Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins

Reverse engineering protection: A comprehensive survey of reverse vaccinology-based vaccines targeting viral pathogens

Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.

An immunoinformatics and structural vaccinology approach to design a novel and potent multi-epitope base vaccine targeting Zika virus

Zika virus is an infectious virus, that belongs to Flaviviridae family, which is transferred to humans through mosquito vectors and severely threatens human health; but, apart from available resources, no effective and secure vaccine is present against Zika virus, to prevent such infections. In current study, we employed structural vaccinology approach to design an epitope-based vaccine against Zika virus, which is biocompatible, and secure and might trigger an adaptive and innate immune response by using computational approaches. We first retrieved the protein sequence from National Center for Biotechnology Information (NCBI) database and carried out for BLAST P. After BLAST P, predicted protein sequences were shortlisted and checked for allergic features and antigenic properties. Final sequence of Zika virus, with accession number (APO40588.1) was selected based on high antigenic score and non-allergenicity. Final protein sequence used various computational approaches including antigenicity testing, toxicity evaluation, allergenicity, and conservancy assessment to identify superior B-cell and T-cell epitopes. Two B-cell epitopes, five MHC-six MHC-II epitopes and I were used to construct an immunogenic multi-epitope-based vaccine by using suitable linkers. A 50S ribosomal protein was added at N terminal to improve the immunogenicity of vaccine. In molecular docking, strong interactions were presented between constructed vaccine and Toll-like receptor 9 (- 1100.6 kcal/mol), suggesting their possible relevance in the immunological response to vaccine. The molecular dynamics simulations ensure the dynamic and structural stability of constructed vaccine. The results of C-immune simulation revealed that constructed vaccine activate B and T lymphocytes which induce high level of antibodies and cytokines to combat Zika infection. The constructed vaccine is an effective biomarker with non-sensitization, nontoxicity; nonallergic, good immunogenicity, and antigenicity, however, experimental assays are required to verify the results of present study.

Synthesis, biological evaluation, and molecular docking study of xanthene-linked thiosemicarbazones as cholinesterase inhibitors

This study delineates the design and synthesis of a series of xanthene-based thiosemicarbazones that show low μM inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), crucial enzymes associated with, among others, Alzheimer's Disease (AD) pathology. Despite FDA-approved AChE inhibitors being frontline treatments for AD, there remains a need for agents exhibiting improved efficacy and selectivity. Our synthesized series demonstrate meaningful inhibition against AChE (IC50 ranging from 4.2 to 62 μM). These compounds exhibit comparatively lower potency against BChE (IC50 values between 64 and 315 μM), showcasing a pronounced AChE selectivity compared to physostigmine. The selectivity index for the compounds between the two targets does vary between 0.02 and 0.75 highlighting that even minor structural differences can have drastic effects on protein interactions. Molecular docking insights further substantiated these observations, revealing the importance of the xanthene scaffold for AChE-binding and the aryl R2 moiety for BChE interactions. Notably, some compounds demonstrated dual enzyme targeting, emphasizing their interactions could be exploited for developing monotherapies against cholinesterase-associated neurodegenerative afflictions like AD. Collectively, these findings suggest that xanthene-based thiosemicarbazones are a promising and highly accessible scaffold that deserve further investigative exploration in the cholinesterase inhibitor therapeutic landscape.Communicated by Ramaswamy H. Sarma.

Epitopes mapping for identification of potential cross-reactive peptide against leptospirosis

Leptospira, the pathogenic helical spirochetes that cause leptospirosis, is an emerging zoonotic disease with effective dissemination tactics in the host and can infect humans and animals with moderate or severe illnesses. Thus, peptide-based vaccines may be the most effective strategy to manage the immune response against Leptospira to close these gaps. In the current investigation, highly immunogenic proteins from the proteome of Leptospira interorgan serogroup Icterohaemorrhagie serovar Lai strain 56601 were identified using immunoinformatic methods. It was discovered that the conserved and most immunogenic outer membrane Lepin protein was both antigenic and non-allergenic by testing 15 linear B-cells and the ten best T-cell (Helper-lymphocyte (HTL) with the most significant number of HLA-DR binding alleles and the eight cytotoxic T lymphocyte (CTL)) epitopes. Furthermore, a 3D structural model of CTL epitopes was created using the Pep-Fold3 platform. Using the Autodock 4.2 docking server, research was conducted to determine how well the top-ranked CTL peptide models attach to HLA-A*0201 (PDB ID: 4U6Y). With HLA-A*0201, the epitope SSGTGNLHV binds with a binding energy of -1.29 kcal/mol. Utilizing molecular dynamics modeling, the projected epitope-allele docked complex structure was optimized, and the stability of the complex system was assessed. Therefore, this epitope can trigger an immunological response and produce effective Leptospira vaccine candidates. Overall, this study offers a unique vaccination candidate and may encourage additional research into leptospirosis vaccines.Communicated by Ramaswamy H. Sarma.

In Vivo Validation of Novel Synthetic tbp1 Peptide-Based Vaccine Candidates against Haemophilus influenzae Strains in BALB/c Mice

Haemophilus influenzae is a Gram-negative bacterium characterized as a small, nonmotile, facultative anaerobic coccobacillus. It is a common cause of a variety of invasive and non-invasive infections. Among six serotypes (a-f), H. influenzae type b (Hib) is the most familiar and predominant mostly in children and immunocompromised individuals. Following Hib vaccination, infections due to other serotypes have increased in number, and currently, there is no suitable effective vaccine to induce cross-strain protective antibody responses. The current study was aimed to validate the capability of two 20-mer highly conserved synthetic tbp1 (transferrin-binding protein 1) peptide-based vaccine candidates (tbp1-E1 and tbp1-E2) predicted using in silico approaches to induce immune responses against H. influenzae strains. Cytokine induction ability, immune simulations, and molecular dynamics (MD) simulations were performed to confirm the candidacy of epitopic docked complexes. Synthetic peptide vaccine formulations in combination with two different adjuvants, BGs (Bacterial Ghosts) and CFA/IFA (complete/incomplete Freund's adjuvant), were used in BALB/c mouse groups in three booster shots at two-week intervals. An indirect ELISA was performed to determine endpoint antibody titers using the Student's t-distribution method. The results revealed that the synergistic use of both peptides in combination with BG adjuvants produced better results. Significant differences in absorbance values were observed in comparison to the rest of the peptide-adjuvant combinations. The findings of this study indicate that these tbp1 peptide-based vaccine candidates may present a preliminary set of peptides for the development of an effective cross-strain vaccine against H. influenzae in the future due to their highly conserved nature.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Computational prediction of B and T-cell epitopes of Kyasanur Forest Disease virus marker proteins towards the development of precise diagnosis and potent subunit vaccine

Kyasanur Forest Disease (KFD), also known as 'monkey fever', caused by KFD Virus (KFDV), is a highly neglected tropical disease endemic to Western Ghat region of Karnataka, India. Recently, KFD, which is fatal for both monkeys and humans with a mortality rate of 2-10% has been found to spread from its epicenter to neighboring districts and states also. The current ELISA based KFD detection method is very non-specific due to cross-reactivity with other flaviviruses. Further, presently available formalin-inactivated vaccine has been found to be less effective leading to disease susceptibility and severity. To address these, the present study was aimed at predicting the potent specific B and T-cell epitopes of KFDV immunogenic marker proteins using diverse computational tools aiming at developing precise diagnostic method and an effective subunit vaccine. Here, we have chosen E, NS1 and NS5 proteins as markers of KFDV by taking into account of their differential and non-overlapping sequences with selected arboviruses. Based on the linear and nonlinear epitope prediction tools and important biophysical parameters, we identified three potential linear and ten nonlinear B-cell epitopes. We also predicted T-cell epitope peptides which binds to MHC class-I and class-II receptors for the effective T-cell activation. Thus, our molecular docking and molecular dynamics simulation analysis has identified six different TH-cell epitopes based on the distribution frequency of MHC-II haplotypes in the human population and one TC-cell epitope from NS5 protein that has maximum interaction with class-I MHC. Overall, we have successfully identified potential B and T-cell epitope marker peptides present in the envelope and two non-structural proteins.Communicated by Ramaswamy H. Sarma.

B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration

Antibody-antigen interaction-at antigenic local environments called B-cell epitopes-is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-nine enveloped virus-antibody structures), 2) two structurally characterized non-enveloped virus epitopes which evolved slowly (out of eight non-enveloped virus-antibody structures), and 3) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of five Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.

Designing and Expression of Recombinant Chimeric Spike Protein from SARS-CoV-2 in Escherichia coli and Its Immunogenicity Assessment

Since December 2019, the world has been grappling with an ongoing global COVID-19 pandemic. Various virus variants have emerged over the past two years, each posing a greater threat than its predecessors. The recent appearance of the omicron variant (B.1.1.529) has raised significant alarm within the field of epidemiology due to its highly contagious nature and rapid transmission rate. The omicron variant possessed mutations in the key receptor-binding domain (RBD) region, the S region, and these modifications have shown a notable impact on the strain's susceptibility to neutralizing antibodies. Developing safe and efficient vaccines to prevent a future severe acute respiratory outbreak of coronavirus syndrome 2 (SARS-CoV-2) is significant. Viral surface spike proteins are ideal targets for vaccines. This study aimed to find a multi-subunit chimeric vaccine. After conducting bioinformatics analysis, the recombinant spike (RS) protein of SARS-CoV-2 was deliberately designed and subsequently produced using E. coli expression systems. The immunogenicity of RS and neutralizing antibody responses were evaluated on immunized BALB/c mice. There was a significant difference in antibody titers between RS-immunized mice and control groups. The endpoint of the serum antibody titer of mice immunized with our chimeric protein was 2.5 times higher than that of the negative control. The chimeric construct could present multiple antigens simultaneously, influentially affecting immunization. Sera from mice vaccinated by RS could recognize the SARS-CoV-2 virus and neutralize antibodies. Our chimeric peptide could bind to antibodies in the serum of patients infected with different serotypes of the SARS-CoV-2 virus, such as alpha, delta, and omicron variants. The results indicated that the RS protein would be a potential novel antigenic candidate for subunit vaccine development and could be used as a useful alternative to generate diagnostic serological tests for SARS-CoV-2 infection.

Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants.

The discovery of Zika virus NS2B-NS3 inhibitors with antiviral activity via an integrated virtual screening approach

With expanding recent outbreaks and a lack of treatment options, the Zika virus (ZIKV) poses a severe health concern. The availability of ZIKV NS2B-NS3 co-crystallized structures paved the way for rational drug discovery. A computer-aided structure-based approach was used to screen a diverse library of compounds against ZIKV NS2B-NS3 protease. The top hits were selected based on various binding free energy calculations followed by per-residue decomposition analysis. The selected hits were then evaluated for their biological potential with ZIKV protease inhibition assay and antiviral activity. Among 26 selected compounds, 8 compounds showed promising activity against ZIKV protease with a percentage inhibition of greater than 25 and 3 compounds displayed ∼50% at 10 µM, which indicates an enrichment rate of approximately 36% (threshold IC₅₀ < 10 µM) in the ZIKV-NS2B-NS3 protease inhibition assay. Of these, only one compound (23) produced whole-cell anti-ZIKV activity, and the binding mode of 23 was extensively analyzed through long-run molecular dynamics simulations. The current study provides a promising starting point for the further development of novel compounds against ZIKV.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Designing a multi-epitope vaccine to provoke the robust immune response against influenza A H7N9

A new strain of Influenza A Virus (IAV), so-called "H7N9 Avian Influenza", is the first strain of this virus in which a human is infected by transmitting the N9 of influenza virus. Although continuous human-to-human transmission has not been reported, the occurrence of various H7N9-associated epidemics and the lack of production of strong antibodies against H7N9 in humans warn of the potential for H7N9 to become a new pandemic. Therefore, the need for effective vaccination against H7N9 as a life-threatening viral pathogen has become a major concern. The current study reports the design of a multi-epitope vaccine against Hemagglutinin (HA) and Neuraminidase (NA) proteins of H7N9 Influenza A virus by prediction of Cytotoxic T lymphocyte (CTL), Helper T lymphocyte (HTL), IFN-γ and B-cell epitopes. Human β-defensin-3 (HβD-3) and pan HLA DR-binding epitope (PADRE) sequence were considered as adjuvant. EAAAK, AAY, GPGPG, HEYGAEALERAG, KK and RVRR linkers were used as a connector for epitopes. The final construct contained 777 amino acids that are expected to be a recombinant protein of about ~ 86.38 kDa with antigenic and non-allergenic properties after expression. Modeled protein analysis based on the tertiary structure validation, docking studies, and molecular dynamics simulations results like Root-mean-square deviation (RMSD), Gyration, Root-mean-square fluctuation (RMSF) and Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) showed that this protein has a stable construct and capable of being in interaction with Toll-like receptor 7 (TLR7), TLR8 and m826 antibody. Analysis of the obtained data the demonstrates that suggested vaccine has the potential to induce the immune response by stimulating T and Bcells, and may be utilizable for prevention purposes against Avian Influenza A (H7N9).

Fragment-based in silico design of SARS-CoV-2 main protease inhibitors

3CLpro is essential for SARS-CoV-2 replication and infection; its inhibition using small molecules is a potential therapeutic strategy. In this study, a comprehensive crystallography-guided fragment-based drug discovery approach was employed to design new inhibitors for SARS-CoV-2 3CLpro. All small molecules co-crystallized with SARS-CoV-2 3CLpro with structures deposited in the Protein Data Bank were used as inputs. Fragments sitting in the binding pocket (87) were grouped into eight geographical types. They were interactively coupled using various synthetically reasonable linkers to generate larger molecules with divalent binding modes taking advantage of two different fragments' interactions. In total, 1,251 compounds were proposed, and 7,158 stereoisomers were screened using Glide (standard precision and extra precision), AutoDock Vina, and Prime MMGBSA. The top 22 hits having conformations approaching the linear combination of their constituent fragments were selected for MD simulation on Desmond. MD simulation suggested 15 of these did adopt conformations very close to their constituent pieces with far higher binding affinity than either constituent domain alone. These structures could provide a starting point for the further design of SARS-CoV-2 3CLpro inhibitors with improved binding, and structures are provided.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Proteome-Wide Mapping and Reverse Vaccinology Approaches to Design a Multi-Epitope Vaccine against Clostridium perfringens

C. perfringens is a highly versatile bacteria of livestock and humans, causing enteritis (a common food-borne illness in humans), enterotoxaemia (in which toxins are formed in the intestine which damage and destroy organs, i.e., the brain), and gangrene (wound infection). There is no particular cure for the toxins of C. perfringens. Supportive care (medical control of pain, intravenous fluids) is the standard treatment. Therefore, a multiple-epitope vaccine (MEV) should be designed to battle against C. perfringens infection. Furthermore, the main objective of this in silico investigation is to design an MEV that targets C. perfringens. For this purpose, we selected the top three proteins that were highly antigenic using immuno-informatics approaches, including molecular docking. B-cells, IFN-gamma, and T cells for target proteins were predicted and the most conserved epitopes were selected for further investigation. For the development of the final MEV, epitopes of LBL5, CTL17, and HTL13 were linked to GPGPG, AAY, and KK linkers. The vaccine N-end was joined to an adjuvant through an EAAK linker to improve immunogenicity. After the attachment of linkers and adjuvants, the final construct was 415 amino acids. B-cell and IFN-gamma epitopes demonstrate that the model structure is enhanced for humoral and cellular immune responses. To validate the immunogenicity and safety of the final construct, various physicochemical properties, and other properties such as antigenicity and non-allergens, were evaluated. Furthermore, molecular docking was carried out for verification of vaccine compatibility with the receptor, evaluated in silico. Also, in silico cloning was employed for the verification of the proper expression and credibility of the construct.

Designing and development of epitope-based vaccines against Helicobacter pylori

Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.

Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro

The SARS-CoV-2 was confirmed to cause the global pandemic of coronavirus disease 2019 (COVID-19). The 3-chymotrypsin-like protease (3CLpro), an essential enzyme for viral replication, is a valid target to combat SARS-CoV and MERS-CoV. In this work, we present a structure-based study to identify potential covalent inhibitors containing a variety of chemical warheads. The targeted Asinex Focused Covalent (AFCL) library was screened based on different reaction types and potential covalent inhibitors were identified. In addition, we screened FDA-approved protease inhibitors to find candidates to be repurposed against SARS-CoV-2 3CLpro. A number of compounds with significant covalent docking scores were identified. These compounds were able to establish a covalent bond (C-S) with the reactive thiol group of Cys145 and to form favorable interactions with residues lining the substrate-binding site. Moreover, paritaprevir and simeprevir from FDA-approved protease inhibitors were identified as potential inhibitors of SARS-CoV-2 3CLpro. The mechanism and dynamic stability of binding between the identified compounds and SARS-CoV-2 3CLpro were characterized by molecular dynamics (MD) simulations. The identified compounds are potential inhibitors worthy of further development as COVID-19 drugs. Importantly, the identified FDA-approved anti-hepatitis-C virus (HCV) drugs paritaprevir and simeprevir could be ready for clinical trials to treat infected patients and help curb COVID-19. Communicated by Ramaswamy H. Sarma.

Integrative Analysis of HTNV Glycoprotein Derived MHC II Epitopes by In Silico Prediction and Experimental Validation

Hantaan virus (HTNV), the causative pathogen of hemorrhagic fever with renal syndrome (HFRS), is a negative RNA virus belonging to the Orthohantaviridae family. HTNV envelope glycoprotein (GP), encoded by the genomic medium segment, is immunogenic and is therefore a promising vaccine candidate. Major histocompatibility complex class I (MHC-I) epitopes derived from HTNV has been extensively studied, but little is known of MHC-II epitopes. In silico predictions based on four databases indicated that the full-length HTNV GP has 1121 15-mer epitopes, of which 289 had a high score for binding to the human and murine MHC-II superfamily. It found that epitope ILTVLKFIANIFHTS could potentially bind most MHC-II molecules covering human and murine haplotypes. Dominant epitopes were validated by enzyme-linked immunospot assay of splenocytes from immunized mice; 6 of 10 epitopes supported the predictions including TATYSIVGPANAKVP, TKTLVIGQCIYTITS, FSLLPGVAHSIAVEL, CETYKELKAHGVSCP, CGLYLDRLKPVGSAY, and NLGENPCKIGLQTSS. Conservation analysis of dominant epitopes revealed host–virus interactions without geographic stratification, thus meeting the requirements of candidate vaccines for large-population prophylaxis. These findings provide insight into hantavirus antigenicity and suggest that vaccines targeting MHC-II could provide immune protection in large population to complement symptomatic therapies for the treatment of HFRS.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

Comparative analysis of non structural protein 1 of SARS-CoV2 with SARS-CoV1 and MERS-CoV: An in silico study

The recently emerged SARS-CoV2 caused a major pandemic of coronavirus disease (COVID-19). Non structural protein 1 (nsp1) is found in all beta coronavirus that cause severe respiratory disease. This protein is considered as a virulence factor and has an important role in pathogenesis. This study aims to elucidate the structural conformations of nsp1 to aid in the prediction of epitope sites and identification of important residues for targeted therapy against COVID-19. In this study, molecular modelling coupled with molecular dynamics simulations were performed to analyse the conformational landscape of nsp1 homologs of SARS-CoV1, SARS-CoV2 and MERS-CoV. Principal component analysis escorted by free energy landscape revealed that SARS-CoV2 nsp1 protein shows greater flexibility compared to SARS-CoV1 and MERS-CoV nsp1. Sequence comparison reveals that 28 mutations are present in SARS-CoV2 nsp1 protein compared to SARS-CoV1 nsp1. Several B-cell and T-cell epitopes were identified by an immunoinformatics approach. SARS-CoV2 nsp1 protein binds with the interface region of the palm and finger domain of POLA1 via hydrogen bonding and salt bridge interactions. Taken together, these in silico findings may help in the development of therapeutics specific against COVID-19.

A vaccine inducing solely cytotoxic T lymphocytes fully prevents Zika virus infection and fetal damage

As vaccine-induced non-neutralizing antibodies may cause antibody-dependent enhancement of Zika virus (ZIKV) infection, we test a vaccine that induces only specific cytotoxic T lymphocytes (CTLs) without specific antibodies. We construct a DNA vaccine expressing a ubiquitinated and rearranged ZIKV non-structural protein 3 (NS3). The protein is immediately degraded and processed in the proteasome for presentation via major histocompatibility complex (MHC) class I for CTL generation. We immunize Ifnar1^-/- adult mice with the ubiquitin/NS3 vaccine, impregnate them, and challenge them with ZIKV. Our data show that the vaccine greatly reduces viral titers in reproductive organs and other tissues of adult mice. All mice immunized with the vaccine survived after ZIKV challenge. The vaccine remarkably reduces placenta damage and levels of pro-inflammatory cytokines, and it fully protects fetuses from damage. CD8⁺ CTLs are essential in protection, as demonstrated via depletion experiments. Our study provides a strategy to develop safe and effective vaccines against viral infections.

Structural basis of UDP-N-acetylglucosamine pyrophosphorylase and identification of promising terpenes to control Aedes aegypti

According to the world health organization (WHO) 2020 report, vector borne diseases account for 17 % of all infections with reported 700 thousand death each year. They are of considerable importance to health professionals as they are posing a serious health threat and include dengue fever, Zika fever, chikungunya, yellow fever, and other disease agents. Aedes aegypti serve as a vector for transmitting several of these tropical fevers. In the present study, UDP-N-acetylglucosamine pyrophosphorylase enzyme (Aa-UAP) of A. aegypti which plays a significant contribution in chitin metabolism is targeted with natural terpenes to propose an eco-friendly and novel candidates for the development of new insecticides. The three dimensional Aa-UAP structure was constructed via a comparative homology approach and validated, followed by structure-based virtual screening against 1000 terpenes collected from natural MDP3 and NPACT databases. Top hits were subjected to molecular dynamics (MD) simulations and binding free energies analysis to elucidate complex intermolecular stability and affinity over simulated time. The results demonstrated that Aa-UAP possesses a homodimer state and its active site residues are well conserved. Three compounds (NPACT00138, NPACT00452, and NPACT00839) were prioritized as they are establishing conserved and stable interactions with the active binding-site residues of Aa-UAP. Conclusively, the reported Aa-UAP specific terpenes could serve as promising leads in order to develop potential insecticides. Importantly, the FDA approved drug NPACT00839 (Paclitaxel) could be used further in the fast-track experimental testing pipeline for biological optimization.

Pichia pastoris displaying ZIKV protein epitopes from the Envelope and NS1 induce in vitro immune activation

The severe consequences of ZIKV infection and its emergence and re-emergence in several countries have boosted vaccines' development. Yeasts such as Pichia pastoris has been widely employed as antigen carriers for immunization against infectious agents. Components of the yeast cell wall have immunostimulatory properties, and recombinant antigens can be anchored to the cell surface to enhance the presentation to the immune system. Here we aimed at producing and anchoring ZIKV proteins in the P. pastoris surface as a vaccine approach. Expression cassettes were designed with epitopes of the Envelope and NS1 proteins. Immunofluorescence microscopy confirmed the anchoring of recombinant proteins. Yeasts' ability to stimulate immune cells was evaluated in vitro by incubation with lymphocytes and monocytes isolated from mouse spleen. P. pastoris expressing EnvNS1 epitopes promoted increased levels of IL-6, IL-10, and TNF-α cytokines and an increase in the number of CD4+, CD8+, and CD16+ lymphocytes, similarly to ZIKV. This profile is indicative of the activation of immunological cells and suggests an immunogenic potential of the proposed yeast vaccines against ZIKV, reinforcing the possibility of P. pastoris as adjuvant and carrier of antigens.

Immunoinformatic approach to design a multiepitope vaccine targeting non-mutational hotspot regions of structural and non-structural proteins of the SARS CoV2

BACKGROUND

The rapid Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV2) outbreak caused severe pandemic infection worldwide. The high mortality and morbidity rate of SARS CoV2 is due to the unavailability of vaccination and mutation in this virus. The present article aims to design a potential vaccine construct VTC3 targeting the non-mutational region of structural and non-structural proteins of SARS CoV2.

METHODS

In this study, vaccines were designed using subtractive proteomics and reverse vaccinology. To target the virus adhesion and evasion, 10 different structural and non-structural proteins have been selected. Shortlisted proteins have been screened for B cell, T cell and IFN gamma interacting epitopes. 3D structure of vaccine construct was modeled and evaluated for its physicochemical properties, immunogenicity, allergenicity, toxicity and antigenicity. The finalized construct was implemented for docking and molecular dynamics simulation (MDS) with different toll-like receptors (TLRs) and human leukocyte antigen (HLA). The binding energy and dissociation construct of the vaccine with HLA and TLR was also calculated. Mutational sensitivity profiling of the designed vaccine was performed, and mutations were reconfirmed from the experimental database. Antibody production, clonal selection, antigen processing, immune response and memory generation in host cells after injection of the vaccine was also monitored using immune simulation.

RESULTS

Subtractive proteomics identified seven (structural and non-structural) proteins of this virus that have a role in cell adhesion and infection. The different epitopes were predicted, and only extracellular epitopes were selected that do not have similarity and cross-reactivity with the host cell. Finalized epitopes of all proteins with minimum allergenicity and toxicity were joined using linkers to designed different vaccine constructs. Docking different constructs with different TLRs and HLA demonstrated a stable and reliable binding affinity of VTC3 with the TLRs and HLAs. MDS analysis further confirms the interaction of VTC3 with HLA and TLR1/2 complex. The VTC3 has a favorable binding affinity and dissociation constant with HLA and TLR. The VTC3 does not have similarities with the human microbiome, and most of the interacting residues of VTC3 do not have mutations. The immune simulation result showed that VTC3 induces a strong immune response. The present study designs a multiepitope vaccine targeting the non-mutational region of structural and non-structural proteins of the SARS CoV2 using an immunoinformatic approach, which needs to be experimentally validated.

Immunoinformatics analysis to design novel epitope based vaccine candidate targeting the glycoprotein and nucleoprotein of Lassa mammarenavirus (LASMV) using strains from Nigeria

Lassa mammarenavirus (LASMV) is responsible for a specific type of acute viral hemorrhagic fever known as Lassa fever. Lack of effective treatments and counter-measures against the virus has resulted in a high mortality rate in its endemic regions. Therefore, in this study, a novel epitope-based vaccine has been designed using the methods of immunoinformatics targeting the glycoprotein and nucleoprotein of the virus. After numerous robust analyses, two CTL epitopes, eight HTL epitopes and seven B-cell epitopes were finally selected for constructing the vaccine. All these most promising epitopes were found to be antigenic, non-allergenic, nontoxic and non-human homolog, which made them suitable for designing the subunit vaccine. Furthermore, the selected T-cell epitopes which were found to be fully conserved across different isolates of the virus, were also considered for final vaccine construction. After that, numerous validation experiments, i.e. molecular docking, molecular dynamics simulation and immune simulation were conducted, which predicted that our designed vaccine should be stable within the biological environment and effective in combating the LASMV infection. In the end, codon adaptation and in silico cloning studies were performed to design a recombinant plasmid for producing the vaccine industrially. However, further in vitro and in vivo assessments should be done on the constructed vaccine to finally confirm its safety and efficacy.Communicated by Ramaswamy H. Sarma.

Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches

Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5-832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening

BACKGROUND

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.

METHODS

In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.

RESULTS

The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.

CONCLUSION

Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Immunoinformatics Approach for the Identification and Characterization of T Cell and B Cell Epitopes towards the Peptide-Based Vaccine against SARS-CoV-2

Presently, immunoinformatics is playing a significant role in epitope identification and vaccine designing for various critical diseases. Using immunoinformatics, several scientists are trying to identify and characterize T cell and B cell epitopes as well as design peptide-based vaccine against SARS-CoV-2. In this review article, we have tried to discuss the importance in adaptive immunity and its significance for designing the SARS-CoV-2 vaccine. Moreover, we have attempted to illustrate several significant key points for utilizing immunoinformatics for vaccine designing, such as the criteria for selection and identification of epitopes, T cell epitope, and B cell epitope prediction and different emerging tools/databases for immunoinformatics. In the current scenario, a few immunoinformatics studies have been performed for various infectious pathogens and related diseases. Thus, we have also summarized and included these current immunoinformatics studies in this review article. Finally, we have discussed about the probable T cell and B cell epitopes and their identification and characterization for vaccine designing against SARS-CoV-2.

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Identification of novel human USP2 inhibitor and its putative role in treatment of COVID-19 by inhibiting SARS-CoV-2 papain-like (PLpro) protease

Human ubiquitin carboxyl-terminal hydrolase-2 (USP2) inhibitors, such as thiopurine analogs, have been reported to inhibit SARS-CoV papain-like proteases (PLpro). The PLpro have significant functional implications in the innate immune response during SARS-CoV-2 infection and considered an important antiviral target. Both proteases share strikingly similar USP fold with right-handed thumb-palm-fingers structural scaffold and conserved catalytic triad Cys-His-Asp/Asn. In this urgency situation of COVID-19 outbreak, there is a lack of in-vitro facilities readily available to test SARS-CoV-2 inhibitors in whole-cell assays. Therefore, we adopted an alternate route to identify potential USP2 inhibitor through integrated in-silico efforts. After an extensive virtual screening protocol, the best compounds were selected and tested. The compound Z93 showed significant IC50 value against Jurkat (9.67 μM) and MOTL-4 cells (11.8 μM). The binding mode of Z93 was extensively analyzed through molecular docking, followed by MD simulations, and molecular interactions were compared with SARS-CoV-2. The relative binding poses of Z93 fitted well in the binding site of both proteases and showed consensus π-π stacking and H-bond interactions with histidine and aspartate/asparagine residues of the catalytic triad. These results led us to speculate that compound Z93 might be the first potential chemical lead against SARS-CoV-2 PLpro, which warrants in-vitro evaluations.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

In silico analysis and in vivo assessment of a novel epitope-based vaccine candidate against uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) are common pathogens in urinary tract infections (UTIs), which show resistance to antibiotics. Therefore, there is a need for a vaccine to reduce susceptibility to the infection. In the present study, bioinformatics approaches were employed to predict the best B and T-cell epitopes of UPEC virulence proteins to develop a multiepitope vaccine candidate against UPEC. Then, the efficacy of the candidate was studied with and without Freund adjuvant. Using bioinformatics methods, 3 epitope-rich domains of IutA and FimH antigens were selected to construct the fusion. Molecular docking and Molecular dynamics (MD) simulation were employed to investigate in silico interaction between designed vaccine and Toll-like receptor 4 (TLR4). Our results showed that the levels of IgG and IgA antibodies were improved in the serum and mucosal samples of the vaccinated mice, and the IgG responses were maintained for at least 6 months. The fusion protein was also able to enhance the level of cytokines IFN.γ (Th1), IL.4 (Th2), and IL.17. In challenge experiments, all vaccine combinations showed high potency in the protection of the urinary tract even after 6 months post first injection. The present study indicates that the designed candidate is able to evoke strong protective responses which warrant further studies.

Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

COVID-19 has been declared as a global health emergency and exposed the world to a deadly virus, which has dramatically changed the lives of humans for an unknown period of time. In the battleground with the virus, we have employed an immunoinformatics framework to design a robust vaccine as an insurance plan for the future. The pathogenic sequence with cryptic epitope taken from patients in Wuhan, China, was harnessed to design a promiscuous cytotoxic T-lymphocyte, helper T-lymphocyte, and B-cell epitope based subunit vaccine, engineered with adjuvants and conformational linkers. The reported vaccine has high antigenicity and immunogenicity profiles with potential TAP affinity, which ensures elevated antigen processing capability. It has strong binding with major histocompatibility complex (MHC) receptors (MHC-1 and MHC-2) and virus-specific membrane receptor TLR-2, with scores of -1010.7, -1035.7, and -1076.3 kcal mol-1, respectively. Molecular dynamics simulation analysis was used to assess the stable binding with TLR-2 with minimal atomic motions through a deformation plot, covariance matrix, and elastic network. Importantly, an in silico immunization assay showed the reliable elicitation of key players in terms of immune cells together with memory cells to evoke adaptive immune responses upon administration of the construct. In view of favorable outcomes, we also propose a plausible vaccine mechanism to elicit an immune response to fight coronavirus.

Immunoinformatics approach for predicting epitopes in HN and F proteins of Porcine rubulavirus

Porcine rubulavirus (PRV), which belongs to the family Paramyxoviridae, causes blue eye disease in pigs, characterized by encephalitis and reproductive failure in newborn and adult pigs, respectively. There is no effective treatment against PRV and no information on the effectiveness of the available vaccines. Continuous outbreaks have occurred in Mexico since the early 1980s, which have caused serious economic losses to pig producers. Vaccination can be used to control this disease. Searching for effective antigen candidates against PRV, we first sequenced the PAC1 F protein, then we used various immunoinformatics tools to predict antigenic determinants of B-cells and T-cells against the two glycoproteins of the virus (HN and F proteins). Finally, we used AutoDock Vina to determine the binding energies. We obtained the F gene sequence of a PRV strain collected in the early 1990s in Mexico and compared its amino acid profile with previous and more recent strains, obtaining an identity similarity of 97.78 to 99.26%. For the F proteins, seven linear B-cell epitopes, six conformational B-cell epitopes and twenty-nine T-cell MHC class I epitopes were predicted. For the HN proteins, sixteen linear B-cell epitopes, seven conformational B-cell epitopes and thirty-four T-cell MHC class I epitopes were predicted. The ATRSETDYY and AAYTTTTCF epitopes of the HN protein might be important for neutralizing the viral infection. We determined the in silico binding energy between the predicted epitopes on the F and HN proteins and swine MHC-I molecules. The binding energy of these epitopes ranged from -5.8 to -7.8 kcal/mol. The present study aimed to assess the use of HN and F proteins as antigens, either as recombinant proteins or as a series of peptides that could activate different responses of the immune system. This may help identify relevant immunogens, saving time and costs in the development of new vaccines or diagnostic tools.

A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach

The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few repurposed drugs. The urgency of the situation requires the development of SARS-CoV-2-based vaccines. Immunoinformatic and molecular modelling are time-efficient methods that are generally used to accelerate the discovery and design of the candidate peptides for vaccine development. In recent years, the use of multiepitope vaccines has proved to be a promising immunization strategy against viruses and pathogens, thus inducing more comprehensive protective immunity. The current study demonstrated a comprehensive in silico strategy to design stable multiepitope vaccine construct (MVC) from B-cell and T-cell epitopes of essential SARS-CoV-2 proteins with the help of adjuvants and linkers. The integrated molecular dynamics simulations analysis revealed the stability of MVC and its interaction with human Toll-like receptors (TLRs), which trigger an innate and adaptive immune response. Later, the in silico cloning in a known pET28a vector system also estimated the possibility of MVC expression in Escherichia coli. Despite that this study lacks validation of this vaccine construct in terms of its efficacy, the current integrated strategy encompasses the initial multiple epitope vaccine design concepts. After validation, this MVC can be present as a better prophylactic solution against COVID-19.

Exploring membrane proteins of Leishmania major to design a new multi-epitope vaccine using immunoinformatics approach

Leishmaniasis is one of the major global endemic diseases. Among all the different forms of the disease, cutaneous Leishmaniasis has the highest prevalence worldwide. Treatment with current drugs has not had a significant effect on the improvement of the disease. An attempt to replace an appropriate vaccine that can stimulate host cellular immunity and induce the response of Major histocompatibility complex I (MHCI) and Major histocompatibility complex II (MHCII) against Leishmania is essential. Vaccine production remains a challenge despite the use of different antigens for vaccination against Leishmania major. Hence, we were used the immunoinformatics approach to design a new multi-epitope vaccine against L. major using immunogenic outer membrane proteins. Helper T-lymphocyte (HTL) and Cytotoxic T lymphocyte (CTL) epitopes were predicted and for final confirmation of the selected epitopes, docking analysis, and molecular dynamics simulation was performed. Then, GDGDG linker and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, and allergenicity. Moreover, the secondary and three-dimensional structure of the final construct was identified. In silico cloning approach was carried out to improve expression of the vaccine construct. Finally, molecular docking, followed by molecular dynamic was performed to determine the interaction between multi-epitope vaccine and TLR11. We hope that the designed vaccine can be a good candidate for the development of cutaneous leishmaniasis vaccine. but its effectiveness should be assessed in vivo.

Immunoinformatics and molecular dynamics studies to predict T-cell-specific epitopes of four Klebsiella pneumoniae fimbriae antigens

Klebsiella pneumoniae (K. pneumoniae) is a causative agent of severe infections in humans. There is no publically available vaccine for K. pneumoniae infections yet. Here, using comprehensive immunoinformatics methods, T-cell-specific epitopes of four type 1 fimbriae antigens of K. pneumoniae were predicted and evaluated as potential vaccine candidates. Both CD8+ (class I) and CD4+ (class II) T-cell-specific epitopes were predicted and the epitopes similar to human proteome were excluded. Subsequently, the windows of class-II epitopes containing class-I epitopes were determined. The immunogenicity, IFN-γ production and population coverage were also estimated. Using the 3D structure of HLA and epitopes, molecular docking was carried out. Two best epitopes were selected for molecular dynamics studies. Our prediction and analyses resulted in the several dominant epitopes for each antigen. The docking results showed that all selected epitopes can bind to their restricted HLA molecules with high affinity. The molecular dynamics results indicated the stability of system with minimum possible deviation, suggesting the selected epitopes can be promising candidates for stably binding to HLA molecules. Altogether, our results suggest that the selected T-cell-specific epitopes of K. pneumoniae fimbriae antigens, particularly the two epitopes confirmed by molecular dynamics, can be applied for vaccine development. However, the in vitro and in vivo studies are required to authenticate the results of the present study.Communicated by Ramaswamy H. Sarma.

Isolation of Antidiabetic Withanolides from Withania coagulans Dunal and Their In Vitro and In Silico Validation

Withania coagulans (W. coagulans) is well-known in herbal medicinal systems for its high biological potential. Different parts of the plant are used against insomnia, liver complications, asthma, and biliousness, as well as it is reported to be sedative, emetic, diuretic, antidiabetic antimicrobial, anti-inflammatory, antitumor, hepatoprotective, antihyperglycemic, cardiovascular, immuno-suppressive and central nervous system depressant. Withanolides present in W. coagulans have attracted an immense interest in the scientific field due to their diverse therapeutic applications. The current study deals with chemical and biological evaluation of chloroform, and n-butanol fractions of W. coagulans. The activity-guided fractionation of both extracts via multiple chromatographic steps and structure elucidation of pure isolates using spectroscopies (NMR, mass spectrometry, FTIR and UV-Vis) led to the identification of a new withanolide glycoside, withacogulanoside-B (1) from n-butanol extract and five known withanolides from chloroform extract [withanolid J (2), coagulin E (3), withaperuvin C (4), 27-hydroxywithanolide I (5), and ajugin E (6)]. Among the tested compounds, compound 5 was the most potent α-glucosidase inhibitor with IC₅₀ = 66.7 ± 3.6 µM, followed by compound 4 (IC₅₀: 407 ± 4.5 µM) and compound 2 (IC₅₀: 683 ± 0.94 µM), while no antiglycation activity was observed with the six isolated compounds. Molecular docking was used to predict the binding potential and binding site interactions of these compounds as α-glucosidase inhibitors. Consequently, this study provides basis to discover specific antidiabetic compounds from W. coagulans.

Vaccine candidate designed against carcinoembryonic antigen-related cell adhesion molecules using immunoinformatics tools

Carcinoembryonic antigen-related cell adhesion (CEACAM) molecules belong to a family of membrane glycoproteins that mediate intercellular interactions influencing cellular growth, immune cell activation, apoptosis, and tumor suppression. Several family members (CEACAM1, CEACAM5, and CEACAM6) are highly expressed in cancers, and they share a conserved N-terminal domain that serves as an attractive target for cancer immunotherapy. A multi-epitope vaccine candidate against this conserved domain has been developed using immunoinformatics tools. Specifically, several epitopes predicted to interact with MHC class I and II molecules were linked together with appropriate linkers. The tertiary structure of the vaccine is generated by homology and ab initio modeling. Molecular docking of epitopes to MHC structures have revealed that the lowest energy conformations are the epitopes bound to the antigen-binding groove of the MHC molecules. Subsequent molecular dynamics simulation has confirmed the stability of the binding conformations in solution. The predicted vaccine has relatively high antigenicity and low allergenicity, suggesting that it is an ideal candidate for further refinement and development.Communicated by Ramaswamy H. Sarma.

A candidate multi-epitope vaccine against SARS-CoV-2

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2. The overall quality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the stability of the designed vaccine. Docking studies revealed stable interactions of the vaccine with Toll-Like Receptors and MHC Receptors. The in silico cloning and codon optimization supported the proficient expression of the designed vaccine in E. coli expression system. The efficiency of the candidate vaccine to trigger an effective immune response was assessed by an in silico immune simulation. The computational analyses suggest that the designed multi-epitope vaccine is structurally stable which can induce specific immune responses and thus, can be a potential vaccine candidate against SARS-CoV-2.

Current Status of Zika Virus Vaccines: Successes and Challenges

The recently emerged Zika virus (ZIKV) spread to the Americas, causing a spectrum of congenital diseases including microcephaly in newborn and Guillain-Barré syndrome (GBS) in adults. The unprecedented nature of the epidemic and serious diseases associated with the viral infections prompted the global research community to understand the immunopathogenic mechanisms of the virus and rapidly develop safe and efficacious vaccines. This has led to a number of ZIKV vaccine candidates that have shown significant promise in human clinical trials. These candidates include nucleic acid vaccines, inactivated vaccines, viral-vectored vaccines, and attenuated vaccines. Additionally, a number of vaccine candidates have been shown to protect animals in preclinical studies. However, as the epidemic has waned in the last three years, further development of the most promising vaccine candidates faces challenges in clinical efficacy trials, which is needed before a vaccine is brought to licensure. It is important that a coalition of government funding agencies and private sector companies is established to move forward with a safe and effective vaccine ready for deployment when the next ZIKV epidemic occurs.

Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study

At present, novel Coronavirus (2019-nCoV, the causative agent of COVID-19) has caused worldwide social and economic disruption. The disturbing statistics of this infection promoted us to develop an effective vaccine candidate against the COVID-19. In this study, bioinformatics approaches were employed to design and introduce a novel multi-epitope vaccine against 2019-nCoV that can potentially trigger both CD⁴⁺ and CD⁸⁺ T-cell immune responses and investigated its biological activities by computational tools. Three known antigenic proteins (Nucleocapsid, ORF3a, and Membrane protein, hereafter called NOM) from the virus were selected and analyzed for prediction of the potential immunogenic B and T-cell epitopes and then validated using bioinformatics tools. Based on in silico analysis, we have constructed a multi-epitope vaccine candidate (NOM) with five rich-epitopes domain including highly scored T and B-cell epitopes. After predicting and evaluating of the third structure of the protein candidate, the best 3 D predicted model was applied for docking studies with Toll-like receptor 4 (TLR4) and HLA-A*11:01. In the next step, molecular dynamics (MD) simulation was used to evaluate the stability of the designed fusion protein with TLR4 and HLA-A*11:01 receptors. MD studies demonstrated that the NOM-TLR4 and NOM-HLA-A*11:01 docked models were stable during simulation time. In silico evaluation showed that the designed chimeric protein could simultaneously elicit humoral and cell-mediated immune responses. Communicated by Ramaswamy H. Sarma.

Computational Methodology for Peptide Vaccine Design for Zika Virus: A Bioinformatics Approach

With the increasing frequency of viral epidemics, vaccines to augment the human immune response system have been the medium of choice to combat viral infections. The tragic consequences of the Zika virus pandemic in South and Central America a few years ago brought the issues into sharper focus. While traditional vaccine development is time-consuming and expensive, recent advances in information technology, immunoinformatics, genetics, bioinformatics, and related sciences have opened the doors to new paradigms in vaccine design and applications.Peptide vaccines are one group of the new approaches to vaccine formulation. In this chapter, we discuss the various issues involved in the design of peptide vaccines and their advantages and shortcomings, with special reference to the Zika virus for which no drugs or vaccines are as yet available. In the process, we outline our work in this field giving a detailed step-by-step description of the protocol we follow for such vaccine design so that interested researchers can easily follow them and do their own designing. Several flowcharts and figures are included to provide a background of the software to be used and results to be anticipated.