Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach

Immunoinformatic approach for designing a multi-epitope vaccine against non-typhoidal salmonellosis using starvation-stress response proteins from Salmonella Oranienburg

Non-typhoidal Salmonella is responsible for gastrointestinal illnesses worldwide. Therefore, it is important to implement effective therapeutic interventions for preventing these diseases. Vaccines have proven highly efficacious in the treatment and prevention of several illnesses. Nevertheless, there is currently no authorized vaccine available for non-typhoidal salmonellosis. This study aimed to employ in silico techniques to develop a multi-epitope vaccine targeting non-typhoidal salmonellosis. Specifically, we focused on proteins associated with the starvation stress response (SSR) in Salmonella Oranienburg. The presence of these proteins is essential for the survival and disease of the host organism. The vaccine sequence was constructed utilizing B-cell and T-cell epitopes. Linkers, adjuvants and PADRE sequences were used to establish connections between epitopes. The vaccine exhibited no allergenicity, toxigenicity and a significantly high antigenicity score. Docking analysis conducted between the designed vaccine and the TLR-1, TLR-2 and TLR-4 receptors demonstrated favorable interactions and the potential to activate these receptors. In addition, it was found through immunological simulation testing that the vaccine elicits a robust immune response. The use of these proteins in the construction of a multi-epitope vaccine shows potential in terms of both safety and immunogenicity.

Synthesis of an Adjuvant-Free Single Polypeptide-Based Tuberculosis Subunit Vaccine that Elicits In Vivo Immunogenicity in Rats

A novel tuberculosis subunit vaccine specific for Mycobacterium tuberculosis dual antigens, culture filtrate protein-10 (CFP-10) and antigen 85B (Ag85B) conjugated with cholera toxin non-toxic B subunit (CTB), was expressed as a single polypeptide in high amounts and cost-effectively in Escherichia coli. The recovery and purification conditions for the recombinant fusion protein were established. This simple peptide vaccine required no exogenous adjuvant as it contained CTB, a potent immune modulator. The vaccine's physiochemical, structural, and immunological properties were determined using the in-silico tools. It was highly antigenic, non-allergenic, and non-toxic. Its BlastP search with human proteomes excluded the chances of autoimmune reactions. The tertiary structure model (3D) was validated by Ramachandran plot assessment. The 3D structure docking with Toll-like receptors, TLR-1, 2, 4, and 6, showed that the binding affinity between the vaccine peptide and TLRs was high, and their complex was stable, indicating a strong immune response. The in-silico immune simulation revealed the vaccine-induced both innate and adaptive immune responses. In-vivo validation of the immunogenicity of CTB.CFP10.Ag85B in Wistar rats revealed higher activation of IgG immune response compared to either antigen protein. Similar results were also obtained using the C-ImmSim simulation online server. A comparison of immunogenicity of CTB.CFP10.Ag85B with the only available TB vaccine, Bacillus Calmette-Guérin (BCG) or as a booster after vaccination of Wistar rats with BCG, indicated that the IgG levels were the highest in rats vaccinated with BCG, followed by a booster dose of CTB.CFP10.Ag85B fusion protein. The fusion protein would be a safe potential vaccine booster candidate in BCG-primed individuals against TB.

The Yin and Yang of TLR4 in COVID-19

Various pattern recognition receptors (PRRs), including toll-like receptors (TLRs), play a crucial role in recognizing invading pathogens as well as damage-associated molecular patterns (DAMPs) released in response to infection. The resulting signaling cascades initiate appropriate immune responses to eliminate these pathogens. Current evidence suggests that SARS-CoV-2-driven activation of TLR4, whether through direct recognition of the spike glycoprotein (alone or in combination with endotoxin) or by sensing various TLR4-activating DAMPs or alarmins released during viral infection, acts as a critical mediator of antiviral immunity. However, TLR4 exerts a dual role in COVID-19, demonstrating both beneficial and deleterious effects. Dysregulated TLR4 signaling is implicated in the proinflammatory consequences linked to the immunopathogenesis of COVID-19. Additionally, TLR4 polymorphisms contribute to severity of the disease. Given its significant immunoregulatory impact on COVID-19 immunopathology and host immunity, TLR4 has emerged as a key target for developing inhibitors and immunotherapeutic strategies to mitigate the adverse effects associated with SARS-CoV-2 and related infections. Furthermore, TLR4 agonists are also being explored as adjuvants to enhance immune responses to SARS-CoV-2 vaccines.

Molecular screening and dynamics simulation reveal potential phytocompounds in Swertia chirayita targeting the UspA1 protein of Moraxella catarrhalis for COPD therapy

Chronic obstructive pulmonary disease (COPD) is a global health burden, with Moraxella catarrhalis significantly contributing to acute exacerbations and increased healthcare challenges. This study aimed to identify potential drug candidates in Swertia chirayita, a traditional Himalayan medicinal plant, demonstrating efficacy against the ubiquitous surface protein A1 (UspA1) of M. catarrhalis through an in-silico computational approach. The three-dimensional structures of 46 phytocompounds of S. chirayita were retrieved from the IMPPAT 2.0 database. The structures underwent thorough analysis and screening, emphasizing key factors such as binding energy, molecular docking performance, drug-likeness, and toxicity prediction to assess their therapeutic potential. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five phytocompounds such as beta-amyrin, calendol, episwertenol, kairatenol and swertanone were identified as the inhibitors of the UspA1 in M. catarrhalis. UspA1 demonstrated binding affinities of -9.1 kcal/mol for beta-amyrin, -8.9 kcal/mol for calendol, -9.4 kcal/mol for episwertenol, -9.6 kcal/mol for kairatenol, and -9.0 kcal/mol for swertanone. All of these affinities were stronger than that of the control drug ceftobiprole, which had a binding score of -6.6 kcal/mol. The toxicity analysis confirmed that all five compounds are safe potential therapeutic options, showing no toxicity or carcinogenicity. We also performed a 100 ns molecular dynamics simulation of the phytocompounds to analyze their stability and interactions as protein-ligand complexes. Among the five screened phytocompounds, beta-amyrin and episwertenol exhibited favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations, and consistent radius of gyration values. Throughout the simulations, intermolecular interactions such as hydrogen bonds and hydrophobic contacts were maintained. Additionally, the compounds demonstrated strong affinity, as indicated by negative binding free energy values. Taken together, findings of this study strongly suggest that beta-amyrin and episwertenol have the potential to act as inhibitors against the UspA1 protein of M. catarrhalis, offering promising prospects for the treatment and management of COPD.

Design and Immune Profile of Multi-Epitope Synthetic Antigen Vaccine Against SARS-CoV-2: An In Silico and In Vivo Approach

BACKGROUND

The rapid advancement of the pandemic caused by SARS-CoV-2 and its variants reinforced the importance of developing easy-to-edit vaccines with fast production, such as multi-epitope DNA vaccines. The present study aimed to construct a synthetic antigen multi-epitope SARS-CoV-2 to produce a DNA vaccine.

METHODS

A database of previously predicted Spike and Nucleocapsid protein epitopes was created, and these epitopes were analyzed for immunogenicity, conservation, population coverage, and molecular docking.

RESULTS

A synthetic antigen with 15 epitopes considered immunogenic, conserved even in the face of variants and that were able to anchor themselves in the appropriate HLA site, together had more than 90% worldwide coverage. A multi-epitope construct was developed with the sequences of these peptides separated from each other by linkers, cloned into the pVAX1 vector. This construct was evaluated in vivo as a DNA vaccine and elicited T CD4+ and T CD8+ cell expansion in the blood and spleen. In hematological analyses, there was an increase in lymphocytes, monocytes, and neutrophils between the two doses. Furthermore, based on histopathological analysis, the vaccines did not cause any damage to the organs analyzed.

CONCLUSIONS

The present study generated a multi-epitope synthetic vaccine antigen capable of generating antibody-mediated and cellular immune responses.

A Bibliometric Analysis on Multi-epitope Vaccine Development Against SARS-CoV-2: Current Status, Development, and Future Directions

The etiological agent for the coronavirus disease 2019 (COVID-19), the SARS-CoV-2, caused a global pandemic. Although mRNA, viral-vectored, DNA, and recombinant protein vaccine candidates were effective against the SARS-CoV-2 Wuhan strain, the emergence of SARS-CoV-2 variants of concern (VOCs) reduced the protective efficacies of these vaccines. This necessitates the need for effective and accelerated vaccine development against mutated VOCs. The development of multi-epitope vaccines against SARS-CoV-2 based on in silico identification of highly conserved and immunogenic epitopes is a promising strategy for future SARS-CoV-2 vaccine development. Considering the evolving landscape of the COVID-19 pandemic, we have conducted a bibliometric analysis to consolidate current findings and research trends in multi-epitope vaccine development to provide insights for future vaccine development strategies. Analysis of 102 publications on multi-epitope vaccine development against SARS-CoV-2 revealed significant growth and global collaboration, with India leading in the number of publications, along with an identification of the most prolific authors. Key journals included the Journal of Biomolecular Structure and Dynamics, while top collaborations involved Pakistan-China and India-USA. Keyword analysis showed a prominent focus on immunoinformatics, epitope prediction, and spike glycoprotein. Advances in immunoinformatics, including AI-driven epitope prediction, offer promising avenues for the development of safe and effective multi-epitope vaccines. Immunogenicity may be further improved through nanoparticle-based systems or the use of adjuvants along with real-time genomic surveillance to tailor vaccines against emerging variants.

In silico and immunoinformatics based multiepitope subunit vaccine design for protection against visceral leishmaniasis

Visceral leishmaniasis (VL) is a vector-borne neglected tropical protozoan disease with high fatality and no certified vaccine. Conventional vaccine preparation is challenging and tedious. Here in this work, we created a global multiepitope subunit vaccination against VL utilizing innovative immunoinformatics technique based on the extensively conserved epitopic regions of the PrimPol protein of Leishmania donovani consisting of four subunits which were analyzed and studied, out of which DNA primase large subunit and DNA polymerase α subunit B were evaluated as antigens by Vaxijen 2.0. The multiepitope vaccine design includes a single adjuvant β-defensins, eight CTL epitopes, eight HTL epitopes, seven linear BCL epitopes and one discontinuous BCL epitope to induce innate, cellular and humoral immune responses against VL. The Expasy ProtParam tool characterized the physiochemical parameters of the vaccine. At the same time, SOLpro evaluated our vaccine constructs to be soluble upon expression. We also modeled the stable tertiary structure of our vaccine construct through Robetta modeling for molecular docking studies with toll-like receptor proteins through HADDOCK 2.4. Simulations based on molecular dynamics revealed an intact vaccine and TLR8 complex, supporting our vaccine design's immunogenicity. Also, the immune simulation of our vaccine by the C-ImmSim server demonstrated the potency of the multiepitope vaccine construct to induce proper immune response for host defense. Codon optimization and in silico cloning of our vaccine further assured high expression. The outcomes of our study on multiepitope vaccine design significantly produced a potential candidate against VL and can potentially eradicate the disease in the future after clinical investigations.Communicated by Ramaswamy H. Sarma.

Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines

Introduction

BCoV is one of the significant causes of enteritis in young calves; it may also be responsible for many respiratory outbreaks in young calves. BCoV participates in the development of bovine respiratory disease complex in association with other bacterial pathogens. Our study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust immune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multiepitope-based vaccines.

Materials and Methods

The goal is achieved through several integrated in silico prediction computational tools to map these epitopes within the major BCoV structural proteins. The final vaccine was constructed in conjugation with the Choleratoxin B toxin as an adjuvant. The tertiary structure of each vaccine construct was modeled through the AlphaFold2 tools. The constructed vaccine was linked to some immunostimulants such as Toll-like receptors (TLR2 and TLR4). We also predicted the affinity binding of these vaccines with this targeted protein using molecular docking. The stability and purity of each vaccine construct were assessed using the Ramachandran plot and the Z-score values. We created the in silico cloning vaccine constructs using various expression vectors through vector builder and Snap gene.

Results and discussion

The average range of major BCoV structural proteins was detected within the range of 0.4 to 0.5, which confirmed their antigen and allergic properties. The binding energy values were detected between -7.9 and -9.4 eV and also confirmed their best interaction between our vaccine construct and Toll-like receptors. Our in silico cloning method expedited the creation of vaccine constructs and established a strong basis for upcoming clinical trials and experimental validations.

Conclusion

Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high-water solubility. Further studies are highly encouraged to validate the efficacy of these novel BCoV vaccines in the natural host.

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.

Exploring stevioside binding affinity with various proteins and receptors actively involved in the signaling pathway and a future candidate for diabetic patients

Introduction and Background: Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and is one of the main global health concerns. Synthetic sugar substrate has many side effects such as leukemia, bladder cancer, hepatotoxicity, breast cancer, headache, and brain toxicity. The WHO and FDA has recently banned some of the synthetic sugar alternatives due to their carcinogenic effects. Objective and Methodology: Therefore, the main objective of the current study was to investigate the safety and binding affinity of Stevioside with Glucose Transpoter-4 (GLUT-4), Akt, Insulin Receptor (IR) and Insulin Receptor Substrate-1 (IRS-1) to confirmed that Stevioside is one the potent natural sweetener/drug for diabetes. This study delves into the molecular interaction between Stevioside and key diabetic proteins: GLUT-4, Akt, IR and IRS-1. A precise molecular docking approach was used to simulate the binding affinity of Stevioside to these proteins. The pharmacokinetic properties of the molecule should be taken into consideration as important variables throughout the virtual screening process. Results: The result of active site analysis of GLUT-4, Akt, IR and IRS-1 showed a zone of 2158.359 Ǻ2, 579.259 Ǻ2, 762.651 Ǻ2, and 152.167 Ǻ2 and a volume of 2765.094 Ǻ³, 355.567 Ǻ³, 686.806 Ǻ³, and 116.874 Ǻ³, respectively. Docking analysis of the Stevioside compound showed the highest docking energy with scores of -9.9 with GLUT-4, -6.7 with Akt, -8.0 with IR and -8.8 with IRS-1. Studies indicated that it remains undigested by stomach acids and enzymes and is not absorbed in the upper small intestine. Further, tests revealed no hepatotoxicity, AMES toxicity, or skin sensitivity, making it a promising candidate for safe consumption as drug metabolism. Conclusion and Recommendations: Instead of other sugar alternatives, Stevioside will help diabetic patients with a lower chance of infections, lowered blood pressure/blood sugar, and increased glucose uptake in diabetic muscles. Stevioside is a natural sweetener, and the current study recommends its usage in various dietary products for diabetic patients.

In-silico study unveils potential phytocompounds in Andrographis paniculata against E6 protein of the high-risk HPV-16 subtype for cervical cancer therapy

Despite therapeutic advancements, cervical cancer caused by high-risk subtypes of the human papillomavirus (HPV) remains a leading cause of cancer-related deaths among women worldwide. This study aimed to discover potential drug candidates from the Asian medicinal plant Andrographis paniculata, demonstrating efficacy against the E6 protein of high-risk HPV-16 subtype through an in-silico computational approach. The 3D structures of 32 compounds (selected from 42) derived from A. paniculata, exhibiting higher binding affinity, were obtained from the PubChem database. These structures underwent subsequent analysis and screening based on criteria including binding energy, molecular docking, drug likeness and toxicity prediction using computational techniques. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five compounds-stigmasterol, 1H-Indole-3-carboxylic acid, 5-methoxy-, methyl ester (AP7), andrographolide, apigenin and wogonin-were selected as the potential inhibitors against the E6 protein of HPV-16. We also performed 200 ns molecular dynamics simulations of the compounds to analyze their stability and interactions as protein-ligand complexes using imiquimod (CID-57469) as a control. Screened compounds showed favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations and consistent radius of gyration values. Intermolecular interactions, such as hydrogen bonds and hydrophobic contacts, were sustained throughout the simulations. The compounds displayed potential affinity, as indicated by negative binding free energy values. Overall, findings of this study suggest that the selected compounds have the potential to act as inhibitors against the E6 protein of HPV-16, offering promising prospects for the treatment and management of CC.

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.

Designing novel peptides for detecting the Omicron variant, specifying SARS-CoV-2, and simultaneously screening coronavirus infections

In this study in silico a candidate diagnostic peptide-based tool was designed in four stages including diagnosis of coronavirus diseases, simultaneously identifying of COVID-19 and SARS from other members of this family, specific identification of SARS-CoV2, and diagnosis of COVID-19 Omicron. Designed candidate peptides consist of four immunodominant peptides from the proteins of the SARS-CoV-2 spike (S) and membrane (M). The tertiary structure of each peptide was predicted. The stimulation ability of the humoral immunity for each peptide was evaluated. Finally, in silico cloning was performed to develop an expression strategy for each peptide. These four peptides have suitable immunogenicity, appropriate construct, and the ability to be expressed in E.coli. These results must be experimentally validated in vitro and in vivo to ensure the immunogenicity of the kit.Communicated by Ramaswamy H. Sarma.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery.

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.

Critical involvement of circular RNAs in virus-associated cancers

Virus-related cancer is cancer where viral infection leads to the malignant transformation of the host's infected cells. Seven viruses (e.g., human papillomavirus (HPV), Epstein-Barr virus (EBV), Kaposi's sarcoma herpesvirus (KSHV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), Human T-lymphotropic virus (HTLV), and Merkel cell polyomavirus (MCV)) that infect humans have been identified as an oncogene and have been associated with several human malignancies. Recently, growing attention has been attracted to exploring the pathogenesis of virus-related cancers. One of the most mysterious molecules involved in carcinogenesis and progression of virus-related cancers is circular RNAs (circRNA). These emerging non-coding RNAs (ncRNAs), due to the absence of 5' and 3' ends, have high stability than linear RNAs and are found in some species across the eukaryotic organisms. Compelling evidence has revealed that viruses also encode a repertoire of circRNAs, as well as dysregulation of these viral circRNAs play a critical role in the pathogenesis and progression of different types of virus-related cancers. Therefore, understanding the exact role and function of the virally encoded circRNAs with virus-associated cancers will open a new road for increasing our knowledge about the RNA world. Hence, in this review, we will focus on emerging roles of virus-encoded circRNAs in multiple cancers, including cervical cancer, gastric cancer, Merkel cell carcinoma, nasopharyngeal carcinoma, Kaposi cancer, and liver cancer.

Some common deleterious mutations are shared in SARS-CoV-2 genomes from deceased COVID-19 patients across continents

The identification of deleterious mutations in different variants of SARS-CoV-2 and their roles in the morbidity of COVID-19 patients has yet to be thoroughly investigated. To unravel the spectrum of mutations and their effects within SARS-CoV-2 genomes, we analyzed 5,724 complete genomes from deceased COVID-19 patients sourced from the GISAID database. This analysis was conducted using the Nextstrain platform, applying a generalized time-reversible model for evolutionary phylogeny. These genomes were compared to the reference strain (hCoV-19/Wuhan/WIV04/2019) using MAFFT v7.470. Our findings revealed that SARS-CoV-2 genomes from deceased individuals belonged to 21 Nextstrain clades, with clade 20I (Alpha variant) being the most predominant, followed by clade 20H (Beta variant) and clade 20J (Gamma variant). The majority of SARS-CoV-2 genomes from deceased patients (33.4%) were sequenced in North America, while the lowest percentage (0.98%) came from Africa. The 'G' clade was dominant in the SARS-CoV-2 genomes of Asian, African, and North American regions, while the 'GRY' clade prevailed in Europe. In our analysis, we identified 35,799 nucleotide (NT) mutations throughout the genome, with the highest frequency (11,402 occurrences) found in the spike protein. Notably, we observed 4150 point-specific amino acid (AA) mutations in SARS-CoV-2 genomes, with D614G (20%) and N501Y (14%) identified as the top two deleterious mutations in the spike protein on a global scale. Furthermore, we detected five common deleterious AA mutations, including G18V, W45S, I33T, P30L, and Q418H, which play a key role in defining each clade of SARS-CoV-2. Our novel findings hold potential value for genomic surveillance, enabling the monitoring of the evolving pattern of SARS-CoV-2 infection, its emerging variants, and their impact on the development of effective vaccination and control strategies.

Immunized mice naturally process in silico-derived peptides from the nucleocapsid of SARS-CoV-2

BACKGROUND

The nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an excellent immunogen that promotes the production of high-titer antibodies. N protein-derived peptides identified using a bioinformatics approach can potentially be used to develop a new generation of vaccines or diagnostic methods for detecting SARS-CoV-2 and its variants. However, further studies must demonstrate their capacity to be naturally processed by the immune system.

OBJECTIVE

We aimed to examine the in vivo processing and recognition of in silico-identified peptides using the serum of immunized animals with the complete protein.

METHODS

Recombinant N (Nrec) protein was subcutaneously administered to six Balb/c mice. Enzyme-linked immunosorbent assay (ELISA), western blotting, dot blotting, and immunoprecipitation were performed to evaluate the recognition of the complete protein and in silico-derived peptides.

RESULTS

The serum of immunized mice recognized ~ 62.5 ng/µL of Nrec with high specificity to linear and conformational epitopes. Dot blot analysis showed that peptides Npep2 and Npep3 were the most reactive.

CONCLUSION

Our data confirm the high immunogenicity of the SARS-CoV-2 N protein and provide evidence on the antigenicity of two peptides located in the N-arm/RNA-binding domain (Npep2) and oligomerization domain/C-tail (Npep3), considered the biologically active site of the N protein.

A new multi-epitope vaccine candidate based on S and M proteins is effective in inducing humoral and cellular immune responses against SARS-CoV-2 variants: an in silico design approach

Available COVID-19 vaccines are primarily based on SARS-CoV-2 spike protein (S). Due to the emergence of new SARS-CoV-2 variants, other virus proteins with more conservancy, such as Membrane (M) protein, are desired for vaccine development. The reverse vaccinology approach was employed to design a multi-epitope SARS-CoV-2 vaccine candidate based on S and M proteins. Cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), linear B-lymphocyte (LBL) and conformational B-lymphocyte (CBL) of S and M proteins were predicted and screened to choose the best epitopes. A multi-epitope vaccine candidate was constructed using selected CTL, HTL and LBL epitopes. The efficiency of the construct in binding to some immune receptors and an RBD-potent neutralizing monoclonal antibody (bebtelovimab) was predicted, and its immunogenicity was simulated. Finally, in silico cloning of the constructed gene was performed. The potency of our construct as a SARS-CoV-2 vaccine was validated using several bioinformatics tools. The simulation results showed that the construct can induce both cellular and humoral immune responses by producing appropriate cytokines, and it can even create an excellent immune memory response. Furthermore, the designed construct interacts with innate immune receptors such as TLR2 and TLR4 and the terminal variable domain of bebtelovimab with high affinity. We developed a multi-epitope construct based on the S and M proteins of the SARS-CoV-2 virus with high immunogenicity potential using the most up-to-date immunoinformatics and computational biology approaches. The actual efficiency of this multi-epitope vaccine should be further evaluated via in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Emergence of Genomic Diversity in the Spike Protein of the "Omicron" Variant

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus) has constantly been evolving into different forms throughout its spread in the population. Emerging SARS-CoV-2 variants, predominantly the variants of concern (VOCs), could have an impact on the virus spread, pathogenicity, and diagnosis. The recently emerged "Omicron" variant has exhibited rapid transmission and divergence. The spike protein of SARS-CoV-2 has consistently been appearing as the mutational hotspot of all these VOCs. In order to determine a deeper understanding of the recently emerged and extremely divergent "Omicron", a study of amino acid usage patterns and their substitution patterns was performed and compared with those of the other four successful variants of concern ("Alpha", "Beta", "Gamma", and "Delta"). We observed that the amino acid usage of "Omicron" has a distinct pattern that distinguishes it from other VOCs and is significantly correlated with the increased hydrophobicity in spike proteins. We observed an increase in the non-synonymous substitution rate compared with the other four VOCs. Considering the phylogenetic relationship, we hypothesized about the functional interdependence between recombination and the mutation rate that might have resulted in a shift in the optimum of the mutation rate for the evolution of the "Omicron" variant. The results suggest that for improved disease prevention and control, more attention should be given to the significant genetic differentiation and diversity of newly emerging variants.

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

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

BCEDB: a linear B-cell epitopes database for SARS-CoV-2

The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection. However, there is still no public repository to host the linear B-cell epitopes for facilitating the development vaccines against SARS-CoV-2. Therefore, we developed BCEDB, a linear B-cell epitopes database specifically designed for hosting, exploring and visualizing linear B-cell epitopes and their features. The database provides a comprehensive repository of computationally predicted linear B-cell epitopes from Spike protein; a systematic annotation of epitopes including sequence, antigenicity score, genomic locations of epitopes, mutations in different virus lineages, mutation sites on the 3D structure of Spike protein and a genome browser to visualize them in an interactive manner. It represents a valuable resource for peptide-based vaccine development. Database URL: http://www.oncoimmunobank.cn/bcedbindex.

Multi-epitope vaccine against drug-resistant strains of Mycobacterium tuberculosis: a proteome-wide subtraction and immunoinformatics approach

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Toll-like receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

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.

A Newly Identified Spike Protein Targeted Linear B-Cell Epitope Based Dissolvable Microneedle Array Successfully Eliciting Neutralizing Activities against SARS-CoV-2 Wild-Type Strain in Mice

Vaccination is a cost-effective medical intervention. Inactivated whole virusor large protein fragments-based severe acute respiratory syndrome coronavirus (SARS-CoV-2) vaccines have high unnecessary antigenic load to induce allergenicity and/orreactogenicity, which can be avoided by peptide vaccines of short peptide fragments that may induce highly targeted immune response. However, epitope identification and peptide delivery remain the major obstacles in developing peptide vaccines. Here, a multi-source data integrated linear B-cell epitope screening strategy is presented and a linear B-cell epitope enriched hotspot region is identified in Spike protein, from which a monomeric peptide vaccine (Epitope25) is developed and applied to subcutaneously immunize wildtype BALB/c mice. Indirect ELISA assay reveals specific and dose-dependent binding between Epitope25 and serum IgG antibodies from immunized mice. The neutralizing activity of sera from vaccinated mice is validated by pseudo and live SARS-CoV-2 wild-type strain neutralization assays. Then a dissolvable microneedle array (DMNA) is developed to pain-freely deliver Epitope25. Compared with intramuscular injection, DMNA and subcutaneous injection elicit neutralizing activities against SARS-CoV-2 wild-type strain as demonstrated by live SARS-CoV-2 virus neutralization assay. No obvious damages are found in major organs of immunized mice. This study may lay the foundation for developing linear B-cell epitope-based vaccines against SARS-CoV-2.

Identification of Leading Compounds from Euphorbia neriifolia (Dudsor) Extracts as a Potential Inhibitor of SARS-CoV-2 ACE2-RBDS1 Receptor Complex: An Insight from Molecular Docking ADMET Profiling and MD-simulation Studies

Coronavirus disease-19 (COVID-19) are deadly and infectious disease that impacts individuals in a variety of ways. Scientists have stepped up their attempts to find an antiviral drug that targets the spike protein (S) of Angiotensin converting enzyme 2 (ACE2) (receptor protein) as a viable therapeutic target for coronavirus. The most recent study examines the potential antagonistic effects of 17 phytochemicals present in the plant extraction of Euphorbia neriifolia on the anti-SARS-CoV-2 ACE2 protein. Computational techniques like molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) investigations, and molecular dynamics (MD) simulation analysis were used to investigate the actions of these phytochemicals. The results of molecular docking studies showed that the control ligand (2-acetamido-2-deoxy-β-D-glucopyranose) had a binding potential of -6.2 kcal/mol, but the binding potentials of delphin, β-amyrin, and tulipanin are greater at -10.4, 10.0, and -9.6 kcal/mol. To verify their drug-likeness, the discovered hits were put via Lipinski filters and ADMET analysis. According to MD simulations of the complex run for 100 numbers, delphin binds to the SARS-CoV-2 ACE2 receptor's active region with good stability. In root-mean-square deviation (RMSD) and root mean square fluctuation (RMSF) calculations, delphinan, β-amyrin, and tulipanin showed reduced variance with the receptor binding domain subunit 1(RBD S1) ACE2 protein complex. The solvent accessible surface area (SASA), radius of gyration (Rg), molecular surface area (MolSA), and polar surface area (PSA) validation results for these three compounds were likewise encouraging. The convenient binding energies across the 100 numbers binding period were discovered by using molecular mechanics of generalized born and surface (MM/GBSA) to estimate the ligand-binding free energies to the protein receptor. All things considered, the information points to a greater likelihood of chemicals found in Euphorbia neriifolia binding to the SARS-CoV-2 ACE2 active site. To determine these lead compounds' anti-SARS-CoV-2 potential, in vitro and in vivo studies should be conducted.

How to cite this article

Islam MN, Pramanik MEA, Hossain MA, et al. Identification of Leading Compounds from Euphorbia Neriifolia (Dudsor) Extracts as a Potential Inhibitor of SARS-CoV-2 ACE2-RBDS1 Receptor Complex: An Insight from Molecular Docking ADMET Profiling and MD-simulation Studies. Euroasian J Hepato-Gastroenterol 2023;13(2):89-107.

Recent Developments and Future Perspectives of Vaccines and Therapeutic Agents against SARS-CoV2 Using the BCOV_S1_CTD of the S Protein

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, the virus kept developing and mutating into different variants over time, which also gained increased transmissibility and spread in populations at a higher pace, culminating in successive waves of COVID-19 cases. The scientific community has developed vaccines and antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease. Realizing that growing SARS-CoV-2 variations significantly impact the efficacy of antiviral therapies and vaccines, we summarize the appearance and attributes of SARS-CoV-2 variants for future perspectives in drug design, providing up-to-date insights for developing therapeutic agents targeting the variants. The Omicron variant is among the most mutated form; its strong transmissibility and immune resistance capacity have prompted international worry. Most mutation sites currently being studied are in the BCOV_S1_CTD of the S protein. Despite this, several hurdles remain, such as developing vaccination and pharmacological treatment efficacies for emerging mutants of SARS-CoV-2 strains. In this review, we present an updated viewpoint on the current issues faced by the emergence of various SARS-CoV-2 variants. Furthermore, we discuss the clinical studies conducted to assist the development and dissemination of vaccines, small molecule therapeutics, and therapeutic antibodies having broad-spectrum action against SARS-CoV-2 strains.

Temporal dynamics and fatality of SARS-CoV-2 variants in Bangladesh

Background and Aims

Since the beginning of the SARS-CoV-2 pandemic, multiple new variants have emerged posing an increased risk to global public health. This study aimed to investigate SARS-CoV-2 variants, their temporal dynamics, infection rate (IFR) and case fatality rate (CFR) in Bangladesh by analyzing the published genomes.

Methods

We retrieved 6610 complete whole genome sequences of the SARS-CoV-2 from the GISAID (Global Initiative on Sharing all Influenza Data) platform from March 2020 to October 2022, and performed different in-silico bioinformatics analyses. The clade and Pango lineages were assigned by using Nextclade v2.8.1. SARS-CoV-2 infections and fatality data were collected from the Institute of Epidemiology Disease Control and Research (IEDCR), Bangladesh. The average IFR was calculated from the monthly COVID-19 cases and population size while average CFR was calculated from the number of monthly deaths and number of confirmed COVID-19 cases.

Results

SARS-CoV-2 first emerged in Bangladesh on March 3, 2020 and created three pandemic waves so far. The phylogenetic analysis revealed multiple introductions of SARS-CoV-2 variant(s) into Bangladesh with at least 22 Nextstrain clades and 107 Pangolin lineages with respect to the SARS-CoV-2 reference genome of Wuhan/Hu-1/2019. The Delta variant was detected as the most predominant (48.06%) variant followed by Omicron (27.88%), Beta (7.65%), Alpha (1.56%), Eta (0.33%) and Gamma (0.03%) variant. The overall IFR and CFR from circulating variants were 13.59% and 1.45%, respectively. A time-dependent monthly analysis showed significant variations in the IFR (p = 0.012, Kruskal-Wallis test) and CFR (p = 0.032, Kruskal-Wallis test) throughout the study period. We found the highest IFR (14.35%) in 2020 while Delta (20A) and Beta (20H) variants were circulating in Bangladesh. Remarkably, the highest CFR (1.91%) from SARS-CoV-2 variants was recorded in 2021.

Conclusion

Our findings highlight the importance of genomic surveillance for careful monitoring of variants of concern emergence to interpret correctly their relative IFR and CFR, and thus, for implementation of strengthened public health and social measures to control the spread of the virus. Furthermore, the results of the present study may provide important context for sequence-based inference in SARS-CoV-2 variant(s) evolution and clinical epidemiology beyond Bangladesh.

In-silico prediction of highly promising natural fungicides against the destructive blast fungus Magnaporthe oryzae

Magnaporthe oryzae causes destructive blast disease in more than 50 species of the major cereal crops rice, wheat and maize and destroys food of millions of people worldwide. Application of synthetic chemical fungicides are environmentally hazardous and unreliable in controlling M. oryzae. Conversely, naturally occurring biofungicides with multiple modes of actions are needed to be discovered for combatting the blast fungus. To find the effective biofungicides, we performed molecular docking study of some potential antifungal natural compounds targeting two proteins including a single-stranded DNA binding protein MoSub1 (4AGH), and an effector protein AVR-Pik (5E9G) of M. oryzae that regulates transcription in fungus and/or suppresses the host cell immunity. The thirty-nine natural compounds previously shown to inhibit M. oryzae growth and reproduction were put under molecular docking against these two proteins followed by simulation, free energy, and interaction analysis of protein-ligand complexes. The virtual screening revealed that two alkaloidal metabolites, camptothecin and GKK1032A2 showed excellent binding energy with any of these target proteins compared to reference commercial fungicides, azoxystrobin and strobilurin. Of the detected compounds, GKK1032A2 bound to both target proteins of M. oryzae. Both compounds showed excellent bioactivity scores as compared to the reference fungicides. Results of our computational biological study suggest that both camptothecin and GKK1032A2 are potential fungicides that could also be considered as lead compounds to design novel fungicides against the blast fungus. Furthermore, the GKK1032A2 acted as a multi-site mode of action fungicide against M. oryzae.

TYK2 single-nucleotide variants associated with the severity of COVID-19 disease

Coronavirus disease 2019 (COVID-19) is a lethal disease caused by the coronavirus SARS-CoV-2, which can result in a broad clinical spectrum of respiratory symptoms. While many clinical risk factors such as concomitant chronic diseases play roles in the pathophysiology of COVID-19, genetic predisposition factors have not been widely studied. The aim of this study was, therefore, to evaluate the relationship between some singlenucleotide polymorphisms (SNPs) of the human genes TYK2 and ACE2 and the severity of SARS-CoV-2 infection. Genomic DNA was isolated from 200 SARS-CoV-2-infected individuals with severe (n = 100) or mild (n = 100) disease. Owing to the importance of ACE2 and TYK2 genes in regulating the immune response to SARS-CoV-2 infection, TYK2 gene SNPs, i.e. rs2304255, rs2304256, rs12720270, and rs12720354 and ACE2 rs382746 variants, were genotyped in the samples. To confirm the results, the expression of different TYK2 genotypes was investigated using real-time PCR. The presence of the nucleotide T at the locus rs2304255 was shown to be a risk factor linked to disease severity (OR [95% CI] = 3.2485 [2.1554-4.8961]). Similarly, the presence of A at the locus rs12720354 increased the risk of severity (OR [95% CI]) = 3.9721 [2.6075-6.0509]). In contrast, the presence of A at the loci rs2304256 and rs12720270 was observed to reduce the severity risk (OR [95% CI] = 0.2495 [0.1642-0.3793] and 0.1668 [0.1083-0.2569], respectively). Real-time PCR results also demonstrated that the expression level of TYK2 in samples with the TT genotype of rs2304255 and the AA genotype of rs12720354 and in samples with the GG genotype of rs12720207 was significantly lower than in those with other genotypes. The results of this study suggest that TYK2 SNPs might be utilized to identify individuals who are at risk for severe COVID-19, in order to better manage their health care. It is predicted that the presence of some alleles (T in rs2304255, A in rs12720354, and G in rs12720207) of TYK2 can affect COVID-19 severity by reducing TYK2 expression and thereby affecting the regulatory role of TYK2 in the immune response.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

In silico screening and epitope mapping of leptospiral outer membrane protein-Lsa46

Leptospirosis is one of the neglected diseases caused by the spirochete, Leptospira interrogans. Leptospiral surface adhesion (Lsa) proteins are surface exposed outer membrane proteins present in the pathogen. It acts as laminin and plasminogen binding proteins which enable them to infect host cells. The major target for the development of vaccine in the current era focuses on surface exposed outer membrane proteins, as they can induce strong and fast immune response in hosts. Therefore, the present study mapped the potential epitopes of the Leptospiral outer membrane proteins, mainly the surface adhesion proteins. Protein sequence analysis of Lsa proteins was done by in silico methods. The primary protein sequence analysis revealed Lsa46 as a suitable target which can be a potent Leptospiral vaccine candidate. Its structure was modelled by threading based method in I-TASSER server and validated by Ramachandran plot. The predicted epitope's interactions with human IgG, IgM(Fab) and T-cell receptor TCR(αβ) were performed by molecular docking studies using Biovia Discovery studio 2018. One of the predicted B-cell epitopes and the IgG showed desirable binding interactions, while four of the predicted B-cell epitopes and T-cell epitopes showed desirable binding interactions with IgM and TCR respectively. The molecular dynamic simulation studies carried out with the molecular docked complexes gave minimized energies indicating stable interactions. The structural analysis of the entire simulated complex showed a stable nature except for one of the Epitope-IgM complex. Further the binding free energy calculation of eight receptor-ligand complex predicted them energetically stable. The results of the study help in elucidating the structural and functional characterization of Lsa46 for epitope-based vaccine design.Communicated by Ramaswamy H. Sarma.

Intermodulation of gut-lung axis microbiome and the implications of biotics to combat COVID-19

The novel coronavirus disease pandemic caused by the COVID-19 virus has infected millions of people around the world with a surge in transmission and mortality rates. Although it is a respiratory viral infection that affects airway epithelial cells, a diverse set of complications, including cytokine storm, gastrointestinal disorders, neurological distress, and hyperactive immune responses have been reported. However, growing evidence indicates that the bidirectional crosstalk of the gut-lung axis can decipher the complexity of the disease. Though not much research has been focused on the gut-lung axis microbiome, there is a translocation of COVID-19 infection from the lung to the gut through the lymphatic system resulting in disruption of gut permeability and its integrity. It is believed that detailed elucidation of the gut-lung axis crosstalk and the role of microbiota can unravel the most significant insights on the discovery of diagnosis using microbiome-based-therapeutics for COVID-19. This review calls attention to relate the influence of dysbiosis caused by COVID-19 and the involvement of the gut-lung axis. It presents first of its kind details that concentrate on the momentousness of biotics in disease progression and restoration. Communicated by Ramaswamy H. Sarma.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Pea eggplant (Solanum torvum Swartz) is a source of plant food polyphenols with SARS-CoV inhibiting potential

Background

Pea eggplant (Solanum torvum Swartz) commonly known as turkey berry or 'terung pipit' in Malay is a vegetable plant widely consumed by the local community in Malaysia. The shrub bears pea-like turkey berry fruits (TBFs), rich in phytochemicals of medicinal interest. The TBF phytochemicals hold a wide spectrum of pharmacological properties. In this study, the TBF phytochemicals' potential inhibitory properties were evaluated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the Coronavirus disease 2019 (COVID-19). The TBF polyphenols were screened against SARS-CoV receptors via molecular docking and the best receptor-ligand complex was validated further by molecular dynamics (MD) simulation.

Method

The SARS-CoV receptor structure files (viral structural components) were retrieved from the Protein Data Bank (PDB) database: membrane protein (PDB ID: 3I6G), main protease (PDB ID: 5RE4), and spike glycoproteins (PDB ID: 6VXX and 6VYB). The receptor binding pocket regions were identified by Discovery Studio (BIOVIA) for targeted docking with TBF polyphenols (genistin, kaempferol, mellein, rhoifolin and scutellarein). The ligand and SARS-CoV family receptor structure files were pre-processed using the AutoDock tools. Molecular docking was performed with the Lamarckian genetic algorithm using AutoDock Vina 4.2 software. The best pose (ligand-receptor complex) from the molecular docking analysis was selected based on the minimum binding energy (MBE) and extent of structural interactions, as indicated by BIOVIA visualization tool. The selected complex was validated by a 100 ns MD simulation run using the GROMACS software. The dynamic behaviour and stability of the receptor-ligand complex were evaluated by the root mean square displacement (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), solvent accessible surface volume (SASV) and number of hydrogen bonds.

Results

At RMSD = 0, the TBF polyphenols showed fairly strong physical interactions with SARS-CoV receptors under all possible combinations. The MBE of TBF polyphenol-bound SARS CoV complexes ranged from -4.6 to -8.3 kcal/mol. Analysis of the structural interactions showed the presence of hydrogen bonds, electrostatic and hydrophobic interactions between the receptor residues (RR) and ligands atoms. Based on the MBE values, the 3I6G-rhoifolin (MBE = -8.3 kcal/mol) and 5RE4-genistin (MBE = -7.6 kcal/mol) complexes were ranked with the least value. However, the latter showed a greater extent of interactions between the RRs and the ligand atoms and thus was further validated by MD simulation. The MD simulation parameters of the 5RE4-genistin complex over a 100 ns run indicated good structural stability with minimal flexibility within genistin binding pocket region. The findings suggest that S. torvum polyphenols hold good therapeutics potential in COVID-19 management.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

Highly potent natural fungicides identified in silico against the cereal killer fungus Magnaporthe oryzae

Magnaporthe oryzae is one of the most notorious fungal pathogens that causes blast disease in cereals, and results in enormous loss of grain production. Many chemical fungicides are being used to control the pathogen but none of them are fully effective in controlling blast disease. Therefore, there is a demand for the discovery of a new natural biofungicide to manage the blast disease efficiently. A large number of new natural products showed inhibitory activities against M. oryzae in vitro. To find out effective biofungicides, we performed in silico molecular docking analysis of some of the potent natural compounds targeting four enzymes namely, scytalone dehydratase, SDH1 (PDB ID:1STD), trihydroxynaphthalene reductase, 3HNR (PDB ID:1YBV), trehalose-6-phosphate synthase, Tps1 (PDB ID:6JBI) and isocitrate lyase, ICL1 (PDB ID:5E9G) of M. oryzae fungus that regulate melanin biosynthesis and/or appresorium formation. Thirty-nine natural compounds that were previously reported to inhibit the growth of M. oryzae were subjected to rigid and flexible molecular docking against aforementioned enzymes followed by molecular dynamic simulation. The results of virtual screening showed that out of 39, eight compounds showed good binding energy with any one of the target enzymes as compared to reference commercial fungicides, azoxystrobin and strobilurin. Among the compounds, camptothecin, GKK1032A2 and chaetoviridin-A bind with more than one target enzymes of M. oryzae. All of the compounds except tricyclazole showed good bioactivity score. Taken together, our results suggest that all of the eight compounds have the potential to develop new fungicides, and remarkably, camptothecin, GKK1032A2 and chaetoviridin-A could act as multi-site mode of action fungicides against the blast fungus M. oryzae.

Therapeutic Strategies in the Fight against COVID-19: From Bench to Bedside

In December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China. This virus rapidly spread worldwide and was declared a global pandemic by the World Health Organization (WHO) in March 2020. High incidence, long incubation period, and diverse clinical signs of the disease posed a huge challenge globally. The efforts of health systems have been focused on repurposing existing drugs or developing innovative therapies to reduce the morbidity and mortality associated with SARS-CoV-2. In addition, most of the large pharmaceutical companies are intensely working on vaccine development to swiftly deliver safe and effective vaccines to prevent further spread of the virus. In this review, we will discuss the latest data on therapeutic strategies undergoing clinical trials. Additionally, we will provide a summary of vaccines currently under development.

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.

Rab7 Investigation Insights into the Existence of White Spot Syndrome Virus in Crustaceans: An In Silico Approach

In this study, previously published Rab7 sequences from National Center for Biotechnology Information (NCBI) have been investigated from chordates, mollusks, annelids, cnidarians, amphibians, priapulids, brachiopods, and arthropods including decapods and other groups. Among decapod crustacean isolates, amino acid variations were found in 13 locations. Penaeid shrimps had variations in positions 13 (I ⟶ J), 22 (T ⟶ A), 124 (G ⟶ X), and 149 (V ⟶ X) while interestingly the freshwater prawn and mitten crab both had amino acid substitutions in positions 87 (V ⟶ C) and 95 (T ⟶ S) along with the other disagreements in amino acid positions 178 (S ⟶ N), 201 (D ⟶ E), 181 (E ⟶ D), 182 (L ⟶ I), 183 (Y ⟶ G), 184 (N ⟶ H), and 198 (A ⟶ T). Among 100 isolates of Rab7 from organisms of various phyla, mutations were observed in several positions. These mutations caused variations in hydrophobicity and isoelectric point which impact the ligand-protein binding affinity. Some common mutations were found in the organisms of the same phylum and among different phyla. Homology modeling of Rab7 proteins from different organisms was done using SWISS-MODEL and validated further by developing Ramachandran plots. Protein-protein docking showed that active residues were there in the binding interfaces of Rab7 from organisms of seven different phyla and VP28 of WSSV. Similarities were observed in the Rab7-VP28 complexes in those selected organisms which differed from the Rab7-VP28 complex in the case of Penaeid shrimp. The findings of this study suggest that WSSV may exist in different marine organisms that have Rab7 protein and transmit to crustaceans like shrimps and crabs which are of commercial importance.

Recombinant vaccines in 2022: a perspective from the cell factory

The last big outbreaks of Ebola fever in Africa, the thousands of avian influenza outbreaks across Europe, Asia, North America and Africa, the emergence of monkeypox virus in Europe and specially the COVID-19 pandemics have globally stressed the need for efficient, cost-effective vaccines against infectious diseases. Ideally, they should be based on transversal technologies of wide applicability. In this context, and pushed by the above-mentioned epidemiological needs, new and highly sophisticated DNA-or RNA-based vaccination strategies have been recently developed and applied at large-scale. Being very promising and effective, they still need to be assessed regarding the level of conferred long-term protection. Despite these fast-developing approaches, subunit vaccines, based on recombinant proteins obtained by conventional genetic engineering, still show a wide spectrum of interesting potentialities and an important margin for further development. In the 80's, the first vaccination attempts with recombinant vaccines consisted in single structural proteins from viral pathogens, administered as soluble plain versions. In contrast, more complex formulations of recombinant antigens with particular geometries are progressively generated and explored in an attempt to mimic the multifaceted set of stimuli offered to the immune system by replicating pathogens. The diversity of recombinant antimicrobial vaccines and vaccine prototypes is revised here considering the cell factory types, through relevant examples of prototypes under development as well as already approved products.

Bioinformatics pipeline unveils genetic variability to synthetic vaccine design for Indian SARS-CoV-2 genomes

In the worrisome scenarios of various waves of SARS-CoV-2 pandemic, a comprehensive bioinformatics pipeline is essential to analyse the virus genomes in order to understand its evolution, thereby identifying mutations as signature SNPs, conserved regions and subsequently to design epitope based synthetic vaccine. We have thus performed multiple sequence alignment of 4996 Indian SARS-CoV-2 genomes as a case study using MAFFT followed by phylogenetic analysis using Nextstrain to identify virus clades. Furthermore, based on the entropy of each genomic coordinate of the aligned sequences, conserved regions are identified. After refinement of the conserved regions, based on its length, one conserved region is identified for which the primers and probes are reported for virus detection. The refined conserved regions are also used to identify T-cell and B-cell epitopes along with their immunogenic and antigenic scores. Such scores are used for selecting the most immunogenic and antigenic epitopes. By executing this pipeline, 40 unique signature SNPs are identified resulting in 23 non-synonymous signature SNPs which provide 28 amino acid changes in protein. On the other hand, 12 conserved regions are selected based on refinement criteria out of which one is selected as the potential target for virus detection. Additionally, 22 MHC-I and 21 MHC-II restricted T-cell epitopes with 10 unique HLA alleles each and 17 B-cell epitopes are obtained for 12 conserved regions. All the results are validated both quantitatively and qualitatively which show that from genetic variability to synthetic vaccine design, the proposed pipeline can be used effectively to combat SARS-CoV-2.

Utility of in silico-identified-peptides in spike-S1 domain and nucleocapsid of SARS-CoV-2 for antibody detection in COVID-19 patients and antibody production

SARS-CoV-2 contains four structural proteins, two of which, the spike and nucleocapsid, are commonly used for the standardization of novel methods for antibody detection; however, some limitations in their use have been observed due to the homology of this virus with other phylogenetically-related viruses. We performed in silico analysis to search for novel immunogenic and antigenic peptides. A total of twenty-five peptides were preliminarily selected, located in the 3D structure of both proteins. Finally, eight peptides were selected: one located in the N protein and seven in the S1 domain of the spike protein. Additionally, the localization of selected peptides in 2D structures and possible changes in the sequences of these peptides in SARS-CoV-2 variants of concern were analyzed. All peptides were synthetized in MAP8 format, and recombinant S (trimer and RBD) and N proteins were used as antigens to search for antibodies in serum samples derived from COVID-19 patients, and for antibody response in New Zealand rabbits. Results showed high recognition of the serum derived from COVID-19 patients to all selected peptides; however, only the RBD3 peptide induced antibody production. In conclusion, this work provides evidence for a new strategy in peptide selection and its use for antibody detection or antibody production in animals.

Strain Variation Based on Spike Glycoprotein Gene of SARS-CoV-2 in Kuwait from 2020 to 2021

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which was first identified in Wuhan, China, in December 2019. With the global transmission of the virus, many SARS-CoV-2 variants have emerged due to the alterations of the spike glycoprotein. Therefore, the S glycoprotein encoding gene has widely been used for the molecular analysis of SARS-Co-2 due to its features affecting antigenicity and immunogenicity. We analyzed the S gene sequences of 35 SARS-CoV-2 isolates in Kuwait from March 2020 to February 2021 using the Sanger method and MinION nanopore technology to confirm novel nucleotide alterations. Our results show that the Kuwaiti strains from clade 19A and B were the dominant variants early in the pandemic, while clade 20I (Alpha, V1) was the dominant variant from February 2021 onward. Besides the known mutations, 21 nucleotide deletions in the S glycoprotein in one Kuwaiti strain were detected, which might reveal a recombinant SARS-CoV-2 with the defective viral genome (DVG). This study emphasizes the importance of closely perceiving the emerging clades with these mutations during this continuous pandemic as some may influence the specificity of diagnostic tests, such as RT-PCR and even vaccine design directing these positions.

Considering epitopes conservity in targeting SARS-CoV-2 mutations in variants: a novel immunoinformatics approach to vaccine design

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has gained mutations at an alarming rate in the past years. Developing mutations can increase the virus's pathogenicity and virulence; reduce the efficacy of vaccines, antibodies neutralization, and even challenge adaptive immunity. So, it is essential to identify conserved epitopes (with fewer mutations) in different variants with appropriate antigenicity to target the variants by an appropriate vaccine design. Yet as, 3369 SARS-CoV-2 genomes were collected from global initiative on sharing avian flu data. Then, mutations in the immunodominant regions (IDRs), immune epitope database (IEDB) epitopes, and also predicted epitopes were calculated. In the following, epitopes conservity score against the total number of events (mutations) and the number of mutated sites in each epitope was weighted by Shannon entropy and then calculated by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Based on the TOPSIS conservity score and antigenicity score, the epitopes were plotted. The result demonstrates that almost all epitopes and IDRs with various lengths have gained different numbers of mutations in dissimilar sites. Herein, our two-step calculation for conservity recommends only 8 IDRs, 14 IEDB epitopes, and 10 predicted epitopes among all epitopes. The selected ones have higher conservity and higher immunogenicity. This method is an open-source multi-criteria decision-making platform, which provides a scientific approach to selecting epitopes with appropriate conservity and immunogenicity; against ever-changing viruses.

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG–miRNA, and DEG–transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

The chimera of S1 and N proteins of SARS-CoV-2: can it be a potential vaccine candidate for COVID-19?

INTRODUCTION

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the biggest global health issues. Spike protein (S) and nucleoprotein (N), the major immunogenic components of SARS-CoV-2, have been shown to be involved in the attachment and replication of the virus inside the host cell.

AREAS COVERED

Several investigations have shown that the SARS-CoV-2 nucleoprotein can elicit a cell-mediated immune response capable of regulating viral replication and lowering viral burden. However, the development of an effective vaccine that can stop the transmission of SARS-CoV-2 remains a matter of concern. Literature was retrieved using the keywords COVID-19 vaccine, role of nucleoprotein as vaccine candidate, spike protein, nucleoprotein immune responses against SARS-CoV-2, and chimera vaccine in PubMed, Google Scholar, and Google.

EXPERT OPINION

We have focussed on the use of chimera protein, consisting of N and S-1 protein components of SARS-CoV-2, as a potential vaccine candidate. This may act as a polyvalent mixed recombinant protein vaccine to elicit a strong T and B cell immune response, which will be capable of neutralizing the wild and mutated variants of SARS-CoV-2, and also restricting its attachment, replication, and budding in the host cell.