Designing a multi-epitope peptide based vaccine against SARS-CoV-2

Peptidomics and machine-learning-based evaluation of ncRNA-derived micropeptides in breast cancer: Expression patterns and functional/therapeutic insights

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The ncRNA-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. Firstly, we constructed a 16,349 unique putative MP sequence dataset by integrating two previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared to non-tumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. Also, some MPs presented potential as anti-cancer, anti-inflammatory, and anti-angiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins

Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN-γ and cytokines like IL-2 were released highly.

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.

Prediction of the 3D conformation of a small peptide vaccine targeting Aβ42 oligomers

The original etiology of Alzheimer's disease (AD) is the deposition of amyloid-beta (Aβ) proteins, which starts from the aggregation of the Aβ oligomers. The optimal therapeutic strategy targeting Aβ oligomer aggregation is the development of AD vaccines. Despite the fact that positive progress has been made for experimental attempts at AD vaccines, the physicochemical and even structural properties of these AD vaccines remain unclear. In this study, through immunoinformatic and molecular dynamics (MD) simulations, we first designed and simulated an alternative of vaccine TAPAS and found that the structure of the alternative can reproduce the 3D conformation of TAPAS determined experimentally. Meanwhile, immunoinformatic methods were used to analyze the physicochemical properties of TAPAS, including immunogenicity, antigenicity, thermal stability, and solubility, which confirm well the efficacy and safety of the vaccine, and validate the scheme reliability of immunoinformatic and MD simulations in designing and simulating the TAPAS vaccine. Using the same scheme, we predicted the 3D conformation of the optimized ACI-24 peptide vaccine, an Aβ peptide with the first 15 residues of Aβ42 (Aβ1-15). The vaccine was verified once to be effective against both full-length Aβ1-42 and truncated Aβ4-42 aggregates, but an experimental 3D structure was absent. We have also explored the immune mechanism of the vaccine at the molecular level and found that the optimized ACI-24 and its analogues can block the growth of either full-length Aβ1-42 or truncated Aβ4-42 pentamer by contacting the hydrophobic residues within the N-terminus and β1 region on the contact surface of either pentamer. Additionally, residues (D1, D7, S8, H13, and Q15) were identified as the key residues of the vaccine to contact either of the two Aβ oligomers. This work provides a feasible implementation scheme of immunoinformatic and MD simulations for the development of AD small peptide vaccines, validating the power of the scheme as a parallel tool to the experimental approaches and injecting molecular-level information into the understanding and design of anti-AD vaccines.

Dominant B cell-T cell epitopes instigated robust immune response in-silico against Scrub Typhus

Scrub typhus, a potentially life-threatening infectious disease, is attributed to bacteria Orientia tsutsugamushi (O. tsutsugamushi). The transmission of this illness to humans occurs through the bite of infected chiggers, which are the larval forms of mites belonging to the genus Leptotrombidium. In this research, we developed a subunit vaccine specifically designed to target outer membrane proteins. Immunodominant cytotoxic T-lymphocytes (CTLs), B- lymphocytes (BCLs), and major histocompatibility complex (MHC)- II epitopes were identified using machine learning and bioinformatics approaches. These epitopes were arranged in different combinations with the help of suitable linkers like AAY, KK, GPGPG and adjuvant (cholera toxin B) that resulted in a vaccine construct. Physiochemical properties were assessed, where the predicted solubility (0.571) was higher than threshold value. Tertiary structure was predicted using I-TASSER web server and evaluated using Ramachandran plot (94 % residues in most favourable region) and z-score (-6.04), which had shown the structure to have good stability and residue arrangement. Molecular docking with immune receptors, Toll-like receptor (TLR)-2 and -4 showed good residue interaction with 13 and 5 hydrogen bonds respectively. Molecular dynamics simulations of receptor-ligand complex provided the idea about the strong interaction having 1.524751 × 10-5 eigenvalue. Amino acid sequence of vaccine was converted to nucleotide sequence and underwent codon optimization. The optimized codon sequence was used for in-silico cloning, which provided idea about the possibility of synthesis of vaccine using E. coli as host. Overall, this study provided a promising blueprint for a scrub typhus vaccine, although experimental validation is needed for confirmation. Furthermore, it is crucial to acknowledge that while bioinformatics provides valuable insights, in-vitro and in-vivo studies are imperative for a comprehensive evaluation of vaccine candidate. Thus, the integration of computational predictions with empirical research is essential to validate the efficacy, safety, and real-world applicability of the designed vaccine against Scrub Typhus. Nevertheless, the findings are good to carry forward for in-vitro and in-vivo investigations.

Immuno-informatics study identifies conserved T cell epitopes in non-structural proteins of Bluetongue virus serotypes: formulation of a computationally optimized next-generation broad-spectrum multi-epitope vaccine

Introduction

Bluetongue (BT) poses a significant threat to the livestock industry, affecting various animal species and resulting in substantial economic losses. The existence of numerous BT virus (BTV) serotypes has hindered control efforts, highlighting the need for broad-spectrum vaccines.

Methodology

In this study, we evaluated the conserved amino acid sequences within key non-structural (NS) proteins of BTV and identified numerous highly conserved murine- and bovine-specific MHC class I-restricted (MHC-I) CD8+ and MHC-II-restricted CD4+ epitopes. We then screened these conserved epitopes for antigenicity, allergenicity, toxicity, and solubility. Using these epitopes, we developed in silico-based broad-spectrum multiepitope vaccines with Toll-like receptor (TLR-4) agonists. The predicted proinflammatory cytokine response was assessed in silico using the C-IMMSIM server. Structural modeling and refinement were achieved using Robetta and GalaxyWEB servers. Finally, we assessed the stability of the docking complexes through extensive 100-nanosecond molecular dynamics simulations before considering the vaccines for codon optimization and in silico cloning.

Results

We found many epitopes that meet these criteria within NS1 and NS2 proteins and developed in silico broad-spectrum vaccines. The immune simulation studies revealed that these vaccines induce high levels of IFN-γ and IL-2 in the vaccinated groups. Protein-protein docking analysis demonstrated promising epitopes with strong binding affinities to TLR-4. The docked complexes were stable, with minimal Root Mean Square Deviation and Root Mean Square Fluctuation values. Finally, the in silico-cloned plasmids have high % of GC content with > 0.8 codon adaptation index, suggesting they are suitable for expressing the protein vaccines in prokaryotic system.

Discussion

These next-generation vaccine designs are promising and warrant further investigation in wet lab experiments to assess their immunogenicity, safety, and efficacy for practical application in livestock. Our findings offer a robust framework for developing a comprehensive, broad-spectrum vaccine, potentially revolutionizing BT control and prevention strategies in the livestock industry.

MultiTEP-Based Vaccines Targeting SARS-CoV-2 Spike Protein IgG Epitopes Elicit Robust Binding Antibody Titers with Limited Virus-Neutralizing Activity

Within the last two decades, SARS-CoV-2 was the third zoonotic severe acute respiratory betacoronavirus (sarbecovirus) to infect humans, following SARS and MERS. The disruptions caused by the pandemic underscore the need for a universal vaccine against respiratory betacoronaviruses. Our group previously developed the universal platform for vaccine development, MultiTEP, which has been utilized in this study to generate a range of SARS-CoV-2 epitope vaccine candidates. We prepared and characterized 18 vaccines incorporating small peptide fragments from SARS-CoV-2 Spike protein fused with the MultiTEP sequence using overlapping PCR. Wild-type mice were immunized intramuscularly with the immunogen formulated in AdvaxCpG adjuvant. Serum antibodies were detected by ELISA, surrogate neutralization, and pseudovirus neutralization assays. Finally, the most promising vaccine candidate was administered to three non-human primates. All vaccines generated high titers of spike-binding IgG antibodies. However, only three vaccines generated antibodies that blocked RBD binding to the ACE2 receptor in a surrogate virus neutralization assay. However, none of the vaccines induced antibodies able to neutralize pseudotype viruses, including after the administration of the lead vaccine to NHPs. MultiTEP-based COVID-19 vaccines elicited robust, IgG-binding responses against the Spike protein in mice and non-human primates, but these antibodies were not neutralizing, underscoring the need to refine this approach further.

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.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Immunoinformatics-Based Designing of Novel and Potent Multi-Epitope PSA D15 and Cag11 Immunogens for Helicobacter pylori Immunodiagnostic Assay Development

Helicobacter pylori (H. pylori) strain is the most genetically diverse pathogenic bacterium and now alarming serious human health concern ranging from chronic gastritis to gastric cancer and human death all over the world. Currently, the majority of commercially available diagnostic assays for H. pylori is a challenging task due to the heterogeneity of virulence factors in various geographical regions. In this concern, designing of universal multi-epitope immunogenic biomarker targeted for all H. pylori strains would be crucial to successfully immunodiagnosis assay and vaccine development for H. pylori infection. Hence, the present study aimed to explore the potential immunogenic epitopes of PSA D15 and Cag11 proteins of H. pylori, using immunoinformatics web tools in order to design novel immune-reactive multi-epitope antigens for enhanced immunodiagnosis in humans. Through an in silico immunoinformatics approach, high-ranked B-cell, MHC-I, and MHC-II epitopes of PSA D15 and Cag11 proteins were predicted, screened, and selected. Subsequently, a novel multi-epitope PSA D15 and Cag11 antigens were designed by fused the high-ranked B-cell, MHC-I, and MHC-II epitopes and 50S ribosomal protein L7/L12 adjuvant using linkers. The antigenicity, solubility, physicochemical properties, secondary and tertiary structures, 3D model refinement, and validations were carried. Furthermore, the designed multi-epitope antigens were subjected to codon adaptation and in silico cloning, immune response simulation, and molecular docking with receptor molecules. A novel, stable multi-epitope PSA D15 and Cag11 H. pylori antigens were developed and immune simulation of the designed antigens showed desirable levels of immunological response. Molecular docking of designed antigens with immune receptors (B-cell, MHC-I, MHC-II, and TLR-2/4) revealed robust interactions and stable binding affinity to the receptors. The codon optimized and in silico cloned showed that the designed antigens were successfully expressed (CAI value of 0.95 for PSA D15 and 1.0 for Cag11) after inserted into pET-32ba (+) plasmid of the E. coli K12 strain. In conclusion, this study revealed that the designed multi-epitope antigens have a huge immunological potential candidate biomarker and useful in developing immunodiagnostic assays and vaccines for H. pylori infection.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

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

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

Immunoinformatics design of novel multi-epitope vaccine against Trueperella Pyogenes using collagen adhesion protein, fimbriae, and pyolysin

Trueperella pyogenes (T. pyogenes) is an opportunistic pathogen that causes infertility, mastitis, and metritis in animals. T. pyogenes is also a zoonotic disease and is considered an economic loss agent in the livestock industry. Therefore, vaccine development is necessary. Using an immunoinformatics approach, this study aimed to construct a multi-epitope vaccine against T. pyogenes. The collagen adhesion protein, fimbriae, and pyolysin (PLO) sequences were initially retrieved. The HTL, CTL, and B cell epitopes were predicted. The vaccine was designed by binding these epitopes with linkers. To increase vaccine immunogenicity, profilin was added to the N-terminal of the vaccine construct. The antigenic features and safety of the vaccine model were investigated. Docking, molecular dynamics simulation of the vaccine with immune receptors, and immunological simulation were used to evaluate the vaccine's efficacy. The vaccine's sequence was then optimized for cloning. The vaccine construct was designed based on 18 epitopes of T. pyogenes. The computational tools validated the vaccine as non-allergenic, non-toxic, hydrophilic, and stable at different temperatures with acceptable antigenic features. The vaccine model had good affinity and stability to bovine TLR2, 4, and 5 as well as stimulation of IgM, IgG, IL-2, IFN-γ, and Th1 responses. This vaccine also increased long-lived memory cells, dendritic cells, and macrophage population. In addition, codon optimization was done and cloned in the E. coli K12 expression vector (pET-28a). For the first time, this study introduced a novel multi-epitope vaccine candidate based on collagen adhesion protein, fimbriae, and PLO of T. pyogenes. It is expected this vaccine stimulates an effective immune response to prevent T. pyogenes infection.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

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.

Proteome based analysis of circulating SARS-CoV-2 variants: approach to a universal vaccine candidate

The discovery of the first infectious variant in Wuhan, China, in December 2019, has posed concerns over global health due to the spread of COVID-19 and subsequent variants. While the majority of patients experience flu-like symptoms such as cold and fever, a small percentage, particularly those with compromised immune systems, progress from mild illness to fatality. COVID-19 is caused by a RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach involved utilizing immunoinformatic to identify vaccine candidates with multiple epitopes and ligand-binding regions in reported SARS-CoV-2 variants. Through analysis of the spike glycoprotein, we identified dominant epitopes for T-cells and B-cells, resulting in a vaccine construct containing two helper T-cell epitopes, six cytotoxic T-cell epitopes, and four linear B-cell epitopes. Prior to conjugation with adjuvants and linkers, all epitopes were evaluated for antigenicity, toxicity, and allergenicity. Additionally, we assessed the vaccine Toll-Like Receptors complex (2, 3, and 4). The vaccine construct demonstrated antigenicity, non-toxicity, and non-allergenicity, thereby enabling the host to generate antibodies with favorable physicochemical characteristics. Furthermore, the 3D structure of the B-cell construct exhibited a ProSA-web z-score plot with a value of -1.71, indicating the reliability of the designed structure. The Ramachandran plot analysis revealed that 99.6% of the amino acid residues in the vaccine subunit were located in the high favored observation region, further establishing its strong candidacy as a vaccination option.

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

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

Translational and structural vaccinomics approach to design a multi-epitope vaccine against NOL4 autologous antigen of small cell lung cancer

Small cell lung cancer (SCLC) is one of the most common cancers and it is the sixth common cause for cancer-related deaths. The high plasticity and metastasis have been a major challenge for humanity to treat the disease. Hence, a vaccine for SCLC has become an urgent need of the hour due to public health concern. Implementation of immunoinformatics technique is one of the best way to find a suitable vaccine candidate. Immunoinformatics tools can be used to overcome the limitations and difficulties of traditional vaccinological techniques. Multi-epitope cancer vaccines have become a next-generation technique in vaccinology which can be used to stimulate more potent immune response against a particular antigen by eliminating undesirable molecules. In this study, we used multiple computational and immunoinformatics approach to design a novel multi-epitope vaccine for small cell lung cancer. Nucleolar protein 4 (NOL4) is an autologous cancer-testis antigen overexpressed in SCLC cells. Seventy-five percent humoral immunity have been identified for this particular antigen. In this study, we mapped immunogenic cytotoxic T lymphocyte, helper T lymphocyte, and interferon-gamma epitopes present in NOL4 antigen and designed a multi-epitope-based vaccine using the predicted epitopes. The designed vaccine was antigenic, non-allergenic, and non-toxic with 100% applicability on human population. The chimeric vaccine construct showed stable and significant interaction with endosomal and plasmalemmal toll-like receptors in molecular docking and protein-peptide interaction analysis, thus assuring a strong potent immune response against the vaccine upon administration. Therefore, these preliminary results can be used to carry out further experimental investigations.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

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.

Interaction of SARS-CoV-2 with host cells and antibodies: experiment and simulation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the devastating global COVID-19 pandemic announced by WHO in March 2020. Through unprecedented scientific effort, several vaccines, drugs and antibodies have been developed, saving millions of lives, but the fight against COVID-19 continues as immune escape variants of concern such as Delta and Omicron emerge. To develop more effective treatments and to elucidate the side effects caused by vaccines and therapeutic agents, a deeper understanding of the molecular interactions of SARS-CoV-2 with them and human cells is required. With special interest in computational approaches, we will focus on the structure of SARS-CoV-2 and the interaction of its spike protein with human angiotensin-converting enzyme-2 (ACE2) as a prime entry point of the virus into host cells. In addition, other possible viral receptors will be considered. The fusion of viral and human membranes and the interaction of the spike protein with antibodies and nanobodies will be discussed, as well as the effect of SARS-CoV-2 on protein synthesis in host cells.

Reverse vaccinology and immunoinformatics approaches to design multi-epitope based vaccine against oncogenic KRAS

Mutant KRAS-induced tumorigenesis is highly involved in the progression of pancreatic, lung, and breast cancer. Comparatively, KRAS G12D and KRAS G12C are the most frequent mutations that promote cancer progression and aggressiveness. Although KRAS mutant inhibitors exhibit significant therapeutic potential, day by day, they are becoming resistant among patients. Multi-epitope based cancer vaccines are a promising alternative strategy that induces an immune response against tumor antigens. In the present study, we have designed, constructed, and validated a novel multi-epitope vaccine construct against KRAS G12D and G12C mutants using reverse vaccinology and immunoinformatics approaches. In addition, the vaccine construct was structurally refined and showed significant physiochemical properties, and could induce an immune response. Furthermore, the optimized vaccine construct was cloned into a pET‑28a (+) expression vector through in silico cloning. Conclusively, the multi-epitope vaccine construct is structurally stable, soluble, antigenic, non‑allergic, and non‑toxic. Further, it has to be studied in in vitro and in vivo to evaluate its therapeutic efficacy against KRAS-mutated cancers in the near future.

Immunoinformatics for Novel Multi-Epitope Vaccine Development in Canine Parvovirus Infections

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate.

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

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

Screening of apical membrane antigen-1 (AMA1), dense granule protein-7 (GRA7) and rhoptry protein-16 (ROP16) antigens for a potential vaccine candidate against Toxoplasma gondii for chickens

Toxoplasmosis is a zoonotic disease caused by the protozoan parasite, Toxoplasma gondii known to infect almost all animals, including birds and humans globally. This disease has impacted the livestock industry and public health, where infection of domestic animals increases the zoonotic risk of transmission of infection to humans, threatening public health. Hence the need to discover novel and safe vaccines to fight against toxoplasmosis. In the current study, a novel multiepitope vaccine was designed using immunoinformatics techniques targeting T. gondii AMA1, GRA7 and ROP16 antigens, consisting of antigenic, immunogenic, non-allergenic and cytokine inducing T-cell (9 CD8⁺ and 15 CD4⁺) epitopes and four (4) B-cell epitopes fused together using AAY, KK and GPGPG linkers. The tertiary model of the proposed vaccine was predicted and validated to confirm the structural quality of the vaccine. The designed vaccine was highly antigenic (antigenicity = 0.6645), immunogenic (score = 2.89998), with molecular weight of 73.35 kDa, instability and aliphatic index of 28.70 and 64.10, respectively; and GRAVY of -0.363. The binding interaction, stability and flexibility were assessed with molecular docking and dynamics simulation, which revealed the proposed vaccine to have good structural interaction (binding affinity = -106.882 kcal/mol) and stability when docked with Toll like receptor-4 (TLR4). The results revealed that the Profilin-adjuvanted vaccine is promising, as it predicted induction of enhanced immune responses through the production of cytokines and antibodies critical in blocking host invasion.

In silico design and immunoinformatics analysis of a universal multi-epitope vaccine against monkeypox virus

Monkeypox virus (MPXV) outbreaks have been reported in various countries worldwide; however, there is no specific vaccine against MPXV. In this study, therefore, we employed computational approaches to design a multi-epitope vaccine against MPXV. Initially, cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), linear B lymphocytes (LBL) epitopes were predicted from the cell surface-binding protein and envelope protein A28 homolog, both of which play essential roles in MPXV pathogenesis. All of the predicted epitopes were evaluated using key parameters. A total of 7 CTL, 4 HTL, and 5 LBL epitopes were chosen and combined with appropriate linkers and adjuvant to construct a multi-epitope vaccine. The CTL and HTL epitopes of the vaccine construct cover 95.57% of the worldwide population. The designed vaccine construct was found to be highly antigenic, non-allergenic, soluble, and to have acceptable physicochemical properties. The 3D structure of the vaccine and its potential interaction with Toll-Like receptor-4 (TLR4) were predicted. Molecular dynamics (MD) simulation confirmed the vaccine's high stability in complex with TLR4. Finally, codon adaptation and in silico cloning confirmed the high expression rate of the vaccine constructs in strain K12 of Escherichia coli (E. coli). These findings are very encouraging; however, in vitro and animal studies are needed to ensure the potency and safety of this vaccine candidate.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections

BACKGROUND

COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity.

METHODS AND RESULTS

In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor.

CONCLUSION

The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

An In Silico Deep Learning Approach to Multi-Epitope Vaccine Design: A Hepatitis E Virus Case Study

Hepatitis E Virus (HEV) is a major cause of acute and chronic hepatitis. The severity of HEV infection increases manyfold in pregnant women and immunocompromised patients. Despite the extensive research on HEV in the last few decades, there is no widely available vaccine yet. In the current study, immunoinformatic analyses were applied to predict a multi-epitope vaccine candidate against HEV. From the ORF2 region, 41 conserved and immunogenic epitopes were prioritized. These epitopes were further analyzed for their probable antigenic and non-allergenic combinations with several linkers. The stability of the vaccine construct was confirmed by molecular dynamic simulations. The vaccine construct is potentially antigenic and docking analysis revealed stable interactions with TLR3. These results suggest that the proposed vaccine can efficiently stimulate both cellular and humoral immune responses. However, further studies are needed to determine the immunogenicity of the vaccine construct.

In silico design of a polypeptide as a vaccine candidate against ascariasis

Ascariasis is the most prevalent zoonotic helminthic disease worldwide, and is responsible for nutritional deficiencies, particularly hindering the physical and neurological development of children. The appearance of anthelmintic resistance in Ascaris is a risk for the target of eliminating ascariasis as a public health problem by 2030 set by the World Health Organisation. The development of a vaccine could be key to achieving this target. Here we have applied an in silico approach to design a multi-epitope polypeptide that contains T-cell and B-cell epitopes of reported novel potential vaccination targets, alongside epitopes from established vaccination candidates. An artificial toll-like receptor-4 (TLR4) adjuvant (RS09) was added to improve immunogenicity. The constructed peptide was found to be non-allergic, non-toxic, with adequate antigenic and physicochemical characteristics, such as solubility and potential expression in Escherichia coli. A tertiary structure of the polypeptide was used to predict the presence of discontinuous B-cell epitopes and to confirm the molecular binding stability with TLR2 and TLR4 molecules. Immune simulations predicted an increase in B-cell and T-cell immune response after injection. This polypeptide can now be validated experimentally and compared to other vaccine candidates to assess its possible impact in human health.

Peptide microarray analysis of in-silico predicted B-cell epitopes in SARS-CoV-2 sero-positive healthcare workers in Bulawayo, Zimbabwe

Immunogenic peptides that mimic linear B-cell epitopes coupled with immunoassay validation may improve serological tests for emerging diseases. This study reports a general approach for profiling linear B-cell epitopes derived from SARS-CoV-2 using an in-silico method and peptide microarray immunoassay, using healthcare workers' SARS-CoV-2 sero-positive sera. SARS-CoV-2 was tested using rapid chromatographic immunoassays and real-time reverse-transcriptase polymerase chain reaction. Immunogenic peptides mimicking linear B-cell epitopes were predicted in-silico using ABCpred. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were selected using the NCBI Protein BLAST. IgG and IgM antibodies against the SARS-CoV-2 spike protein, membrane glycoprotein and nucleocapsid derived peptides were measured in sera using peptide microarray immunoassay. Fifty-three healthcare workers included in the study were RT-PCR negative for SARS-CoV-2. Using rapid chromatographic immunoassays, 10 were SARS-CoV-2 IgM sero-positive and 7 were SARS-CoV-2 IgG sero-positive. From a total of 10 SARS-CoV-2 peptides contained on the microarray, 3 (QTH34388.1-1-14, QTN64908.1-135-148, and QLL35955.1-22-35) showed reactivity against IgG. Three peptides (QSM17284.1-76-89, QTN64908.1-135-148 and QPK73947.1-8-21) also showed reactivity against IgM. Based on the results we predicted one peptide (QSM17284.1-76-89) that had an acceptable diagnostic performance. Peptide QSM17284.1-76-89 was able to detect IgM antibodies against SARS-CoV-2 with area under the curve (AUC) 0.781 when compared to commercial antibody tests. In conclusion in silico peptide prediction and peptide microarray technology may provide a platform for the development of serological tests for emerging infectious diseases such as COVID-19. However, we recommend using at least three in-silico peptide prediction tools to improve the sensitivity and specificity of B-cell epitope prediction, to predict peptides with excellent diagnostic performances.

Identification of potential candidate vaccines against Mycobacterium ulcerans based on the major facilitator superfamily transporter protein

Buruli ulcer is a neglected tropical disease that is characterized by non-fatal lesion development. The causative agent is Mycobacterium ulcerans (M. ulcerans). There are no known vectors or transmission methods, preventing the development of control methods. There are effective diagnostic techniques and treatment routines; however, several socioeconomic factors may limit patients' abilities to receive these treatments. The Bacillus Calmette-Guérin vaccine developed against tuberculosis has shown limited efficacy, and no conventionally designed vaccines have passed clinical trials. This study aimed to generate a multi-epitope vaccine against M. ulcerans from the major facilitator superfamily transporter protein using an immunoinformatics approach. Twelve M. ulcerans genome assemblies were analyzed, resulting in the identification of 11 CD8+ and 7 CD4+ T-cell epitopes and 2 B-cell epitopes. These conserved epitopes were computationally predicted to be antigenic, immunogenic, non-allergenic, and non-toxic. The CD4+ T-cell epitopes were capable of inducing interferon-gamma and interleukin-4. They successfully bound to their respective human leukocyte antigens alleles in in silico docking studies. The expected global population coverage of the T-cell epitopes and their restricted human leukocyte antigens alleles was 99.90%. The population coverage of endemic regions ranged from 99.99% (Papua New Guinea) to 21.81% (Liberia). Two vaccine constructs were generated using the Toll-like receptors 2 and 4 agonists, LprG and RpfE, respectively. Both constructs were antigenic, non-allergenic, non-toxic, thermostable, basic, and hydrophilic. The DNA sequences of the vaccine constructs underwent optimization and were successfully in-silico cloned with the pET-28a(+) plasmid. The vaccine constructs were successfully docked to their respective toll-like receptors. Molecular dynamics simulations were carried out to analyze the binding interactions within the complex. The generated binding energies indicate the stability of both complexes. The constructs generated in this study display severable favorable properties, with construct one displaying a greater range of favorable properties. However, further analysis and laboratory validation are required.

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

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

A novel vaccine based on SARS-CoV-2 CD4+ and CD8+ T cell conserved epitopes from variants Alpha to Omicron

COVID-19 caused, as of September, 1rst, 2022, 599,825,400 confirmed cases, including 6,469,458 deaths. Currently used vaccines reduced severity and mortality but not virus transmission or reinfection by different strains. They are based on the Spike protein of the Wuhan reference virus, which although highly antigenic suffered many mutations in SARS-CoV-2 variants, escaping vaccine-generated immune responses. Multiepitope vaccines based on 100% conserved epitopes of multiple proteins of all SARS-CoV-2 variants, rather than a single highly mutating antigen, could offer more long-lasting protection. In this study, a multiepitope multivariant vaccine was designed using immunoinformatics and in silico approaches. It is composed of highly promiscuous and strong HLA binding CD4+ and CD8+ T cell epitopes of the S, M, N, E, ORF1ab, ORF 6 and ORF8 proteins. Based on the analysis of one genome per WHO clade, the epitopes were 100% conserved among the Wuhan-Hu1, Alpha, Beta, Gamma, Delta, Omicron, Mµ, Zeta, Lambda and R1 variants. An extended epitope-conservancy analysis performed using GISAID metadata of 3,630,666 SARS-CoV-2 genomes of these variants and the additional genomes of the Epsilon, Lota, Theta, Eta, Kappa and GH490 R clades, confirmed the high conservancy of the epitopes. All but one of the CD4 peptides showed a level of conservation greater than 97% among all genomes. All but one of the CD8 epitopes showed a level of conservation greater than 96% among all genomes, with the vast majority greater than 99%. A multiepitope and multivariant recombinant vaccine was designed and it was stable, mildly hydrophobic and non-toxic. The vaccine has good molecular docking with TLR4 and promoted, without adjuvant, strong B and Th1 memory immune responses and secretion of high levels of IL-2, IFN-γ, lower levels of IL-12, TGF-β and IL-10, and no IL-6. Experimental in vivo studies should validate the vaccine's further use as preventive tool with cross-protective properties.

Insight into the first multi-epitope-based peptide subunit vaccine against avian influenza A virus (H5N6): An immunoinformatics approach

The rampant spread of highly pathogenic avian influenza A (H5N6) virus has drawn additional concerns along with ongoing Covid-19 pandemic. Due to its migration-related diffusion, the situation is deteriorating. Without an existing effective therapy and vaccines, it will be baffling to take control measures. In this regard, we propose a revers vaccinology approach for prediction and design of a multi-epitope peptide based vaccine. The induction of humoral and cell-mediated immunity seems to be the paramount concern for a peptide vaccine candidate; thus, antigenic B and T cell epitopes were screened from the surface, membrane and envelope proteins of the avian influenza A (H5N6) virus, and passed through several immunological filters to determine the best possible one. Following that, the selected antigenic with immunogenic epitopes and adjuvant were linked to finalize the multi-epitope-based peptide vaccine by appropriate linkers. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLR8). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, molecular dynamics simulation was performed within the highest binding affinity complex to observe the stability, and minimize the designed vaccine's high mobility region to order to increase its stability. Then, Codon optimization and other physicochemical properties were performed to reveal that the vaccine would be suitable for a higher expression at cloning level and satisfactory thermostability condition. In conclusion, predicting the overall in silico assessment, we anticipated that our designed vaccine would be a plausible prevention against avian influenza A (H5N6) virus.

Current clinical status of new COVID-19 vaccines and immunotherapy

COVID-19, caused by SARS-CoV-2, is a positive-strand RNA belonging to Coronaviridae family, along with MERS and SARS. Since its first report in 2019 in Wuhan, China, it has affected over 530 million people and led to 6.3 million deaths worldwide until June 2022. Despite eleven vaccines being used worldwide already, new variants are of concern. Therefore, the governing bodies are re-evaluating the strategies for achieving universal vaccination. Initially, the WHO expected that vaccines showing around 50-80% efficacy would develop in 1-2 years. However, US-FDA announced emergency approval of the two m-RNA vaccines within 11 months of vaccine development, which enabled early vaccination for healthcare workers in many countries. Later, in January 2021, 63 vaccine candidates were under human clinical trials and 172 under preclinical development. Currently, the number of such clinical studies is still increasing. In this review, we have summarized the updates on the clinical status of the COVID-19 and the available treatments. Additionally, COVID-19 had created negative impacts on world's economy; affected agriculture, industries, and tourism service sectors; and majorly affected low-income countries. The review discusses the clinical outcomes, latest statistics, socio-economic impacts of pandemic and treatment approaches against SARS-CoV-2, and strategies against the new variant of concern. The review will help understand the current status of vaccines and other therapies while also providing insights about upcoming vaccines and therapies for COVID-19 management.

Design, construction and in vivo functional assessment of a hinge truncated sFLT01

Gene therapy for the treatment of ocular neovascularization has reached clinical trial phases. The AAV2-sFLT01 construct was already evaluated in a phase 1 open-label trial administered intravitreally to patients with advanced neovascular age-related macular degeneration. SFLT01 protein functions by binding to VEGF and PlGF molecules and inhibiting their activities simultaneously. It consists of human VEGFR1/Flt-1 (hVEGFR1), a polyglycine linker, and the Fc region of human IgG1. The IgG1 upper hinge region of the sFLT01 molecule makes it vulnerable to radical attacks and prone to causing immune reactions. This study pursued two goals: (i) minimizing the immunogenicity and vulnerability of the molecule by designing a truncated molecule called htsFLT01 (hinge truncated sFLT01) that lacked the IgG1 upper hinge and lacked 2 amino acids from the core hinge region; and (ii) investigating the structural and functional properties of the aforesaid chimeric molecule at different levels (in silico, in vitro, and in vivo). Molecular dynamics simulations and molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation calculations revealed comparable free energy of binding and binding affinity for sFLT01 and htsFLT01 to their cognate ligands. Conditioned media from human retinal pigment epithelial (hRPE) cells that expressed htsFLT01 significantly reduced tube formation in HUVECs. The AAV2-htsFLT01 virus suppressed vascular development in the eyes of newborn mice. The htsFLT01 gene construct is a novel anti-angiogenic tool with promising improvements compared to existing treatments.

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.

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.

COVID-19 management landscape: A need for an affordable platform to manufacture safe and efficacious biotherapeutics and prophylactics for the developing countries

To gain world-wide control over COVID-19 pandemic, it is necessary to have affordable and accessible vaccine and monoclonal antibody technologies across the globe. In comparison to the western countries, Asian and African countries have less percentage of vaccination done which warrants urgent attention. Global manufacturer production capacities, dependency on advanced nations for the supply of vaccines or the raw material, national economy, limited research facilities, and logistics could be the factors. This review article elaborates the existing therapeutic and prophylactic strategies available for COVID-19, currently adopted vaccine and monoclonal antibody platforms for SARS-CoV-2 along with the approaches to bridge the gap prevailing in the challenges faced by low- and middle-income countries. We believe adoption of yeast-derived P. pastoris technology can help in developing safe, proven, easy to scale-up, and affordable recombinant vaccine or monoclonal antibodies against SARS-CoV-2. This platform has the advantage of not requiring a dedicated or specialized facility making it an affordable option using existing manufacturing facilities, without significant additional capital investments. Besides, the technology platform of multiantigen vaccine approach and monoclonal antibody cocktail will serve as effective weapons to combat the threat posed by the SARS-CoV-2 variants. Successful development of vaccines and monoclonal antibodies using such a technology will lead to self-sufficiency of these nations in terms of availability of vaccines and monoclonal antibodies.

In silico design of a multi-epitope vaccine against HPV16/18

BACKGROUND

Cervical cancer is the fourth most common cancer affecting women and is caused by human Papillomavirus (HPV) infections that are sexually transmitted. There are currently commercially available prophylactic vaccines that have been shown to protect vaccinated individuals against HPV infections, however, these vaccines have no therapeutic effects for those who are previously infected with the virus. The current study's aim was to use immunoinformatics to develop a multi-epitope vaccine with therapeutic potential against cervical cancer.

RESULTS

In this study, T-cell epitopes from E5 and E7 proteins of HPV16/18 were predicted. These epitopes were evaluated and chosen based on their antigenicity, allergenicity, toxicity, and induction of IFN-γ production (only in helper T lymphocytes). Then, the selected epitopes were sequentially linked by appropriate linkers. In addition, a C-terminal fragment of Mycobacterium tuberculosis heat shock protein 70 (HSP70) was used as an adjuvant for the vaccine construct. The physicochemical parameters of the vaccine construct were acceptable. Furthermore, the vaccine was soluble, highly antigenic, and non-allergenic. The vaccine's 3D model was predicted, and the structural improvement after refinement was confirmed using the Ramachandran plot and ProSA-web. The vaccine's B-cell epitopes were predicted. Molecular docking analysis showed that the vaccine's refined 3D model had a strong interaction with the Toll-like receptor 4. The structural stability of the vaccine construct was confirmed by molecular dynamics simulation. Codon adaptation was performed in order to achieve efficient vaccine expression in Escherichia coli strain K12 (E. coli). Subsequently, in silico cloning of the multi-epitope vaccine was conducted into pET-28a ( +) expression vector.

CONCLUSIONS

According to the results of bioinformatics analyses, the multi-epitope vaccine is structurally stable, as well as a non-allergic and non-toxic antigen. However, in vitro and in vivo studies are needed to validate the vaccine's efficacy and safety. If satisfactory results are obtained from in vitro and in vivo studies, the vaccine designed in this study may be effective as a therapeutic vaccine against cervical cancer.

The Role of Exposomes in the Pathophysiology of Autoimmune Diseases II: Pathogens

In our continuing examination of the role of exposomes in autoimmune disease, we use this review to focus on pathogens. Infections are major contributors to the pathophysiology of autoimmune diseases through various mechanisms, foremost being molecular mimicry, when the structural similarity between the pathogen and a human tissue antigen leads to autoimmune reactivity and even autoimmune disease. The three best examples of this are oral pathogens, SARS-CoV-2, and the herpesviruses. Oral pathogens reach the gut, disturb the microbiota, increase gut permeability, cause local inflammation, and generate autoantigens, leading to systemic inflammation, multiple autoimmune reactivities, and systemic autoimmunity. The COVID-19 pandemic put the spotlight on SARS-CoV-2, which has been called “the autoimmune virus.” We explore in detail the evidence supporting this. We also describe how viruses, in particular herpesviruses, have a role in the induction of many different autoimmune diseases, detailing the various mechanisms involved. Lastly, we discuss the microbiome and the beneficial microbiota that populate it. We look at the role of the gut microbiome in autoimmune disorders, because of its role in regulating the immune system. Dysbiosis of the microbiota in the gut microbiome can lead to multiple autoimmune disorders. We conclude that understanding the precise roles and relationships shared by all these factors that comprise the exposome and identifying early events and root causes of these disorders can help us to develop more targeted therapeutic protocols for the management of this worldwide epidemic of autoimmunity.

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.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

Peptide candidates for the development of therapeutics and vaccines against β-coronavirus infection

Betacoronaviruses (β-CoVs) have caused major viral outbreaks in the last two decades in the world. The mutation and recombination abilities in β-CoVs resulted in zoonotic diseases in humans. Proteins responsible for viral attachment and replication are highly conserved in β-CoVs. These conserved proteins have been extensively studied as targets for preventing infection and the spread of β-CoVs. Peptides are among the most promising candidates for developing vaccines and therapeutics against viral pathogens. The immunostimulatory and viral inhibitory potential of natural and synthetic peptides has been extensively studied since the SARS-CoV outbreak. Food-derived peptides demonstrating high antiviral activity can be used to develop effective therapeutics against β-CoVs. Specificity, tolerability, and customizability of peptides can be explored to develop potent drugs against β-CoVs. However, the proteolytic susceptibility and low bioavailability of peptides pose challenges for the development of therapeutics. This review illustrates the potential role of peptides in eliciting an adaptive immune response and inhibiting different stages of the β-CoV life cycle. Further, the challenges and future directions associated with developing peptide-based therapeutics and vaccines against existing and future β-CoV pathogens have been discussed.

In silico analysis of mutant epitopes in new SARS-CoV-2 lineages suggest global enhanced CD8+ T cell reactivity and also signs of immune response escape

SARS-CoV-2 variants of concern have emerged since the COVID-19 outburst, notably the lineages detected in the UK, South Africa, and Brazil. Their increased transmissibility and higher viral load put them in the spotlight. Much has been investigated on the ability of those new variants to evade antibody recognition. However, little attention has been given to pre-existing and induced SARS-CoV-2-specific CD8+ T cell responses by new lineages. In this work, we predicted SARS-CoV-2-specific CD8+ T cell epitopes from the main variants of concern and their potential to trigger or hinder CD8+ T cell response by using HLA binding and TCR reactivity in silico predictions. Also, we estimated the population's coverage for different lineages, which accounts for the ability to present a set of peptides based on the most frequent HLA alleles of a given population. We considered binding predictions to 110 ccClass I HLA alleles from 29 countries to investigate differences in the fraction of individuals expected to respond to a given epitope set from new and previous lineages. We observed a higher population coverage for the variant detected in the UK (B.1.1.7), and South Africa (B.1.351), as well as for the Brazilian P.1 lineage, but not P.2, compared to the reference lineage. Moreover, individual mutations such as Spike N501Y and Nucleocapsid D138Y were predicted to have an overall stronger affinity through HLA-I than the reference sequence while Spike E484K shows signs of evasion. In summary, we provided evidence for the existence of potentially immunogenic and conserved epitopes across new SARS-CoV-2 variants, but also mutant peptides exhibiting diminished or abolished HLA-I binding. It also highlights the augmented population coverage for three new lineages. Whether these changes imply more T cell reactivity or potential to evade from CD8+ T cell responses requires experimental verification.

Recombinant subunits of SARS‐CoV‐2 spike protein as vaccine candidates to elicit neutralizing antibodies

Objectives

The spike protein has been reported as one of the most critical targets for vaccine design strategies against the SARS‐CoV‐2 infection. Hence, we have designed, produced, and evaluated the potential use of three truncated recombinant proteins derived from spike protein as vaccine candidates capable of neutralizing SARS‐CoV‐2 virus.

Methods

In silico tools were used to design spike‐based subunit recombinant proteins (RBD (P₁), fusion peptide (P₂), and S1/S2 cleavage site (P₃)). These proteins were checked for their ability to be identified by the anti‐SARS‐CoV‐2 antibodies by exposing them to COVID‐19 serum samples. The proteins were also injected into mice and rabbit, and the antibody titers were measured for 390 days to assess their neutralization efficiency.

Results

The antibodies that existed in the serum of COVID‐19 patients were identified by designed proteins. The anti‐spike antibody titer was increased in the animals injected with recombinant proteins. The VNT results revealed that the produced antibodies could neutralize the cultured live virus.

Conclusion

Truncated subunit vaccines could also be considered as robust tools for effective vaccination against COVID‐19. Using a combination of in silico, in vitro, and in vivo experiments, it was shown that the injection of spike‐based truncated recombinant proteins could stimulate long‐lasting and neutralizing antibody responses.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

Immune escape facilitation by mutations of epitope residues in RdRp of SARS-CoV-2

Mutations drive viral evolution and genome variability that causes viruses to escape host immunity and to develop drug resistance. SARS-CoV-2 has considerably higher mutation rate. SARS-CoV-2 possesses a RNA dependent RNA polymerase (RdRp) which helps to replicate its genome. The mutation P323L in RdRp is associated with the loss of a particular epitope (321-327) from this protein. We consider the effects of mutations in some of the epitope region including the naturally occurring mutation P323L on the structure of the epitope and their interface with paratope using all-atom molecular dynamics (MD) simulation studies. We observe that the mutations cause conformational changes in the epitope region by opening up the region associated with increase in the radius of gyration and intramolecular hydrogen bonds, making the region less accessible. Moreover, we study the conformational stability of the epitope region and epitope:paratope interface under the mutation from the fluctuations in the dihedral angles. We observe that the mutation renders the epitope and the epitope:paratope interface unstable compared to the corresponding wild type ones. Thus, the mutations may help in escaping antibody mediated immunity of the hostCommunicated by Ramaswamy H. Sarma.

Prediction of putative potential siRNAs for inhibiting SARS-CoV-2 strains, including variants of concern and interest

Aim: To predict siRNAs as a therapeutic intervention for highly infectious new variants of SARS-CoV-2. Methods: Conserved coding sequence regions of 11 SARS-CoV-2 proteins were used to construct siRNAs through sampling of metadata comprising 214,256 sequences. Results: Predicted siRNAs S1: 5′-UCAUUGAGAAAUGUUUACGCA-3′ and S2: 5′-AAAGACAUCAGCAUACUCCUG-3′ against RdRp of SARS-CoV-2 satisfied all the stringent filtering processes and showed good binding characteristics. The designed siRNAs are expected to inhibit viral replication and transcription of various coronavirus strains encompassing variants of concern and interest. Conclusion: The predicted siRNAs are expected to be potent against SARS-CoV-2, and following in vitro and in vivo validations may be considered as potential therapeutic measures.