, greater population coverage (99.26%), and high conservancy among reported HPIV-3 isolates worldwide. All of the T- and B-cell epitopes are then joined by putative ligands, yielding a 478-amino acid-long final construct. Upon computational refinement, validation, and thorough screening, several programs rated our peptide vaccine as biophysically stable, antigenic, allergenic, and non-toxic in humans. The vaccine protein demonstrated sufficiently stable interaction as well as binding affinity with innate immune receptors TLR3, TLR4, and TLR8. Furthermore, codon optimization and virtual cloning of the vaccine sequence in a pET32a ( +) vector showed that it can be readily expressed in the bacterial system.

CONCLUSION

The in silico designed HPIV-3 vaccine demonstrated potential in evoking an effective immune response. This study paves the way for further preclinical and clinical evaluation of the vaccine, offering hope for a future solution to combat HPIV-3 infections.

Computational identification of a multi-peptide vaccine candidate in E2 glycoprotein against diverse Hepatitis C virus genotypes

Hepatitis C Virus (HCV) is estimated to affect nearly 180 million people worldwide, culminating in ∼0.7 million yearly casualties. However, a safe vaccine against HCV is not yet available. This study endeavored to identify a multi-genotypic, multi-epitopic, safe, and globally competent HCV vaccine candidate. We employed a consensus epitope prediction strategy to identify multi-epitopic peptides in all known envelope glycoprotein (E2) sequences, belonging to diverse HCV genotypes. The obtained peptides were screened for toxicity, allergenicity, autoimmunity and antigenicity, resulting in two favorable peptides viz., P2 (VYCFTPSPVVVG) and P3 (YRLWHYPCTV). Evolutionary conservation analysis indicated that P2 and P3 are highly conserved, supporting their use as part of a designed multi-genotypic vaccine. Population coverage analysis revealed that P2 and P3 are likely to be presented by >89% Human Leukocyte Antigen (HLA) molecules from six geographical regions. Indeed, molecular docking predicted the physical binding of P2 and P3 to various representative HLAs. We designed a vaccine construct using these peptides and assessed its binding to toll-like receptor 4 (TLR-4) by molecular docking and simulation. Subsequent analysis by energy-based and machine learning tools predicted high binding affinity and pinpointed the key binding residues (i.e. hotspots) in P2 and P3. Also, a favorable immunogenic profile of the construct was predicted by immune simulations. We encourage the scientific community to validate our vaccine construct in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Computer-Aided Multi-Epitope Based Vaccine Design Against Monkeypox Virus Surface Protein A30L: An Immunoinformatics Approach

Monkeypox, a viral zoonotic disease resembling smallpox, has emerged as a significant national epidemic primarily in Africa. Nevertheless, the recent global dissemination of this pathogen has engendered apprehension regarding its capacity to metamorphose into a sweeping pandemic. To effectively combat this menace, a multi-epitope vaccine has been meticulously engineered with the specific aim of targeting the cell envelope protein of Monkeypox virus (MPXV), thereby stimulating a potent immunological response while mitigating untoward effects. This new vaccine uses T-cell and B-cell epitopes from a highly antigenic, non-allergenic, non-toxic, conserved, and non-homologous A30L protein to provide protection against the virus. In order to ascertain the vaccine design with the utmost efficacy, protein-protein docking methodologies were employed to anticipate the intricate interactions with Toll-like receptors (TLR) 2, 3, 4, 6, and 8. This meticulous approach led the researchers to discern an optimal vaccine architecture, bolstered by affirmative prognostications derived from both molecular dynamics (MD) simulations and immune simulations. The current research findings indicate that the peptides ATHAAFEYSK, FFIVVATAAV, and MNSLSIFFV exhibited antigenic properties and were determined to be non-allergenic and non-toxic. Through the utilization of codon optimization and in-silico cloning techniques, our investigation revealed that the prospective vaccine exhibited a remarkable expression level within Escherichia coli. Moreover, upon conducting immune simulations, we observed the induction of a robust immune response characterized by elevated levels of both B-cell and T-cell mediated immunity. Moreover, as the initial prediction with in-silico techniques has yielded promising results these epitope-based vaccines can be recommended to in vitro and in silico studies to validate their immunogenic properties.

A new candidate epitope-based vaccine against PspA PhtD of Streptococcus pneumoniae: a computational experimental approach

Introduction

Pneumococcus is an important respiratory pathogen that is associated with high rates of death in newborn children and the elderly. Given the disadvantages of current polysaccharide-based vaccines, the most promising alternative for developing improved vaccines may be to use protein antigens with different roles in pneumococcus virulence. PspA and PhtD, highly immunogenic surface proteins expressed by almost all pneumococcal strains, are capable of eliciting protective immunity against lethal infections.

Methods

In this study using immunoinformatics approaches, we constructed one fusion construct (called PAD) by fusing the immunodominant regions of PspA from families 1 & 2 (PA) to the immunodominant regions of PhtD (PD). The objective of this project was to test the immunogenicity of the fusion protein PAD and to compare its protective activity against S. pneumoniae infection with PA or PD alone and a combination of PA and PD. The prediction of physicochemical properties, antigenicity, allergenicity, toxicity, and 3D-structure of the constructs, as well as molecular docking with HLA receptor and immune simulation were performed using computational tools. Finally, mice were immunized and the serum levels of antibodies/cytokines and functionality of antibodies in vitro were evaluated after immunization. The mice survival rates and decrease of bacterial loads in the blood/spleen were examined following the challenge.

Results

The computational analyses indicated the proposed constructs could be antigenic, non-allergenic, non-toxic, soluble and able to elicit robust immune responses. The results of actual animal experiments revealed the candidate vaccines could induce the mice to produce high levels of antibodies and cytokines. The complement-mediated bactericidal activity of antibodies was confirmed and the antibodies provided favorable survival in immunized mice after bacterial challenge. In general, the experimental results verified the immunoinformatics studies.

Conclusion

For the first time this report presents novel peptide-based vaccine candidates consisting of immunodominant regions of PspA and PhtD antigens. The obtained findings confirmed that the fusion formulation could be relatively more efficient than the individual and combination formulations. The results propose that the fusion protein alone could be used as a serotype-independent pneumococcal vaccine or as an effective partner protein for a conjugate polysaccharide vaccine.

Designing of Multi-Epitope Peptide Vaccine Based on Outer Membrane Proteins OmpF, OmpC, and PgtE of Salmonella entericaTyphi

Consumption of contaminated water and foods by Salmonella Typhi cause the most common enteric disease known as Typhoid fever in both humans and animals. Despite the existence of various vaccines but infectious diseases remain a major cause of mortality worldwide. Nowadays, in-silico tools design a reliable and stable vaccine to combat such infections. The study aimed to design and evaluate a multi-epitope vaccine based on the outer-membrane proteins of Salmonella Typhi. B-cells and T-cells epitopes were predicted. Predicted epitopes were connected by AAY, KK, and GPGPG linkers. Heparin-Binding Hemagglutinin Adhesin (HBHA) has been attached to the N-terminal of the final vaccine as a potent immune adjuvant. Epitope's antigenicity, allergenicity, immunogenicity, and physicochemical characteristics were defined using in-silico tools. Molecular docking of vaccine-TLR4 was done. ∆G of vaccine-TLR4 is -3.91×104 Kcal mol-1 with 1.93 RMSD. The results indicated protein was stable and non-allergen. In conclusion, the multi-epitope vaccine base on outer membrane proteins of the Salmonella Typhi bacterium might be considered to combat typhoid fever.

Serological Conversion through a Second Exposure to Inactivated Foot-and-Mouth Disease Virus Expressing the JC Epitope on the Viral Surface

Foot-and-mouth disease (FMD) is a fatal contagious viral disease that affects cloven-hoofed animals and causes severe economic damage at the national level. There are seven serotypes of the causative foot-and-mouth disease virus (FMDV), and type O is responsible for serious outbreaks and shows a high incidence. Recently, the Cathay, Southeast Asia (SEA), and ME-SA (Middle East-South Asia) topotypes of type O have been found to frequently occur in Asia. Thus, it is necessary to develop candidate vaccines that afford protection against these three different topotypes. In this study, an experimental FMD vaccine was produced using a recombinant virus (TWN-JC) with the JC epitope (VP1 140-160 sequence of the O/SKR/Jincheon/2014) between amino acid 152 and 153 of VP1 in TWN-R. Immunization with this novel vaccine candidate was found to effectively protect mice against challenge with the three different topotype viruses. Neutralizing antibody titers were considerably higher after a second vaccination. The serological differences between the topotype strains were identified in guinea pigs and swine. In conclusion, a significant serological difference was observed at 56 days post-vaccination between animals that received the TWN-JC vaccine candidate and those that received the positive control virus (TWN-R). The TWN-JC vaccine candidate induced IFNγ and IL-12B.

Immunoinformatic and reverse vaccinology-based designing of potent multi-epitope vaccine against Marburgvirus targeting the glycoprotein

Marburg virus (MARV) has been a major concern since its first outbreak in 1967. Although the deadly BSL-4 pathogen has been reported in few individuals with sporadic outbreaks following 1967, its rarity commensurate the degree of disease severity. The virus has been known to cause extreme hemorrhagic fever presenting flu-like symptoms (as implicated in COVID-19) with a 90% case fatality rate (CFR). After a number of plausible evidences, it has been observed that the virus usually originates from African fruit bat, Rousettus aegyptiacus, who themselves do not indicate any signs of illness. Thus, efforts have been made in the recent years for a universal treatment of the infection, but till date, no such vaccine or therapeutics could circumvent the viral pathogenicity. In an attempt to formulate a vaccine design computationally, we have explored the entire proteome of the virus and found a strong correlation of its glycoprotein (GP) in receptor binding and subsequent role in infection progression. The present study, explores the MARV glycoprotein GP1 and GP2 domains for quality epitopes to elicit an extended immune response design potential vaccine construct using appropriate linkers and adjuvants. Finally, the chimeric vaccine wass evaluated for its binding affinity towards the receptors via molecular docking and molecular dynamics simulation studies. The rare, yet deadly zoonotic infection with mild outbreaks in recent years has flustered an alarming future with various challenges in terms of viral diseases. Thus, our study has aimed to provide novel insights to design potential vaccines by using the predictive framework.

Validation of Multi-epitope Peptides Encapsulated in PLGA Nanoparticles Against Influenza A Virus

BACKGROUND

Influenza is a highly contagious respiratory disease which poses a serious threat to public health globally, causing severe diseases in 3-5 million humans and resulting in 650,000 deaths annually. The current licensed seasonal influenza vaccines lacked cross-reactivity against novel emerging influenza strains as they conferred limited neutralising capabilities. To address the issue, we designed a multi-epitope peptide-based vaccine delivered by the self-adjuvanting PLGA nanoparticles against influenza infections.

METHODS

A total of six conserved peptides representing B- and T-cell epitopes of Influenza A were identified and they were formulated in either incomplete Freund's adjuvant containing CpG ODN 1826 or being encapsulated in PLGA nanoparticles for the evaluation of immunogenicity in BALB/c mice.

RESULTS

The self-adjuvanting PLGA nanoparticles encapsulating the six conserved peptides were capable of eliciting the highest levels of IgG and IFN- γ producing cells. In addition, the immunogenicity of the six peptides encapsulated in PLGA nanoparticles showed greater humoral and cellular mediated immune responses elicited by the mixture of six naked peptides formulated in incomplete Freund's adjuvant containing CpG ODN 1826 in the immunized mice. Peptide 3 from the mixture of six peptides was found to exert necrotic effect on CD3+ T-cells and this finding indicated that peptide 3 should be removed from the nanovaccine formulation.

CONCLUSION

The study demonstrated the self-adjuvanting properties of the PLGA nanoparticles as a delivery system without the need for incorporation of toxic and costly conventional adjuvants in multi-epitope peptide-based vaccines.

Structural and non-structural proteins in SARS-CoV-2: potential aspects to COVID-19 treatment or prevention of progression of related diseases

Coronavirus disease 2019 (COVID-19) is caused by a new member of the Coronaviridae family known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There are structural and non-structural proteins (NSPs) in the genome of this virus. S, M, H, and E proteins are structural proteins, and NSPs include accessory and replicase proteins. The structural and NSP components of SARS-CoV-2 play an important role in its infectivity, and some of them may be important in the pathogenesis of chronic diseases, including cancer, coagulation disorders, neurodegenerative disorders, and cardiovascular diseases. The SARS-CoV-2 proteins interact with targets such as angiotensin-converting enzyme 2 (ACE2) receptor. In addition, SARS-CoV-2 can stimulate pathological intracellular signaling pathways by triggering transcription factor hypoxia-inducible factor-1 (HIF-1), neuropilin-1 (NRP-1), CD147, and Eph receptors, which play important roles in the progression of neurodegenerative diseases like Alzheimer's disease, epilepsy, and multiple sclerosis, and multiple cancers such as glioblastoma, lung malignancies, and leukemias. Several compounds such as polyphenols, doxazosin, baricitinib, and ruxolitinib could inhibit these interactions. It has been demonstrated that the SARS-CoV-2 spike protein has a stronger affinity for human ACE2 than the spike protein of SARS-CoV, leading the current study to hypothesize that the newly produced variant Omicron receptor-binding domain (RBD) binds to human ACE2 more strongly than the primary strain. SARS and Middle East respiratory syndrome (MERS) viruses against structural and NSPs have become resistant to previous vaccines. Therefore, the review of recent studies and the performance of current vaccines and their effects on COVID-19 and related diseases has become a vital need to deal with the current conditions. This review examines the potential role of these SARS-CoV-2 proteins in the initiation of chronic diseases, and it is anticipated that these proteins could serve as components of an effective vaccine or treatment for COVID-19 and related diseases. Video Abstract.

An immunoinformatics approach to study the epitopes of SARS-CoV-2 helicase, Nsp13

Introduction and objective.

Vaccines are administered worldwide to control on-going coronavirus disease-19 (COVID-19) pandemic caused by SARS-CoV-2. Vaccine efficacy is largely contributed by the epitopes present on the viral proteins and their alteration might help emerging variants to escape host immune surveillance. Therefore, this study was designed to study SARS-CoV-2 Nsp13 protein, its epitopes and evolution.

Methods

Clustal Omega was used to identify mutations in Nsp13 protein. Secondary structure and disorder score was predicted by CFSSP and PONDR-VSL2 webservers. Protein stability was predicted by DynaMut webserver. B cell epitopes were predicted by IEDB DiscoTope 2.0 tools and their 3D structures were represented by discovery studio. Antigenicity and allergenicity of epitopes were predicted by Vaxijen2.0 and AllergenFPv.1.0. Physiochemical properties of epitopes were predicted by Toxinpred, HLP webserver tool.

Results

Our data revealed 182 mutations in Nsp13 among Indian SARS-CoV-2 isolates, which were characterised by secondary structure and per-residue disorderness, stability and dynamicity predictions. To correlate the functional impact of these mutations, we characterised the most prominent B cell and T cell epitopes contributed by Nsp13. Our data revealed twenty-one epitopes, which exhibited antigenicity, stability and interactions with MHC class-I and class-II molecules. Subsequently, the physiochemical properties of these epitopes were analysed. Furthermore, eighteen mutations reside in these Nsp13 epitopes.

Conclusions

We report appearance of eighteen mutations in the predicted twenty-one epitopes of Nsp13. Among these, at least seven epitopes closely matches with the functionally validated epitopes. Altogether, our study shows the pattern of evolution of Nsp13 epitopes and their probable implications.

Chimeric Hepatitis B core virus-like particles harboring SARS-CoV2 epitope elicit a humoral immune response in mice

BACKGROUND

Virus-like particles are an interesting vector platform for vaccine development. Particularly, Hepatitis B virus core antigen has been used as a promising VLP platform. It is highly expressed in different recombinant expression systems, such as E. coli, and self-assembled in vitro. It effectively improves the immunogenicity of foreign antigenic epitopes on its surface. Various foreign antigens from bacteria, viruses, and protozoa can be genetically inserted into such nanoparticles. The effective immunogenicity due to VLP vaccines has been reported. However, no research has been performed on the SARS-CoV2 vaccine within this unique platform through genetic engineering. Considering the high yield of target proteins, low cost of production, and feasibility of scaling up, E. coli is an outstanding expression platform to develop such vaccines. Therefore, in this investigation, we planned to study and develop a unique HBc VLP-based vaccine against SARS-Cov2 utilizing the E. coli expression system due to its importance.

RESULTS

Insertion of the selected epitope was done into the major immunodominant region (MIR) of truncated (149 residues) hepatitis B core capsid protein. The chimeric protein was constructed in PET28a⁺ and expressed through the bacterial E. coli BL21 expression system. However, the protein was expressed in inclusion body forms and extracted following urea denaturation from the insoluble phase. Following the extraction, the vaccine protein was purified using Ni2 + iminodiacetic acid (IDA) affinity chromatography. SDS-PAGE and western blotting were used to confirm the protein expression. Regarding the denaturation step, the unavoidable refolding process was carried out, so that the chimeric VLP reassembled in native conformation. Based on the transmission electron microscopy (TEM) analysis, the HBC VLP was successfully assembled. Confirming the assembled chimeric VLP, we explored the immunogenic effectivity of the vaccine through mice immunization with two-dose vaccination with and without adjuvant. The utilization of adjuvant was suggested to assess the effect of adjuvant on improving the immune elicitation of chimeric VLP-based vaccine. Immunization analysis based on anti-spike specific IgG antibody showed a significant increase in antibody production in harvested serum from immunized mice with HBc-VLP harboring antigenic epitope compared to HBc-VLP- and PBS-injected mice.

CONCLUSIONS

The results approved the successful production and the effectiveness of the vaccine in terms of humoral IgG antibody production. Therefore, this platform can be considered a promising strategy for developing safe and reasonable vaccines; however, more complementary immunological evaluations are needed.

Immunoinformatics-aided design of a new multi-epitope vaccine adjuvanted with domain 4 of pneumolysin against Streptococcus pneumoniae strains

BACKGROUND

Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus.

RESULTS

The B- and T-cell epitopes from highly protective antigens PspA (clades 1-5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA-web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector.

CONCLUSION

The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.

Exploring staphylococcal superantigens to design a potential multi-epitope vaccine against Staphylococcus aureus: an in-silico reverse vaccinology approach

Staphylococcus aureus is a horrifying bacteria capable of causing millions of deaths yearly across the globe. A major contribution to the success of S. aureus as an ESKAPE pathogen is the abundance of virulence factors that can manipulate the innate and adaptive immune system of the individual. Currently, no vaccine is available to treat S. aureus-mediated infections. In this study, we present in-silico approaches to design a stable, safe and immunogenic vaccine that could help to control the infections associated with the bacteria. Three vital pathogenic secreted toxins of S. aureus, such as staphylococcal enterotoxin A (SEA), staphylococcal enterotoxin B (SEB), Toxic-shock syndrome toxin (TSST-1), were selected using the reverse vaccinology approach to design the multi-epitope vaccine (MEV). Linear B-lymphocyte, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. For designing the multi-epitope vaccine (MEV), B-cell epitopes were joined with the KK linker, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Finally, to increase the immune response to the vaccine, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminus of the MEV construct. The final MEV was found to be antigenic and non-allergen in nature. In-silico immune simulation and cloning analysis predicted the immune-stimulating potential of the designed MEV construct along with the cloning feasibility in the pET28a(+) vector with the E. coli expression system. This immunoinformatics study provides a platform for designing a suitable, safe and effective vaccine against S. aureus.Communicated by Ramaswamy H. Sarma.

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.

In silico design of a multi-epitope vaccine against the spike and the nucleocapsid proteins of the Omicron variant of SARS-CoV-2

Computational studies can comprise an effective approach to treating and preventing viral infections. Since 2019, the world has been dealing with the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most important achievement in this short period of time in the effort to reduce morbidity and mortality was the production of vaccines and effective antiviral drugs. Although the virus has been significantly suppressed, it continues to evolve, spread, and evade the host's immune system. Recently, researchers have turned to immunoinformatics tools to reduce side effects and save the time and cost of traditional vaccine production methods. In the present study, an attempt has been made to design a multi-epitope vaccine with humoral and cellular immune response stimulation against the Omicron variant of SARS-CoV-2 by investigating new mutations in spike (S) and nucleocapsid (N) proteins. The population coverage of the vaccine was evaluated as appropriate compared to other studies. The results of molecular dynamics simulation and molecular mechanics/generalized Born surface area (MM/GBSA) calculations predict the stability and proper interaction of the vaccine with Toll-like receptor 4 (TLR-4) as an innate immune receptor. The results of the immune simulation show a significant increase in the coordinated response of IgM and IgG after the third injection of the vaccine. Also, in the continuation of the research, spike proteins from BA.4 and BA.5 lineages were screened by immunoinformatics filters and effective epitopes were suggested for vaccine design. Despite the high precision of computational studies, in-vivo and in-vitro research is needed for final confirmation.Communicated by Ramaswamy H. Sarma.

Immunoinformatics Approaches in Designing Vaccines Against COVID-19

Since the onset of the COVID-19 pandemic, a number of approaches have been adopted by the scientific communities for developing efficient vaccine candidate against SARS-CoV-2. Conventional approaches of developing a vaccine require a long time and a series of trials and errors which indeed limit the feasibility of such approaches for developing a dependable vaccine in an emergency situation like the COVID-19 pandemic. Hitherto, most of the available vaccines have been developed against a particular antigen of SARS-CoV, spike protein in most of the cases, and intriguingly, these vaccines are not effective against all the pathogenic coronaviruses. In this context, immunoinformatics-based reverse vaccinology approaches enable a robust design of efficacious peptide-based vaccines against all the infectious strains of coronaviruses within a short frame of time. In this chapter, we enumerate the methodological trajectory of developing a universal anti-SARS-CoV-2 vaccine, namely, "AbhiSCoVac," through advanced computational biology-based immunoinformatics approach and its in-silico validation using molecular dynamics simulations.

Understanding immunity to influenza: implications for future vaccine development

INTRODUCTION

Influenza virus changes its genotype through antigenic drift or shift making it difficult to develop immunity to infection or vaccination. Zoonotic influenza A virus (IAV) strains can become established in humans. Several impediments to human infection and transmission include sialic acid expression, host anti-viral factors (including interferons), and other elements that govern viral replication. Controlling influenza infection, replication, and transmission is important because IAVs cause annual epidemics and occasional pandemics. Effective seasonal influenza vaccines exist, but these vaccines do not fully protect against novel or pandemic strains.

AREAS COVERED

With new vaccine production technology, vaccines can be produced rapidly. Universal IAV vaccines are being developed to protect against seasonal, novel, and zoonotic IAVs. These efforts are being enhanced and accelerated by a better understanding the host immune response to influenza viruses.

EXPERT OPINION

This review discusses several implications for future influenza vaccine development. Host immune responses to influenza virus infection or vaccination can guide vaccine development as anti-influenza immunity is affected by responses influenced by the previous immune history including first and subsequent exposures to influenza virus infections and vaccinations.

Association of Toll-like receptor-4 polymorphism with SARS CoV-2 infection in Kurdish Population

Genetic variations are critical for understanding clinical outcomes of infections including server acute respiratory syndrome coronavirus 2 (SARS CoV-2). The immunological reactions of human immune genes with SARS CoV-2 have been under investigation. Toll-like receptors (TLRs), a group of proteins, are important for microbial detections including bacteria and viruses. TLR4 can sense both bacterial lipopolysaccharides (LPS) and endogenous oxidized phospholipids triggered by Covid-19 infection. Two TLR4 single nucleotide polymorphisms (SNPs), Asp299Gly and Thr399Ile have been linked to infectious diseases. No studies have focused on these SNPs in association with Covid-19. This study aims to reveal the association between Covid-19 infection with these SNPs by comparing a group of patients and a general population. Restriction fragment length polymorphisms (RFLP) were used to identify the TLR4 SNPs in both the general population (n = 114) and Covid-19 patient groups (n = 125). The results found no association between the TLR4 polymorphisms and Covid-19 infections as the data showed no statistically significant difference between the compared groups. This suggested that these TLR4 SNPs may not be associated with Covid-19 infections.

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.

A subtractive proteomics and immunoinformatics approach towards designing a potential multi-epitope vaccine against pathogenic Listeriamonocytogenes

Listeria monocytogenes is the causative agent of listeriosis, which is dangerous for pregnant women, the elderly or individuals with a weakened immune system. Individuals with leukaemia, cancer, HIV/AIDS, kidney transplant and steroid therapy suffer from immunological damage are menaced. World Health Organization (WHO) reports that human listeriosis has a high mortality rate of 20-30% every year. To date, no vaccine is available to treat listeriosis. Thereby, it is high time to design novel vaccines against L. monocytogenes. Here, we present computational approaches to design an antigenic, stable and safe vaccine against the L. monocytogenes that could help to control the infections associated with the pathogen. Three vital pathogenic proteins of L. monocytogenes, such as Listeriolysin O (LLO), Phosphatidylinositol-specific phospholipase C (PI-PLC), and Actin polymerization protein (ActA), were selected using a subtractive proteomics approach to design the multi-epitope vaccine (MEV). A total of 5 Cytotoxic T-lymphocyte (CTL) and 9 Helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. To design the multi-epitope vaccine (MEV) from the selected proteins, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Additionally, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminal side of the final MEV construct to increase the immune response to the vaccine. The final MEV was predicted to be antigenic, non-allergen and non-toxic in nature. Physicochemical property analysis suggested that the MEV construct is stable and could be easily purified through the E. coli expression system. This in-silico study showed that MEV has a robust binding interaction with Toll-like receptor 2 (TLR2), a key player in the innate immune system. Current subtractive proteomics and immunoinformatics study provides a background for designing a suitable, safe and effective vaccine against pathogenic L. monocytogenes.

Designing multi-epitope-based vaccine targeting surface immunogenic protein of Streptococcus agalactiae using immunoinformatics to control mastitis in dairy cattle

Background

Milk provides energy as well as the basic nutrients required by the body. In particular, milk is beneficial for bone growth and development in children. Based on scientific evidence, cattle milk is an excellent and highly nutritious dietary component that is abundant in vitamins, calcium, potassium, and protein, among other minerals. However, the commercial productivity of cattle milk is markedly affected by mastitis. Mastitis is an economically important disease that is characterized by inflammation of the mammary gland. This disease is frequently caused by microorganisms and is detected as abnormalities in the udder and milk. Streptococcus agalactiae is a prominent cause of mastitis. Antibiotics are rarely used to treat this infection, and other available treatments take a long time to exhibit a therapeutic effect. Vaccination is recommended to protect cattle from mastitis. Accordingly, the present study sought to design a multi-epitope vaccine using immunoinformatics.

Results

The vaccine was designed to be antigenic, immunogenic, non-toxic, and non-allergic, and had a binding affinity with Toll-like receptor 2 (TLR2) and TLR4 based on structural modeling, docking, and molecular dynamics simulation studies. Besides, the designed vaccine was successfully expressed in E. coli. expression vector (pET28a) depicts its easy purification for production on a larger scale, which was determined through in silico cloning. Further, immune simulation analysis revealed the effectiveness of the vaccine with an increase in the population of B and T cells in response to vaccination.

Conclusion

This multi-epitope vaccine is expected to be effective at generating an immune response, thereby paving the way for further experimental studies to combat mastitis.

Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine Against Clostridium difficile

Clostridium difficile (C.difficile) is an exclusively anaerobic, spore-forming, and Gram-positive pathogen that is the most common cause of nosocomial diarrhea and is becoming increasingly prevalent in the community. Because C. difficile is strictly anaerobic, spores that can survive for months in the external environment contribute to the persistence and diffusion of C. difficile within the healthcare environment and community. Antimicrobial therapy disrupts the natural intestinal flora, allowing spores to develop into propagules that colonize the colon and produce toxins, thus leading to antibiotic-associated diarrhea and pseudomembranous enteritis. However, there is no licensed vaccine to prevent Clostridium difficile infection (CDI). In this study, a multi-epitope vaccine was designed using modern computer methods. Two target proteins, CdeC, affecting spore germination, and fliD, affecting propagule colonization, were chosen to construct the vaccine so that it could simultaneously induce the immune response against two different forms (spore and propagule) of C. difficile. We obtained the protein sequences from the National Center for Biotechnology Information (NCBI) database. After the layers of filtration, 5 cytotoxic T-cell lymphocyte (CTL) epitopes, 5 helper T lymphocyte (HTL) epitopes, and 7 B-cell linear epitopes were finally selected for vaccine construction. Then, to enhance the immunogenicity of the designed vaccine, an adjuvant was added to construct the vaccine. The Prabi and RaptorX servers were used to predict the vaccine's two- and three-dimensional (3D) structures, respectively. Additionally, we refined and validated the structures of the vaccine construct. Molecular docking and molecular dynamics (MD) simulation were performed to check the interaction model of the vaccine-Toll-like receptor (TLR) complexes, vaccine-major histocompatibility complex (MHC) complexes, and vaccine-B-cell receptor (BCR) complex. Furthermore, immune stimulation, population coverage, and in silico molecular cloning were also conducted. The foregoing findings suggest that the final formulated vaccine is promising against the pathogen, but more researchers are needed to verify it.

In silico designed Staphylococcus aureus B-cell multi-epitope vaccine did not elicit antibodies against target antigens suggesting multi-domain approach

The vaccine development strategies have evolved from using an entire organism as an immunogen to a single antigen and further towards an epitope. Since an epitope is a relatively tiny and immunologically relevant part of an antigen, it has the potential to stimulate more robust and specific immune responses while causing minimal adverse effects. As a result, the recent focus of vaccine development has been to develop multi-epitope vaccines that can target multiple virulence mechanisms. Accordingly, we designed multi-epitope vaccine candidates B (multi-B-cell epitope immunogen) and CTB-B (an adjuvant - cholera toxin subunit B (CTB) - attached to immunogen B) against S. aureus by employing immunoinformatics approaches. The designed vaccines are composed of B-cell epitope segments (20-mer) of the eight well-characterized S. aureus virulence factors, namely ClfB, FnbpA, Hla, IsdA, IsdB, LukE, SdrD, and SdrE connected in series. The designed vaccines were expressed, purified, and administered to C57BL/6 mice with Freund adjuvant to evaluate the immunogenicity and protective efficacy. The results revealed that the immunized mice showed high IgG titer for the immunogen, and the antibody titer increased significantly following the second immunization. However, the generated antibodies did not protect mice from infection. The interaction of anti-B antibodies with source virulence factors showed that the generated antibodies have no binding affinity with any of the corresponding virulence factors. Our results demonstrate the limitation of in silico designed B-cell multi-epitope vaccine and suggest that a protein domain carrying both linear and conformational B-cell epitopes might be a better choice for developing an effective multi-epitope vaccine against S. aureus.

COVID-19: A systematic review and update on prevention, diagnosis, and treatment

Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).

Cytotoxic T-Cell-Based Vaccine against SARS-CoV-2: A Hybrid Immunoinformatic Approach

This paper presents an alternative vaccination platform that provides long-term cellular immune protection mediated by cytotoxic T-cells. The immune response via cellular immunity creates superior resistance to viral mutations, which are currently the greatest threat to the global vaccination campaign. Furthermore, we also propose a safer, more facile, and physiologically appropriate immunization method using either intranasal or oral administration. The underlying technology is an adaptation of synthetic long peptides (SLPs) previously used in cancer immunotherapy. The overall quality of the SLP constructs was validated using in silico methods. SLPs comprising HLA class I and class II epitopes were designed to stimulate antigen cross-presentation and canonical class II presentation by dendritic cells. The desired effect is a cytotoxic T cell-mediated prompt and specific immune response against the virus-infected epithelia and a rapid and robust virus clearance. Epitopes isolated from COVID-19 convalescent patients were screened for HLA class I and class II binding (NetMHCpan and NetMHCIIpan) and highest HLA population coverage (IEDB Population Coverage). 15 class I and 4 class II epitopes were identified and used for this SLP design. The constructs were characterized based on their toxicity (ToxinPred), allergenicity (AllerCatPro), immunogenicity (VaxiJen 2.0), and physico-chemical parameters (ProtParam). Based on in silico predictions, out of 60 possible SLPs, 36 candidate structures presented a high probability to be immunogenic, non-allergenic, non-toxic, and stable. 3D peptide folding followed by 3D structure validation (PROCHECK) and molecular docking studies (HADDOCK 2.4) with Toll-like receptors 2 and 4 provided positive results, suggestive for favorable antigen presentation and immune stimulation.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

Immunoinformatics and reverse vaccinomic approaches for effective design

The emergence of mutagenic strains of severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) worst hit the world which already suffered from the Coronavirus disease-2019 (COVID-19) pandemic for 2 years. Due to recent advances in vaccinomics, many vaccine candidates are available but their efficacy against a mutant version of SARS-CoV-2 has remained uncertain. The immune-informatics-based reverse vaccinomic approaches have shown promising investigations recently for the development of cost-effective vaccinomics candidates in a very short period of time. The strategic vaccine development of selected epitopes using artificial intelligence for both B- and T-cells is a very crucial step in this process. This approach provides a highly effective and immunogenic vaccine that offers immunological safety against autoimmunity and other adverse effects over ethnicities, pregnant women, and vulnerable age groups. Several researchers have developed effective vaccine candidates using computational vaccinology and the immune-informatics approach. In this process, a unique peptide sequence of viral proteins such as Nucleocapsid, spike, envelope protein was identified by various in silico tools which are acting as immunological epitopes against TLRs, T-cells, and B-cells. While the conventional immunological vaccine studies take years for vaccine candidature, the immunoinformatics approach is a time-efficient way for the next generation research to study host-pathogen interactions and vaccine development. It is also cost-effective and leads to a better understanding of disease pathogenesis, diagnosis, and immunological response. Owing to the advantage of immunoinformatics-based vaccine approaches the present chapter aimed to discuss vaccine development using immunoinformatics approaches. Besides, the current challenges and future aspects have also been discussed herewith.

COVID-19: Origin, epidemiology, virology, pathogenesis, and treatment

COVID-19 (or Coronavirus Disease) originated in China (Hubei provenance, Wuhan city). The first recorded illness occurred in December 2019. It has affected all parts of the world, and the WHO designated the COVID-19 disease, caused by the new Coronavirus SARS-CoV-2, a pandemic on March 11, 2020. Some debatable speculations indicate that it is a man-made virus, intentionally synthesized in the laboratory but was unintentionally emancipated from a laboratory of Wuhan, China. The primitive theory suggested the spread from the Hunan seafood market of China probably from an animal source. However, this theory is not fully supported. COVID-19 infection has a varying range of signs and symptoms from low fever, dry cough to lower respiratory tract infection, breathing difficulties, pulmonary edema, acute respiratory distress syndrome (ARDS), metabolic acidosis, sepsis, coagulation, lymphopenia, hypoxemia, multiorgan failure, and eventually, mortality. In patients with comorbidity such as diabetes, cardiovascular disease, high blood pressure, stroke, and kidney disease, fatality rate is higher. Young and elderly people are more likely to experience unfavorable outcomes due to poor immunity. There have been several treatment methods explored to tackle the COVID-19 pandemic, including medications, interferon, vaccines, oligonucleotides, peptides, and monoclonal and immunomodulatory antibodies, among other things. The World Health Organization has recommended preventive measures like washing hands, using face masks, sanitizers, and maintaining a safe distance to prevent the spread of the pandemic. One of the promising alternatives is the vaccine. One must take all preventive measures in the pandemic until it becomes feeble.

Immunoinformatics mapping of potential epitopes in SARS-CoV-2 structural proteins

All approved coronavirus disease 2019 (COVID-19) vaccines in current use are safe, effective, and reduce the risk of severe illness. Although data on the immunological presentation of patients with COVID-19 is limited, increasing experimental evidence supports the significant contribution of B and T cells towards the resolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Despite the availability of several COVID-19 vaccines with high efficacy, more effective vaccines are still needed to protect against the new variants of SARS-CoV-2. Employing a comprehensive immunoinformatic prediction algorithm and leveraging the genetic closeness with SARS-CoV, we have predicted potential immune epitopes in the structural proteins of SARS-CoV-2. The S and N proteins of SARS-CoV-2 and SARS-CoVs are main targets of antibody detection and have motivated us to design four multi-epitope vaccines which were based on our predicted B- and T-cell epitopes of SARS-CoV-2 structural proteins. The cardinal epitopes selected for the vaccine constructs are predicted to possess antigenic, non-allergenic, and cytokine-inducing properties. Additionally, some of the predicted epitopes have been experimentally validated in published papers. Furthermore, we used the C-ImmSim server to predict effective immune responses induced by the epitope-based vaccines. Taken together, the immune epitopes predicted in this study provide a platform for future experimental validations which may facilitate the development of effective vaccine candidates and epitope-based serological diagnostic assays.

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.

In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19

The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial.

Vaccinomics: a future avenue for vaccine development against emerging pathogens

INTRODUCTION

Vaccines are a major achievement in medical sciences, but the development of more effective vaccines against infectious diseases is essential for prevention and control of emerging pathogens worldwide. The application of omics technologies has advanced vaccinology through the characterization of host-vector-pathogen molecular interactions and the identification of candidate protective antigens. However, major challenges such as host immunity, pathogen and environmental factors, vaccine efficacy and safety need to be addressed. Vaccinomics provides a platform to address these challenges and improve vaccine efficacy and safety.

AREAS COVERED

In this review, we summarize current information on vaccinomics and propose quantum vaccinomics approaches to further advance vaccine development through the identification and combination of antigen protective epitopes, the immunological quantum. The COVID-19 pandemic caused by SARS-CoV-2 is an example of emerging infectious diseases with global impact on human health.

EXPERT OPINION

Vaccines are required for the effective and environmentally sustainable intervention for the control of emerging infectious diseases worldwide. Recent advances in vaccinomics provide a platform to address challenges in improving vaccine efficacy and implementation. As proposed here, quantum vaccinomics will contribute to vaccine development, efficacy, and safety by facilitating antigen combinations to target pathogen infection and transmission in emerging infectious diseases.

Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understanding the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. Results indicate that the immune recognition potential of SARS-CoV2 epitopes tend to vary between different ethnic groups. While the South Asians are likely to recognize higher number of CD8-specific epitopes, Europeans are likely to identify higher number of CD4-specific epitopes. We also hypothesize and provide clues that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gaging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ∼40,000 viral genomes.

Immunogenic amino acid motifs and linear epitopes of COVID-19 mRNA vaccines

Reverse vaccinology is an evolving approach for improving vaccine effectiveness and minimizing adverse responses by limiting immunizations to critical epitopes. Towards this goal, we sought to identify immunogenic amino acid motifs and linear epitopes of the SARS-CoV-2 spike protein that elicit IgG in COVID-19 mRNA vaccine recipients. Paired pre/post vaccination samples from N = 20 healthy adults, and post-vaccine samples from an additional N = 13 individuals were used to immunoprecipitate IgG targets expressed by a bacterial display random peptide library, and preferentially recognized peptides were mapped to the spike primary sequence. The data identify several distinct amino acid motifs recognized by vaccine-induced IgG, a subset of those targeted by IgG from natural infection, which may mimic 3-dimensional conformation (mimotopes). Dominant linear epitopes were identified in the C-terminal domains of the S1 and S2 subunits (aa 558-569, 627-638, and 1148-1159) which have been previously associated with SARS-CoV-2 neutralization in vitro and demonstrate identity to bat coronavirus and SARS-CoV, but limited homology to non-pathogenic human coronavirus. The identified COVID-19 mRNA vaccine epitopes should be considered in the context of variants, immune escape and vaccine and therapy design moving forward.

Immunoinformatics Approach to Design Multi-Epitope- Subunit Vaccine against Bovine Ephemeral Fever Disease

Bovine ephemeral fever virus (BEFV) is an overlooked pathogen, recently gaining widespread attention owing to its associated enormous economic impacts affecting the global livestock industries. High endemicity with rapid spread and morbidity greatly impacts bovine species, demanding adequate attention towards BEFV prophylaxis. Currently, a few suboptimum vaccines are prevailing, but were confined to local strains with limited protection. Therefore, we designed a highly efficacious multi-epitope vaccine candidate targeted against the geographically distributed BEFV population. By utilizing immunoinformatics technology, all structural proteins were targeted for B- and T-cell epitope prediction against the entire allele population of BoLA molecules. Prioritized epitopes were adjoined by linkers and adjuvants to effectively induce both cellular and humoral immune responses in bovine. Subsequently, the in silico construct was characterized for its physicochemical parameters, high immunogenicity, least allergenicity, and non-toxicity. The 3D modeling, refinement, and validation of ligand (vaccine construct) and receptor (bovine TLR7) then followed molecular docking and molecular dynamic simulation to validate their stable interactions. Moreover, in silico cloning of codon-optimized vaccine construct in the prokaryotic expression vector (pET28a) was explored. This is the first time HTL epitopes have been predicted using bovine datasets. We anticipate that the designed construct could be an effective prophylactic remedy for the BEF disease that may pave the way for future laboratory experiments.

The Clinical Information Systems Response to the COVID-19 Pandemic

OBJECTIVE

The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS

PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS

CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION

Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Chimeric vaccine designs against Acinetobacter baumannii using pan genome and reverse vaccinology approaches

Acinetobacter baumannii (A. baumannii), an opportunistic, gram-negative pathogen, has evoked the interest of the medical community throughout the world because of its ability to cause nosocomial infections, majorly infecting those in intensive care units. It has also drawn the attention of researchers due to its evolving immune evasion strategies and increased drug resistance. The emergence of multi-drug-resistant-strains has urged the need to explore novel therapeutic options as an alternative to antibiotics. Due to the upsurge in antibiotic resistance mechanisms exhibited by A. baumannii, the current therapeutic strategies are rendered less effective. The aim of this study is to explore novel therapeutic alternatives against A. baumannii to control the ailed infection. In this study, a computational framework is employed involving, pan genomics, subtractive proteomics and reverse vaccinology strategies to identify core promiscuous vaccine candidates. Two chimeric vaccine constructs having B-cell derived T-cell epitopes from prioritized vaccine candidates; APN, AdeK and AdeI have been designed and checked for their possible interactions with host BCR, TLRs and HLA Class I and II Superfamily alleles. These vaccine candidates can be experimentally validated and thus contribute to vaccine development against A. baumannii infections.

Engineering a multi epitope vaccine against SARS-CoV-2 by exploiting its non structural and structural proteins

SARS-CoV-2, the causative agent behind the ongoing pandemic exhibits an enhanced potential for infection when compared to its related family members- the SARS-CoV and MERS-CoV; which have caused similar disease outbreaks in the past. The severity of the global health burden, increasing mortality rate and the emergent economic crisis urgently demands the development of next generation vaccines. Amongst such emergent next generation vaccines are the multi-epitope subunit vaccines, which hold promise in combating deadly pathogens. In this study we have exploited immunoinformatics applications to delineate a vaccine candidate possessing multiple B and T cells epitopes by utilizing the SARS-CoV-2 non structural and structural proteins. The antigenicity potential, safety, structural stability and the production feasibility of the designed construct was evaluated computationally. Furthermore, due to the known role of human TLR-3 immune receptor in viral sensing, which facilitates host cells activation for an immune response, the vaccine construct was examined for its binding efficiency using molecular docking and molecular dynamics simulation studies, which resulted in strong and stable interactions. Finally, the immune simulation studies suggested an effective immune response on vaccine administration. Overall, the immunoinformatics analysis advocates that the proposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

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.