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

Developing a chimeric multiepitope vaccine against Nipah virus (NiV) through immunoinformatics, molecular docking and dynamic simulation approaches

Nipah virus (NiV) is a highly lethal zoonotic pathogen that poses a significant threat to human and animal health. Unfortunately, no effective treatments have been developed for this deadly zoonotic disease. Therefore, we designed a chimeric multiepitope vaccine targeting the Nipah virus (NiV) glycoprotein and fusion protein through immunoinformatic approaches. Therefore, the vaccine was developed by combining promising and potential antigenic MHC-I, MHC-II, and B-cell epitopes obtained from the selected proteins. When combined, the MHC-I and MHC-II epitopes offered 100 % global population coverage. The physicochemical characterization also exhibited favorable properties, including solubility and potential functional stability of the vaccine within the body (GRAVY score of -0.308). Structural analyses unveiled a well-stabilized secondary and tertiary structure with a Ramachandran score of 84.4 % and a Z score of -5.02. Findings from docking experiments with TLR-2 (-1089.3 kJ/mol) and TLR-4 (-1016.7 kJ/mol) showed a strong affinity of the vaccine towards the receptor. Molecular dynamics simulations revealed unique conformational dynamics among the "vaccine-apo," "vaccine-TLR-2," and "vaccine-TLR-4″ complexes. Consequently, the complexes exhibited significant compactness, flexibility, and exposure to solvents. The results of the codon optimization were remarkable, as the vaccine showed a significant amount of expression in the E. coli vector (GC content of 45.36 % and a CAI score of 1.0). The results of immune simulations, however, showed evidence of both adaptive and innate immune responses induced by the vaccine. Therefore, we highly recommend further research on this chimeric multiepitope vaccine to establish its efficacy and safety against the Nipah virus (NiV).

Novel SARS-COV2 poly epitope phage-based candidate vaccine and its immunogenicity

Background and purpose

The global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prompted widespread concern. Bacteriophages have recently gained attention as a cost-effective and stable alternative for vaccine development due to their adjuvant properties. This study aimed to design and validate a poly epitope composed of viral proteins.

Experimental approach

SARS-CoV-2 proteins (spike, nucleocapsid, membrane, envelope, papain-like protease, and RNA-dependent RNA polymerase) were selected for analysis. Immunoinformatic methods were employed to predict B and T cell epitopes, assessing their antigenicity, allergenicity, and toxicity. Epitopes meeting criteria for high antigenicity, non-allergenicity, and non-toxicity were linked to form poly epitopes. These sequences were synthesized and cloned into pHEN4 plasmids to generate Poly1 and Poly2 phagemid vectors. Recombinant Poly1 and Poly2 phages were produced by transforming M13ΔIII plasmids and phagemid vectors into E. coli TG1. Female Balb/c mice were immunized with a cocktail of Poly1 and Poly2 phages, and their serum was collected for ELISA testing. Interferon-gamma (IFN-γ) testing was performed on spleen-derived lymphocytes to evaluate immune system activation.

Findings/Results

Recombinant Poly1 and Poly2 phages were produced, and their titer was determined as 1013 PFU/mL. Efficient humoral immune responses and cellular immunity activation in mice were achieved following phage administration.

Conclusion and implication

Poly epitopes displayed on phages exhibit adjuvant properties, enhancing humoral and cellular immunity in mice. This suggests that phages could serve as adjuvants to bolster immunity against SARS-Cov-2. Recombinant phages could be applied as effective candidates for injectable and oral vaccine development strategies.

Prediction of promiscuous multiepitope-based peptide vaccine against RdRp of rotavirus using immunoinformatics studies

Rotavirus, a dsRNA virus in the Reoviridae family, shows a segmented genome. The VP1 gene encodes the RNA-dependent RNA polymerase (RdRp). This study aims to develop a multiepitope-based vaccine targeting RdRp using immunoinformatic approaches. In this study, 100 available nucleotide sequences of VP1-Rotavirus belonging to different strains across the world were retrieved from NCBI database. The selected sequences were aligned, and a global consensus sequence was developed by using CLC work bench. The study involved immunoinformatic approaches and molecular docking studies to reveal the promiscuous epitopes that can be eventually used as active vaccine candidates for Rotavirus. In total, 27 highly immunogenic, antigenic, and non-allergenic T-cell and B-cell epitopes were predicted for the Multiepitope vaccine (MEV) against rotavirus. It was also observed that MEV can prove to be effective worldwide due to its high population coverage, demonstrating the consistency of this vaccine. Moreover, there is a high docking interaction and immunological response with a binding score of -50.2 kcal/mol, suggesting the vaccine's efficacy. Toll-like receptors (TLRs) also suggest that the vaccine is physiologically and immunologically effective. Collectively, our data point to an effective MEV against rotavirus that can effectively reduce viral infections and improve the health status worldwide.

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

Insights into structural vaccinology harnessed for universal coronavirus vaccine development

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

Recent advances and applications of peptide-agent conjugates for targeting tumor cells

BACKGROUND

Cancer, being a complex disease, presents a major challenge for the scientific and medical communities. Peptide therapeutics have played a significant role in different medical practices, including cancer treatment.

METHOD

This review provides an overview of the current situation and potential development prospects of anticancer peptides (ACPs), with a particular focus on peptide vaccines and peptide-drug conjugates for cancer treatment.

RESULTS

ACPs can be used directly as cytotoxic agents (molecularly targeted peptides) or can act as carriers (guiding missile) of chemotherapeutic agents and radionuclides by specifically targeting cancer cells. More than 60 natural and synthetic cationic peptides are approved in the USA and other major markets for the treatment of cancer and other diseases. Compared to traditional cancer treatments, peptides exhibit anticancer activity with high specificity and the ability to rapidly kill target cancer cells. ACP's target and kill cancer cells via different mechanisms, including membrane disruption, pore formation, induction of apoptosis, necrosis, autophagy, and regulation of the immune system. Modified peptides have been developed as carriers for drugs, vaccines, and peptide-drug conjugates, which have been evaluated in various phases of clinical trials for the treatment of different types of solid and leukemia cancer.

CONCLUSIONS

This review highlights the potential of ACPs as a promising therapeutic option for cancer treatment, particularly through the use of peptide vaccines and peptide-drug conjugates. Despite the limitations of peptides, such as poor metabolic stability and low bioavailability, modified peptides show promise in addressing these challenges. Various mechanism of action of anticancer peptides. Modes of action against cancer cells including: inducing apoptosis by cytochrome c release, direct cell membrane lysis (necrosis), inhibiting angiogenesis, inducing autophagy-mediated cell death and immune cell regulation.

TPpred-LE: therapeutic peptide function prediction based on label embedding

BACKGROUND

Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data.

RESULTS

In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data.

CONCLUSIONS

In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.

Immunoinformatics applications in the development of therapeutic vaccines against human papillomavirus-related infections and cervical cancer

The human papillomavirus (HPV) represents the most prevalent sexually transmitted infectious agent worldwide. HPV penetrates the epithelium through microlesions and establishes an infectious focus that can lead to the development of cervical cancer. Prophylactic HPV vaccines are available, but do not affect already-established infections. Using in silico prediction tools is a promising strategy for identifying and selecting vaccine candidate T cell epitopes. An advantage of this strategy is that epitopes can be selected according to the degree of conservation within a group of antigenic proteins. This makes achieving comprehensive genotypic coverage possible with a small set of epitopes. Therefore, this paper revises the general characteristics of HPV biology and the current knowledge on developing therapeutic peptide vaccines against HPV-related infections and cervical cancer.

Gonococcal microparticle vaccine in dissolving microneedles induced immunity and enhanced bacterial clearance in infected mice

There is an alarming rise in the number of gonorrhea cases worldwide. Neisseria gonorrhoeae, the bacteria that causes gonorrhea infection, has gradually developed antimicrobial resistance over the years. To date, there is no licensed vaccine for gonorrhea. This study investigates the in vivo immunogenicity of a whole-cell inactivated gonococci in a microparticle formulation (Gc-MP) along with adjuvant microparticles (Alhydrogel®- Alum MP and AddaVax™ MP) delivered transdermally using dissolving microneedles (MN). The proposed vaccine formulation (Gc-MP + Alum MP + AddaVax™ MP) was assessed for induction of humoral, cellular, and protective immune responses in vivo. Our results show the induction of significant gonococcal-specific serum IgG, IgG1, IgG2a, and vaginal mucosal IgA antibodies in mice immunized with Gc-MP + Alum MP + AddaVax™ MP and Gc-MP when compared to the control groups receiving blank MN or no treatment. The serum bactericidal assay revealed that the antibodies generated in mice after immunization with Gc-MP + Alum MP + AddaVax™ MP were bactericidal towards live Neisseria gonorrhoeae. Gc-MP + Alum MP + AddaVax™ MP and Gc-MP-immunized mice showed enhanced clearance rate of gonococcal bacterial infection post challenge. In contrast, the control groups did not begin to clear the infection until day 10. In addition, the mice which received Gc-MP + Alum MP + AddaVax™ MP showed enhanced expression of cellular immunity markers CD4 and CD8 on the surface of T cells in the spleen and lymph nodes. Taken together, the data shows that microneedle immunization with whole-cell inactivated gonococci MP in mice induced humoral, cellular, and protective immunity against gonococcal infection.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Synthetic peptides derived from predicted B cell epitopes of nervous necrosis virus (NNV) show antigenicity and elicit immunogenic responses in Asian seabass (Lates calcarifer)

Nervous necrosis virus (NNV) has spread throughout the world, affecting more than 120 freshwater and marine fish species. While vaccination effectively prevents disease outbreaks, the difficulty of producing sufficient viruses using cell lines continues to be a significant disadvantage for producing inactivated vaccines. This study, therefore, explored the application of synthetic peptides as potential vaccine candidates for the prevention of NNV in Asian seabass (Lates calcarifer). Using the epitope prediction tool and molecular docking, three predicted immunogenic B cell epitopes (30-32 aa) derived from NNV coat protein were selected and synthesised, corresponding to amino acid positions 5 to 34 (P1), 133 to 162 (P2) and 181 to 212 (P3). All the predicted peptides interact with Asian sea bass's MHC class II by docking. The antigenicity of these peptides was determined through ELISA and all peptides were able to react with NNV-specific antibodies. Subsequently, the immunogenicity of these synthetic peptides was investigated by immunisation of Asian seabass with individual peptides (30 μg/fish) and a peptide cocktail (P1+P2+P3, 10 μg each/fish) by intraperitoneal injection, followed by a booster dose at day 28 post-primary immunisation. There was a subset of immunised fish that were able to induce upregulation of immune genes (IL-1β, TNFα, MHCI, MHCII β, CD4, CD8, and IgM-like) in the head kidney and spleen post immunization. Importantly, antibodies derived from fish immunised with synthetic peptides reacted with whole NNV virions, and sera from P1 group could neutralise NNV in an in vitro assay. Taken together, these findings indicate that synthetic linear peptides based on predicted B cell epitopes exhibited both antigenic and immunogenic properties, suggesting that they could be potential vaccine candidates for the prevention of NNV in fish.

Nucleic acid-based vaccine platforms against the coronavirus disease 19 (COVID-19)

The coronavirus disease 2019 (COVID-19) pandemic has infected 673,010,496 patients and caused the death of 6,854,959 cases globally until today. Enormous efforts have been made to develop fundamentally different COVID-19 vaccine platforms. Nucleic acid-based vaccines consisting of mRNA and DNA vaccines (third-generation vaccines) have been promising in terms of rapid and convenient production and efficient provocation of immune responses against the COVID-19. Several DNA-based (ZyCoV-D, INO-4800, AG0302-COVID19, and GX-19N) and mRNA-based (BNT162b2, mRNA-1273, and ARCoV) approved vaccine platforms have been utilized for the COVID-19 prevention. mRNA vaccines are at the forefront of all platforms for COVID-19 prevention. However, these vaccines have lower stability, while DNA vaccines are needed with higher doses to stimulate the immune responses. Intracellular delivery of nucleic acid-based vaccines and their adverse events needs further research. Considering re-emergence of the COVID-19 variants of concern, vaccine reassessment and the development of polyvalent vaccines, or pan-coronavirus strategies, is essential for effective infection prevention.

Construction and validation of a multi-epitope in silico vaccine model for lymphatic filariasis by targeting Brugia malayi: a reverse vaccinology approach

BACKGROUND

Lymphatic filariasis (LF), often referred to as elephantiasis, has been identified as one of the 17 neglected tropical diseases by the World Health Organization. Currently, there are no vaccines available to treat this infection in humans. Therefore, with the objective of devising a novel preventive measure, we exploited an immunoinformatics approach to design a multi-epitope-based subunit vaccine for LF, that can elicit a variety of immune responses within the host. In this study, different B cell, T_C cell, and T_H cell-binding epitopes were screened from the antigenic proteins of Brugia malayi and they were passed through several immunological filters to determine the optimal epitopes.

RESULTS

As a result, 15 CD8+, 3 CD4+, and 3 B cell epitopes were found to be prominent, antigenic, non-toxic, immunogenic and non-allergenic. The presence of conformational B cell epitopes and cytokine-inducing epitopes confirmed the humoral and cell-mediated immune response that would be triggered by the constructed vaccine model. Following that, the selected epitopes and TLR-4-specific adjuvant were ligated by appropriate peptide linkers to finalize the vaccine construct. Protein-protein docking of the vaccine structure with the TLR4 receptor predicted strong binding affinity and hence putatively confirms its ability to elicit an immune response. Further, the efficiency of the vaccine candidate to provide a long-lasting protective immunity was assessed by in silico immune simulation. The reverse translated vaccine sequence was also virtually cloned in the pET28a (+) plasmid after the optimization of the gene sequence.

CONCLUSION

So taken together, by monitoring the overall in silico assessment, we hypothesize that our engineered peptide vaccine could be a viable prophylactic approach in the development of vaccines against the threat of human lymphatic filariasis.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s42269-023-01013-0.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Plants as Biofactories for Therapeutic Proteins and Antiviral Compounds to Combat COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had a profound impact on the world's health and economy. Although the end of the pandemic may come in 2023, it is generally believed that the virus will not be completely eradicated. Most likely, the disease will become an endemicity. The rapid development of vaccines of different types (mRNA, subunit protein, inactivated virus, etc.) and some other antiviral drugs (Remdesivir, Olumiant, Paxlovid, etc.) has provided effectiveness in reducing COVID-19's impact worldwide. However, the circulating SARS-CoV-2 virus has been constantly mutating with the emergence of multiple variants, which makes control of COVID-19 difficult. There is still a pressing need for developing more effective antiviral drugs to fight against the disease. Plants have provided a promising production platform for both bioactive chemical compounds (small molecules) and recombinant therapeutics (big molecules). Plants naturally produce a diverse range of bioactive compounds as secondary metabolites, such as alkaloids, terpenoids/terpenes and polyphenols, which are a rich source of countless antiviral compounds. Plants can also be genetically engineered to produce valuable recombinant therapeutics. This molecular farming in plants has an unprecedented opportunity for developing vaccines, antibodies, and other biologics for pandemic diseases because of its potential advantages, such as low cost, safety, and high production volume. This review summarizes the latest advancements in plant-derived drugs used to combat COVID-19 and discusses the prospects and challenges of the plant-based production platform for antiviral agents.

Experimental Validation of MHC Class I and II Peptide-Based Potential Vaccine Candidates for Human Papilloma Virus Using Sprague-Dawly Models

Human papilloma virus (HPV) causes cervical and many other cancers. Recent trend in vaccine design is shifted toward epitope-based developments that are more specific, safe, and easy to produce. In this study, we predicted eight immunogenic peptides of CD4+ and CD8+ T-lymphocytes (MHC class I and II as M1 and M2) including early proteins (E2 and E6), major (L1) and minor capsid protein (L2). Male and female Sprague Dawly rats in groups were immunized with each synthetic peptide. L1M1, L1M2, L2M1, and L2M2 induced significant immunogenic response compared to E2M1, E2M2, E6M1 and E6M2. We observed optimal titer of IgG antibodies (>1.25 g/L), interferon-γ (>64 ng/L), and granzyme-B (>40 pg/mL) compared to control at second booster dose (240 µg/500 µL). The induction of peptide-specific IgG antibodies in immunized rats indicates the T-cell dependent B-lymphocyte activation. A substantial CD4+ and CD8+ cell count was observed at 240 µg/500 µL. In male and female rats, CD8+ cell count for L1 and L2 peptide is 3000 and 3118, and CD4+ is 3369 and 3484 respectively compared to control. In conclusion, we demonstrated that L1M1, L1M2, L2M1, L2M2 are likely to contain potential epitopes for induction of immune responses supporting the feasibility of peptide-based vaccine development for HPV.

The influence of deliberate rumination on the post-traumatic growth of college students during the COVID-19 pandemic and the moderating role of self-efficacy

Objective

To understand the relationship between deliberate rumination and post-traumatic growth and the mechanisms affecting this relationship, we constructed an adjustment model to test the impact of deliberate rumination on the post-traumatic growth of college students and the moderating role of self-efficacy during the 2019 COVID-19 pandemic.

Study design and setting

A total of 881 college students from a university of science and technology in Guangdong Province, China, completed a questionnaire that measured deliberate rumination, post-traumatic growth, and self-efficacy. SPSS (version 26) and the PROCESS plug-in (version 4.0) were used for correlation and moderation analyses.

Results

The correlation analysis showed that deliberate rumination was positively correlated with post-traumatic growth (r = 0.353, P < 0.01) and self-efficacy (r = 0.261, P < 0.01). Self-efficacy was also positively correlated with post-traumatic growth (r = 0.466, P < 0.01). In addition, we found that self-efficacy had a regulatory effect on the relationship between deliberate rumination and post-traumatic growth (R ² = 0.287, P < 0.001) and that this effect was significant.

Conclusion

The results show that deliberate rumination can be a positive predictor of post-traumatic growth and can play a certain role in fostering such growth. In addition, self-efficacy is a moderator that plays a buffer role between deliberate rumination and post-traumatic growth. These results contribute to a more comprehensive understanding of the mechanisms that affect post-traumatic growth.

Application of big data and artificial intelligence in epidemic surveillance and containment

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

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.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV)

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.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.

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

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.

Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach

Background

Candida tropicalis causes tropical invasive fungal infections, with a high mortality. This fungus has been found to be resistant to antifungal classes such as azoles, echinocandins, and polyenes in several studies. As a result, it is vital to identify novel approaches to prevent and treat C. tropicalis infections. In this study, an in silico technique was utilized to deduce and evaluate a powerful multivalent epitope-based vaccine against C. tropicalis, which targets the secreted aspartic protease 2 (SAP2) protein. This protein is implicated in virulence and host invasion.

Results

By focusing on the Sap2 protein, 11 highly antigenic, non-allergic, non-toxic, and conserved epitopes were identified. These were subsequently paired with RS09 and flagellin adjuvants, as well as a pan HLA DR-binding epitope (PADRE) sequence to create a vaccine candidate that elicited both cell-mediated and humoral immune responses. It was projected that the vaccine design would be soluble, stable, antigenic, and non-allergic. Ramachandran plot analysis was applied to validate the vaccine construct’s 3-dimensional model. The vaccine construct was tested (at 100 ns) using molecular docking and molecular dynamics simulations, which demonstrated that it can stably connect with MHC-I and Toll-like receptor molecules. Based on in silico studies, we have shown that the vaccine construct can be expressed in E. coli. We surmise that the vaccine design is unrelated to any human proteins, indicating that it is safe to use.

Conclusions

The vaccine design looks to be an effective option for preventing C. tropicalis infections, based on the outcomes of the studies. A fungal vaccine can be proposed as prophylactic medicine and could provide initial protection as sometimes diagnosis of infection could be challenging. However, more in vitro and in vivo research is needed to prove the efficacy and safety of the proposed vaccine design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00415-3.

Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development

The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.

Review on the COVID-19 pandemic prevention and control system based on AI

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

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.

Platform for Active Vaccine Formulation Using a Two-Mode Enhancement Mechanism of Epitope Presentation by Pickering Emulsion

The efficiency of epitope-based vaccination (subunit vaccines) is tightly correlated with heterogeneity and the high density of epitope presentation, which maximizes the potential antigenic determinants. Here, we developed a two-mode platform for intensifying the epitope presentation of subunit vaccines. The two-mode epitope presentation enhancement includes a covalent attachment of high concentrations of SARS-CoV-2-S1 peptide epitope to the surface of virus-like-particles (VLPs) and the subsequent assembly of VLP/epitope conjugates on the oil droplet surface at an oil/water interface of an emulsion as Pickering stabilizers. The resultant emulsions were stable for weeks in ambient conditions, and our platform was challenged using the epitope of the SARS-CoV-2-S1 peptide that served as a model epitope in this study. In vivo assays showed that the αSARS-CoV-2-S1 immunoglobulin G (IgG) titers of the studied mouse antisera, developed against the SARS-CoV-2-S1 peptide under different epitope preparation conditions, showed an order of magnitude higher IgG titers in the studied VLP-based emulsions than epitopes dissolved in water and epitopes administered with an adjuvant, thereby confirming the efficacy of the formulation. This VLP-based Pickering emulsion platform is a fully synthetic approach that can be readily applied for vaccine development to a wide range of pathogens.

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.

Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates

This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began its global threat at the end of 2019 and since then has had a devastating impact on the whole world. Measures to reduce threats from the pandemic include social restrictions, restrictions on international travel, and vaccine development. In most cases, vaccine development depends on the spike glycoprotein, which serves as a medium for its entry into host cells. Although several variants of SARS-CoV-2 have emerged from mutations crossing continental boundaries, about 6000 delta variants have been reported along the coast of more than 20 countries in Africa, with South Africa accounting for the highest percentage. This also applies to the omicron variant of the SARS-CoV-2 virus in South Africa. The authors suggest that bioinformatics and immunoinformatics approaches be used to develop a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. Various immunoinformatics tools have been used to predict T- and B-lymphocyte epitopes. The epitopes were further subjected to multiple evaluations to select epitopes that could elicit a sustained immunological response. The candidate vaccine consisted of seven epitopes, a highly immunogenic adjuvant, an MHC I-targeting domain (MITD), a signal peptide, and linkers. The molecular weight (MW) was predicted to be 223.1 kDa, well above the acceptable threshold of 110 kDa on an excellent vaccine candidate. In addition, the results showed that the candidate vaccine was antigenic, non-allergenic, non-toxic, thermostable, and hydrophilic. The vaccine candidate has good population coverage, with the highest range in East Africa (80.44%) followed by South Africa (77.23%). West Africa and North Africa have 76.65% and 76.13%, respectively, while Central Africa (75.64%) has minimal coverage. Among seven epitopes, no mutations were observed in 100 randomly selected SARS-CoV-2 spike glycoproteins in the study area. Evaluation of the secondary structure of the vaccine constructs revealed a stabilized structure showing 36.44% alpha-helices, 20.45% drawn filaments, and 33.38% random helices. Molecular docking of the TLR4 vaccine showed that the simulated vaccine has a high binding affinity for TLR-4, reflecting its ability to stimulate the innate and adaptive immune response.

Examination of Common Coronavirus Antibodies in SARS-CoV-2-Infected and Uninfected Participants in a Household Transmission Investigation

We compared paired serum specimens from household contacts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases with detectable SARS-CoV-2 seroconversion with contacts who remained seronegative. No protection from SARS-CoV-2 infection was associated with human coronavirus antibodies; however, an increase in common betacoronavirus antibodies was associated with seroconversion to SARS-CoV-2 in mild to moderately ill cases.

Design of a Multiepitope Vaccine against Chicken Anemia Virus Disease

Chicken anemia virus (CAV) causes severe clinical and sub-clinical infection in poultry globally and thus leads to economic losses. The drawbacks of the commercially available vaccines against CAV disease signal the need for a novel, safe, and effective vaccine design. In this study, a multiepitope vaccine (MEV) consisting of T-cell and B-cell epitopes from CAV viral proteins (VP1 and VP2) was computationally constructed with the help of linkers and adjuvant. The 3D model of the MEV construct was refined and validated by different online bioinformatics tools. Molecular docking showed stable interaction of the MEV construct with TLR3, and this was confirmed by Molecular Dynamics Simulation. Codon optimization and in silico cloning of the vaccine in pET-28a (+) vector also showed its potential expression in the E. coli K12 system. The immune simulation also indicated the ability of this vaccine to induce an effective immune response against this virus. Although the vaccine in this study was computationally constructed and still requires further in vivo study to confirm its effectiveness, this study marks a very important step towards designing a potential vaccine against CAV disease.

Implementation of immuno-chemoinformatics approaches to construct multi-epitope for vaccine development against Omicron and Delta SARS-CoV-2 variants

Background

The new coronavirus is still a life-threatening menace, because of its changing nature and capacity to produce many mutations to bypass the immune system. The vaccination is the first effective weapon against COVID-19.

Aim

The study's goal was to design a multi-epitope peptide vaccine (MEPV) for a mix of Omicron and Delta Coronavirus strains using immuno-chemoinformatics tools.

Methods

To create the vaccine epitopes, seven proteins from the Omicron and Delta coronavirus strains were selected (ORF1a, ORF3a, surface protein, membrane protein, ORF7a, ORF8, and nucleocapsid protein). Antigenicity, toxicity, and allergenicity of the epitopes were evaluated.

Results

The designed vaccine is made up of 534 amino acids that are homogeneous, antigenic, and non-toxic. Sticky restriction enzymes (XhoI and XbaI) were used to incorporate the MEPV into the pmirGLO luciferase vector. SnapGene server was used to create primers for PCR testing. Developing the MEPV is a terrific cost-effective strategy. The created MEPV's physiochemical properties have been determined to be basic, hydrophobic, and stableImmunogenicity and immune response profiles of the developed vaccine candidate were better assessed using in silico immunological simulations.

Conclusions

We advocate moving the built vaccine to the biological validation step, where it may test our findings using appropriate model organisms.

A conserved subunit vaccine designed against SARS-CoV-2 variants showed evidence in neutralizing the virus

Novel coronavirus (SARS-CoV-2) leads to coronavirus disease 19 (COVID-19), declared as a pandemic that outbreaks within almost 225 countries worldwide. For the time being, numerous mutations have been reported that led to the generation of numerous variants spread more rapidly. This study aims to establish an efficient multi-epitope subunit vaccine that could elicit both T-cell and B-cell responses sufficient to recognize three confirmed surface proteins of the virus. The sequences of the viral surface proteins, e.g., an envelope protein (E), membrane glycoprotein (M), and S1 and S2 domain of spike surface glycoprotein (S), were analyzed by an immunoinformatic approach. Top immunogenic epitopes have been selected based on the assessment of the affinity with MHC class-I and MHC class-II, population coverage, along with conservancy among wild type and new variants of SARS-CoV-2 genomes. Molecular docking and molecular dynamic simulation suggest that the proposed top peptides have the potential to interact with the highest number of both the MHC class I and MHC class II. The epitopes were assembled by the appropriate linkers to form a multi-epitope vaccine. Epitopes used in the vaccine construct are conserved in all the variants evolved till now. This in silico-designed multi-epitope vaccine is highly immunogenic and induces levels of SARS-CoV2-neutralizing antibodies in mice, which is detected by inhibition of cytopathic effect in Vero cell monolayer. Further studies are required to improve its efficiency in the prevention of virus replication in lung tissue, in addition to safety validation as a step for human application to combat SARS-CoV-2 variants. KEY POINTS: • We discovered five T-cell epitopes from three surface proteins of SARS-CoV-2. • These are conserved in the wild-type virus and variants, e.g., beta, delta, and omicron. • The multi-epitope vaccine can induce IgG in mice that can neutralize the virus.

Probing the competitive inhibitor efficacy of frog-skin alpha helical AMPs identified against ACE2 binding to SARS-CoV-2 S1 spike protein as therapeutic scaffold to prevent COVID-19

In COVID-19 infection, the SARS-CoV-2 spike protein S1 interacts to the ACE2 receptor of human host, instigating the viral infection. To examine the competitive inhibitor efficacy of broad spectrum alpha helical AMPs extracted from frog skin, a comparative study of intermolecular interactions between viral S1 and AMPs was performed relative to S1-ACE2p interactions. The ACE2 binding region with S1 was extracted as ACE2p from the complex for ease of computation. Surprisingly, the Spike-Dermaseptin-S9 complex had more intermolecular interactions than the other peptide complexes and importantly, the S1-ACE2p complex. We observed how atomic displacements in docked complexes impacted structural integrity of a receptor-binding domain in S1 through conformational sampling analysis. Notably, this geometry-based sampling approach confers the robust interactions that endure in S1-Dermaseptin-S9 complex, demonstrating its conformational transition. Additionally, QM calculations revealed that the global hardness to resist chemical perturbations was found more in Dermaseptin-S9 compared to ACE2p. Moreover, the conventional MD through PCA and the torsional angle analyses indicated that Dermaseptin-S9 altered the conformations of S1 considerably. Our analysis further revealed the high structural stability of S1-Dermaseptin-S9 complex and particularly, the trajectory analysis of the secondary structural elements established the alpha helical conformations to be retained in S1-Dermaseptin-S9 complex, as substantiated by SMD results. In conclusion, the functional dynamics proved to be significant for viral Spike S1 and Dermaseptin-S9 peptide when compared to ACE2p complex. Hence, Dermaseptin-S9 peptide inhibitor could be a strong candidate for therapeutic scaffold to prevent infection of SARS-CoV-2.

Peptide-Based Vaccines and Therapeutics for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been prevalent in the humans since 2019 and has given rise to a pandemic situation. With the discovery and ongoing use of drugs and vaccines against SARS-CoV-2, there is still no surety of its complete suppression of this disease or if there is a need for additional booster doses. There is an urgent need for alternative treatment strategies against COVID-19. Peptides and peptidomimetics have several advantages as therapeutic agents because of their target selectivity, better interactions, and lower toxicity. Minor structural alterations to peptides can help prevent their fast metabolism and provide long-action. This comprehensive review provides an overview of different peptide-based vaccines and therapeutics against SARS-CoV-2. It discusses the design and mechanism of action of the peptide-based vaccines, peptide immunomodulators, anti-inflammatory agents, and peptides as entry inhibitors of SARS-CoV-2. Moreover, the mechanism of action, sequences and current clinical trial studies are also summarized. The review also discusses the future aspects of peptide-based vaccines and therapeutics for COVID-19.

Graphical Abstract

Recent and advanced nano-technological strategies for COVID-19 vaccine development

The outbreak of the COVID-19 pandemic in 2019 has been one of the greatest challenges modern medicine and science has ever faced. It has affected millions of people around the world and altered human life and activities as we once knew. The high prevalence as well as an extended period of incubations which usually does not present with symptoms have played a formidable role in the transmission and infection of millions. A lot of research has been carried out on developing suitable treatment and effective preventive measures for the control of the pandemic. Preventive strategies which include social distancing, use of masks, washing of hands, and contact tracing have been effective in slowing the spread of the virus; however, the infectious nature of the SARS-COV-2 has made these strategies unable to eradicate its spread. In addition, the continuous increase in the number of cases and death, as well as the appearance of several variants of the virus, has necessitated the development of effective and safe vaccines in a bid to ensure that human activities can return to normalcy. Nanotechnology has been of great benefit in the design of vaccines as nano-sized materials have been known to aid the safe and effective delivery of antigens as well as serve as suitable adjuvants to potentiate responses to vaccines. There are only four vaccine candidates currently approved for use in humans while many other candidates are at various levels of development. This review seeks to provide updated information on the current nano-technological strategies employed in the development of COVID-19 vaccines.

DNA Vaccines Expressing the Envelope and Membrane Proteins Provide Partial Protection Against SARS-CoV-2 in Mice

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency of international concern, and an effective vaccine is urgently needed to control the pandemic. Envelope (E) and membrane (M) proteins are highly conserved structural proteins among SARS-CoV-2 and SARS-CoV and have been proposed as potential targets for the development of cross-protective vaccines. Here, synthetic DNA vaccines encoding SARS-CoV-2 E/M proteins (called p-SARS-CoV-2-E/M) were developed, and mice were immunised with three doses via intramuscular injection and electroporation. Significant cellular immune responses were elicited, whereas no robust humoral immunity was detected. In addition, novel H-2d-restricted T-cell epitopes were identified. Notably, although no drop in lung tissue virus titre was detected in DNA-vaccinated mice post-challenge with SARS-CoV-2, immunisation with either p-SARS-CoV-2-E or p-SARS-CoV-2-M provided minor protection and co-immunisation with p-SARS-CoV-2-E+M increased protection. Therefore, E/M proteins should be considered as vaccine candidates as they may be valuable in the optimisation of vaccination strategies against COVID-19.

Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch vaccines, designed by reverse epitomics approach, show potential to cover large ethnically distributed human population worldwide

The SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is responsible for the COVID-19 outbreak. The highly contagious COVID-19 disease has spread to 216 countries in less than six months. Though several vaccine candidates are being claimed, an effective vaccine is yet to come. A novel reverse epitomics approach, 'overlapping-epitope-clusters-to-patches' method is utilized to identify the antigenic regions from the SARS-CoV-2 proteome. These antigenic regions are named as 'Ag-Patch or Ag-Patches', for Antigenic Patch or Patches. The identification of Ag-Patches is based on the clusters of overlapping epitopes rising from SARS-CoV-2 proteins. Further, we have utilized the identified Ag-Patches to design Multi-Patch Vaccines (MPVs), proposing a novel method for the vaccine design. The designed MPVs were analyzed for immunologically crucial parameters, physiochemical properties and cDNA constructs. We identified 73 CTL (Cytotoxic T-Lymphocyte) and 49 HTL (Helper T-Lymphocyte) novel Ag-Patches from the proteome of SARS-CoV-2. The identified Ag-Patches utilized to design MPVs cover 768 overlapping epitopes targeting 55 different HLA alleles leading to 99.98% of world human population coverage. The MPVs and Toll-Like Receptor ectodomain complex shows stable complex formation tendency. Further, the cDNA analysis favors high expression of the MPVs constructs in a human cell line. We identified highly immunogenic novel Ag-Patches from the entire proteome of SARS CoV-2 by a novel reverse epitomics approach and utilized them to design MPVs. We conclude that the novel MPVs could be a highly potential novel approach to combat SARS-CoV-2, with greater effectiveness, high specificity and large human population coverage worldwide. Communicated by Ramaswamy H. Sarma.

Peptides for Vaccine Development

This review discusses peptide epitopes used as antigens in the development of vaccines in clinical trials as well as future vaccine candidates. It covers peptides used in potential immunotherapies for infectious diseases including SARS-CoV-2, influenza, hepatitis B and C, HIV, malaria, and others. In addition, peptides for cancer vaccines that target examples of overexpressed proteins are summarized, including human epidermal growth factor receptor 2 (HER-2), mucin 1 (MUC1), folate receptor, and others. The uses of peptides to target cancers caused by infective agents, for example, cervical cancer caused by human papilloma virus (HPV), are also discussed. This review also provides an overview of model peptide epitopes used to stimulate non-specific immune responses, and of self-adjuvanting peptides, as well as the influence of other adjuvants on peptide formulations. As highlighted in this review, several peptide immunotherapies are in advanced clinical trials as vaccines, and there is great potential for future therapies due the specificity of the response that can be achieved using peptide epitopes.

Toll-Like Receptors (TLRs) as Therapeutic Targets for Treating SARS-CoV-2: An Immunobiological Perspective

INTRODUCTION

Coronavirus disease-19 (COVID-19) caused by SARS-CoV-2 is presently the biggest threat to mankind throughout the globe. Increasing reports on deaths, cases of new infection, and socioeconomic losses are continuously coming from all parts of the world. Developing an efficacious drug and/or vaccine is currently the major goal to the scientific communities. In this context, toll-like receptors (TLRs) could be the useful targets in adopting effective therapeutic approaches.

METHODS

This chapter has been written by incorporating the findings on TLR-based therapies against SARS-CoV-2 demonstrated in the recently published research papers/reviews.

RESULTS

TLRs are the essential components of host immunity and play critical roles in deciding the fate of SARS-CoV-2 by influencing the immunoregulatory circuits governing human immune response to this pathogen. Hitherto, a number of multi-subunit peptide-based vaccines and pharmacological agents developed against SARS-CoV-2 have been found to manipulate TLR function. Therefore, circumventing overt immunopathology of COVID-19 applying TLR-antagonists can effectively reduce the morality caused from "cytokine storm"-induced multiorgan failure. Similarly, pre-administration of TLR- agonists may be used as a prophylaxis to sensitize the immune system of the individuals having risk of infection. A lot of collaborative efforts are required for bench-to-bench transformation of these knowledges.

CONCLUSION

This chapter enlightens the potentials and promises of TLR-guided therapeutic strategies against COVID-19 by reviewing the major findings and achievements depicted in the literatures published till date.

Structure-guided design of multi-epitopes vaccine against variants of concern (VOCs) of SARS-CoV-2 and validation through In silico cloning and immune simulations

Severe Acute Respiratory Syndrome Corovirus2 (SARS-CoV-2) has been determined to be the cause of the current pandemic. Typical symptoms of patient having COVID-19 are fever, runny nose, cough (dry or not) and dyspnea. Several vaccines are available in markets that are tackling current pandemic. Many different strains of SAR-CoV-2 have been evolved with the passage of time. The emergence of VOCs particularly the B.1.351 ("South African") variant of SARS-CoV-2 has been reported to be more resistant than other SARS-CoV-2 strains to the current vaccines. Thus, the current research is focused to design multi-epitope subunit Vaccine (MEV) using structural vaccinology techniques. As a result, the designed MEV exhibit antigenic properties and possess therapeutic features that can trigger an immunological response against COVID-19. Furthermore, validation of the MEV using immune simulation and in silico cloning revealed that the proposed vaccine candidate effectively triggered the immune response. Conclusively, the developed MEV needs further wet lab exploration and could be a viable vaccine to manage and prevent COVID-19.

Selection and T-cell antigenicity of synthetic long peptides derived from SARS-CoV-2

The pandemic caused by SARS-CoV-2 has led to the successful development of effective vaccines however the prospect of variants of SARS-CoV-2 and future coronavirus outbreaks necessitates the investigation of other vaccine strategies capable of broadening vaccine mediated T-cell responses and potentially providing cross-immunity. In this study the SARS-CoV-2 proteome was assessed for clusters of immunogenic epitopes restricted to diverse human leucocyte antigen. These regions were then assessed for their conservation amongst other coronaviruses representative of different alpha and beta coronavirus genera. Sixteen highly conserved peptides containing numerous HLA class I and II restricted epitopes were synthesized from these regions and assessed in vitro for their antigenicity against T-cells from individuals with previous SARS-CoV-2 infection. Monocyte derived dendritic cells were generated from these peripheral blood mononuclear cells (PBMC), loaded with SARS-CoV-2 peptides, and used to induce autologous CD4+ and CD8+ T cell activation. The SARS-CoV-2 peptides demonstrated antigenicity against the T-cells from individuals with previous SARS-CoV-2 infection indicating that this approach holds promise as a method to activate anti-SAR-CoV-2 T-cell responses from conserved regions of the virus which are not included in vaccines utilising the Spike protein.

Implementation of immuno-chemoinformatics approaches to construct multi-epitope for vaccine development against Omicron and Delta SARS-CoV-2 variants

Background

The new coronavirus is still a life-threatening menace, because of its changing nature and capacity to produce many mutations to bypass the immune system. The vaccination is the first effective weapon against COVID-19.

Aim

The study's goal was to design a multi-epitope peptide vaccine (MEPV) for a mix of Omicron and Delta Coronavirus strains using immuno-chemoinformatics tools.

Methods

To create the vaccine epitopes, seven proteins from the Omicron and Delta coronavirus strains were selected (ORF1a, ORF3a, surface protein, membrane protein, ORF7a, ORF8, and nucleocapsid protein). Antigenicity, toxicity, and allergenicity of the epitopes were evaluated.

Results

The designed vaccine is made up of 534 amino acids that are homogeneous, antigenic, and non-toxic. Sticky restriction enzymes (XhoI and XbaI) were used to incorporate the MEPV into the pmirGLO luciferase vector. SnapGene server was used to create primers for PCR testing. Developing the MEPV is a terrific cost-effective strategy. The created MEPV's physiochemical properties have been determined to be basic, hydrophobic, and stableImmunogenicity and immune response profiles of the developed vaccine candidate were better assessed using in silico immunological simulations.

Conclusions

We advocate moving the built vaccine to the biological validation step, where it may test our findings using appropriate model organisms.

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.

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson–Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

Vaccines Targeting Numerous Coronavirus Antigens, Ensuring Broader Global Population Coverage: Multi-epitope and Multi-patch Vaccines

Coronaviruses are causative agents of different zoonosis including SARS, MERS, or COVID-19 in humans. The high transmission rate of coronaviruses, the time-consuming development of efficient anti-infectives and vaccines, the possible evolutionary adaptation of the virus to conventional vaccines, and the challenge to cover broad human population worldwide are the major reasons that made it challenging to avoid coronaviruses outbreaks. Although, a plethora of different approaches are being followed to design and develop vaccines against coronaviruses, most of them target subunits, full-length single, or only a very limited number of proteins. Vaccine targeting multiple proteins or even the entire proteome of the coronavirus is yet to come. In the present chapter, we will be discussing multi-epitope vaccine (MEV) and multi-patch vaccine (MPV) approaches to design and develop efficient and sustainably successful strategies against coronaviruses. MEV and MPV utilize highly conserved, potentially immunogenic epitopes and antigenic patches, respectively, and hence they have the potential to target large number of coronavirus proteins or even its entire proteome, allowing us to combat the challenge of its evolutionary adaptation. In addition, the large number of human leukocyte antigen (HLA) alleles targeted by the chosen specific epitopes enables MEV and MPV to cover broader global population.

Natural medicinal plant products as an immune-boosters: A possible role to lessen the impact of Covid-19

Transmissible Covid-19, caused by novel corona virus since last of 2019 has outspread widely until now. Where, India was the second most affected country and 3rd in mortality rate. In world ancient history, medicinal plants were played a crucial role to cure several diseases. In present study, we show some novel natural medicinal plant metabolites as the potential inhibitors against papain-like protease (PLpro), main protease (Mpro) and RNA-dependent RNA polymerase (RdRp), transmembrane proteinase Serine 2 (TMPRSS2) and angiotensin converting enzyme-2 (ACE-2) of Covid-19. Plant metabolites were having been proven to inhibit SARS-CoVs, which also actively walkable against Covid-19.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.