AllerTOP--a server for in silico prediction of allergens

Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine's design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of -1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.Communicated by Ramaswamy H. Sarma.

Leveraging immunoinformatics for developing a multi-epitope subunit vaccine against Helicobacter pylori and Fusobacterium nucleatum

Gastric ulcers caused by Helicobacter pylori and Fusobacterium nucleatum remain a significant global health concern without an established vaccine. In this study, we utilized immunoinformatics methods to design a multi-epitope vaccine targeting these pathogens. Outer membrane proteins from H. pylori and F. nucleatum were scrutinized to identify high antigenic T-cell and B-cell epitopes. The resulting vaccine comprised carefully analyzed and evaluated epitopes, including cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocytes epitopes. This vaccine exhibited notable antigenicity, suitable immunogenicity, and demonstrated non-allergenicity and non-toxicity. It displayed favorable physiochemical characteristics and high solubility. In interaction studies, the vaccine exhibited robust binding to toll-like receptor 4 (TLR4). Molecular dynamic simulations revealed cohesive structural integrity and stable attachment. Codon adaptation utilizing Escherichia coli K12 host yielded a vaccine with elevated Codon Adaptation Index (CAI) and optimal GC content. In silico cloning into the pET28+(a) vector demonstrated efficient expression. Immune simulations indicated the vaccine's ability to initiate immune responses in humans, mirroring real-life scenarios. Based on these comprehensive findings, we propose that our developed vaccine has the potential to confer robust immunity against H. pylori and F. nucleatum infections.Communicated by Ramaswamy H. Sarma.

Alg-MFDL: A multi-feature deep learning framework for allergenic proteins prediction

The escalating global incidence of allergy patients illustrates the growing impact of allergic issues on global health. Allergens are small molecule antigens that trigger allergic reactions. A widely recognized strategy for allergy prevention involves identifying allergens and avoiding re-exposure. However, the laboratory methods to identify allergenic proteins are often time-consuming and resource-intensive. There is a crucial need to establish efficient and reliable computational approaches for the identification of allergenic proteins. In this study, we developed a novel allergenic proteins predictor named Alg-MFDL, which integrates pre-trained protein language models (PLMs) and traditional handcrafted features to achieve a more complete protein representation. First, we compared the performance of eight pre-trained PLMs from ProtTrans and ESM-2 and selected the best-performing one from each of the two groups. In addition, we evaluated the performance of three handcrafted features and different combinations of them to select the optimal feature or feature combination. Then, these three protein representations were fused and used as inputs to train the convolutional neural network (CNN). Finally, the independent validation was performed on benchmark datasets to evaluate the performance of Alg-MFDL. As a result, Alg-MFDL achieved an accuracy of 0.973, a precision of 0.996, a sensitivity of 0.951, and an F1 value of 0.973, outperforming the most of current state-of-the-art (SOTA) methods across all key metrics. We anticipated that the proposed model could be considered a useful tool for predicting allergen proteins.

Development of a broad-spectrum epitope-based vaccine against Streptococcus pneumoniae

Streptococcus pneumoniae (SPN) is a significant pathogen causing pneumonia and meningitis, particularly in vulnerable populations like children and the elderly. Available pneumonia vaccines have limitations since they only cover particular serotypes and have high production costs. The emergence of antibiotic-resistant SPN strains further underscores the need for a new, cost-effective, broad-spectrum vaccine. Two potential vaccine candidates, CbpA and PspA, were identified, and their B-cell, CTL, and HTL epitopes were predicted and connected with suitable linkers, adjivant and PADRE sequence. The vaccine construct was found to be antigenic, non-toxic, non-allergenic, and soluble. The three-dimensional structure of the vaccine candidate was built and validated. Docking analysis of the vaccine candidate by ClusPro demonstrated robust and stable binding interactions between the MEV and toll-like receptor 4 in both humans and animals. The iMOD server and Amber v.22 tool has verified the stability of the docking complexes. GenScript server confirmed the high efficiency of cloning for the construct and in-silico cloning into the pET28a (+) vector using SnapGene, demonstrating successful translation of the epitope region. Immunological responses were shown to be enhanced by the C-IMMSIM server. This study introduced a strong peptide vaccine candidate that has the potential to contribute to the development of a rapid and cost-effective solution for combating SPN. However, experimental verification is necessary to evaluate the vaccine's effectiveness.

Molecular dynamics simulation based prediction of T-cell epitopes for the production of effector molecules for liver cancer immunotherapy

Liver cancer is the sixth most frequent malignancy and the fourth major cause of deaths worldwide. The current treatments are only effective in early stages of cancer. To overcome the therapeutic challenges and exploration of immunotherapeutic options, broad spectral therapeutic vaccines could have significant impact. Based on immunoinformatic and integrated machine learning tools, we predicted the potential therapeutic vaccine candidates of liver cancer. In this study, machine learning and MD simulation-based approach are effectively used to design T-cell epitopes that aid the immune system against liver cancer. Antigenicity, molecular weight, subcellular localization and expression site predictions were used to shortlist liver cancer associated proteins including AMBP, CFB, CDHR5, VTN, APOBR, AFP, SERPINA1 and APOE. We predicted CD8+ T-cell epitopes of these proteins containing LGEGATEAE, LLYIGKDRK, EDIGTEADV, QVDAAMAGR, HLEARKKSK, HLCIRHEMT, LKLSKAVHK, EQGRVRAAT and CD4+ T-cell epitopes of VLGEGATEA, WVTKQLNEI, VEEDTKVNS, FTRINCQGK, WGILGREEA, LQDGEKIMS, VKFNKPFVF, VRAATVGSL. We observed the substantial physicochemical properties of these epitopes with a significant binding affinity with MHC molecules. A polyvalent construct of these epitopes was designed using suitable linkers and adjuvant indicated significant binding energy (>-10.5 kcal/mol) with MHC class-I and II molecule. Based on in silico cloning, we found the considerable compatibility of this polyvalent construct with the E. coli expression system and the efficiency of its translation in host. The system-level and machine learning based cross validations showed the possible effect of these T-cell epitopes as potential vaccine candidates for the treatment of liver cancer.

From bytes to bites: In-silico creation of a novel multi-epitope vaccine against Murray Valley Encephalitis Virus

Flaviviruses transmitted by arthropods, including the Murray Valley Encephalitis Virus (MVEV), are RNA viruses capable of causing severe encephalitis in various hosts. The spread of these viruses is closely linked to climatic conditions and the habitats of host and vector species, leading to outbreaks in new geographic regions. Notable encephalitis-causing flaviviruses include Japanese encephalitis virus (JEV), West Nile virus (WNV), and Kunjin virus (KUNV). MVEV, primarily spread by the mosquito Culex annulirostris and amplified by water birds such as egrets and Nankeen night herons, has caused significant outbreaks in Australia, including severe epidemics in 1951, 1956, and 1974. Despite its severity, no rapid diagnostic techniques or effective antiviral treatments are available, and current interventions are limited to supportive care and mosquito management. Given the absence of a licensed vaccine, this study aimed to develop a multi-epitope hybrid vaccine targeting MVEV using in silico approaches. The study focused on identifying B-cell and T-cell epitopes from the MVEV Envelope (E) protein, constructing a vaccine candidate, and computationally validating its immunogenic potential. The designed vaccine underwent rigorous analysis of its antigenic properties, allergenicity, and toxicity. Disulfide engineering and assessment of physicochemical properties ensured the structural integrity of the vaccine, supported by Ramachandran plot and ProSA web analyses. Molecular docking studies assessed the vaccine's binding affinities with TLR-3, and MHC-I. Population coverage analysis of MHC-I and MHC-II epitopes evaluated global efficacy. Additionally, molecular dynamics simulations explored the stability of docked complexes, and PDBsum analysis elucidated interaction details. Immunological simulations were conducted to predict immune response outcomes, providing comprehensive validation of the vaccine's antigenicity. The findings highlight the potential of a multi-epitope vaccine as a viable strategy for MVEV prevention.

Designing multi-epitope vaccines against Echinococcus granulosus: an in-silico study using immuno-informatics

Cystic echinococcosis (CE) is a worldwide zoonotic public health issue. The reasons for this include a lack of specific therapy options, increasing antiparasitic drug resistance, a lack of control strategies, and the absence of an approved vaccine. The aim of the current study is to develop a multiepitope vaccine against CE by in-silico identification and using different Antigen B subunits. The five Echinococcus granulosus antigen B (EgAgB) subunits were examined for eminent antigenic epitopes, and then the best B-cell and Major Histocompatibility Complex MHC-binding epitopes were predicted. Most significant epitopes were combined to create an effective multi-epitope vaccine, which was then validated by testing its secondary and tertiary structures, physicochemical properties, and molecular dynamics (MD) modelling. A multi-epitope vaccine construct of 483 amino acid sequences was designed. It contains B-cell, Helper T Lymphocyte (HTL), and Cytotoxic T Lymphocyte (CTL) epitopes as well as the appropriate adjuvant and linker molecules. The resultant vaccinal construct had a GDT-HA value of 0.9725, RMSD of 0.299, MolProbity of 1.891, Clash score of 13.1, Poor rotamers of 0.9, and qualifying features with Rama favoured of 89.9. It was also highly immunogenic and less allergic. The majority of the amino acids were positioned in the Ramachandran plot's favourable area, and during the molecular dynamic simulation at 100 ns, no notable structural abnormalities were noticed. The resultant construct was significantly expressed and received good endorsement in the pIB2-SEC13-mEGFP expressional vector. In conclusion, the current in-silico multi-epitope vaccine may be evaluated in-vitro, in-vivo, and in clinical trials as an immunogenic vaccine model. It can also play a vital role in preventing this zoonotic parasite infection.

In silico design and assessment of a multi-epitope peptide vaccine against multidrug-resistant Acinetobacter baumannii

Acinetobacter baumannii, an opportunistic and notorious nosocomial pathogen, is responsible for many infections affecting soft tissues, skin, lungs, bloodstream, and urinary tract, accounting for more than 722,000 cases annually. Despite the numerous advancements in therapeutic options, no approved vaccine is currently available for this particular bacterium. Consequently, this study focused on creating a rational vaccine design using bioinformatics tools. Three outer membrane proteins with immunogenic potential and properties of good vaccine candidates were used to select epitopes based on good antigenic properties, non-allergenicity, high binding scores, and a low IC50 value. A multi-epitope peptide (MEP) construct was created by sequentially linking the epitopes using suitable linkers. ClusPro 2.0 and C-ImmSim web servers were used for docking analysis with TLR2/TLR4 and immune response respectively. The Ramachandran plot showed an accurate model of the MEP with 100% residue in the most favored and allowed regions. The construct was highly antigenic, stable, non-allergenic, non-toxic, and soluble, and showed maximum population coverage. Additionally, molecular docking demonstrated strong binding between the designed MEP vaccine and TLR2/TLR4. In silico immunological simulations showed significant increases in T-cell and B-cell populations. Finally, codon optimization and in silico cloning were conducted using the pET-28a (+) plasmid vector to evaluate the efficiency of the expression of vaccine peptide in the host organism (Escherichia coli). This designed MEP vaccine would support and accelerate the laboratory work to develop a potent vaccine targeting MDR Acinetobacter baumannii.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00292-3.

Multi-epitope Based Peptide Vaccine Candidate Against Babesia Infection From Rhoptry-Associated Protein 1 (RAP-1) Antigen Using Immuno-Informatics: An In Silico Approach

Objective

Babesiosis is a significant haemoparasitic infection caused by apicomplexan parasites of the genus Babesia. This infection has continuously threatened cattle farmers owing to its devastating effects on productivity and severe economic implications. Failure to curb the increase of the infection has been attributed to largely ineffective vaccines. This study was designed to develop a potential vaccine candidate.

Method

Rhoptry-associated protein-1 (RAP-1) was used to identify and design a potential multi-epitope vaccine candidate due to its immunogenic properties through an immunoinformatics approach.

Results and conclusions

A multi-epitope vaccine comprising 11 CD8+, 17 CD4+, and 3 B-cell epitopes was constructed using the AAY, GPGPG, and KK linkers. Beta-defensin-3 was added as an adjuvant to potentiate the immune response using the EAAK linker. The designed vaccine was computationally predicted to be antigenic (antigenicity scores: 0.6), soluble (solubility index: 0.730), and non-allergenic. The vaccine construct comprises 595 amino acids with a molecular weight of 64 152 kDa, an instability and aliphatic index of 13.89 and 65.82, which confers stability with a Grand average of hydropathicity (GRAVY) value of 0.122, indicating the hydrophobicity of the construct. Europe has the highest combined class population coverage, with a percentage of 96.07%, while Central America has the lowest population coverage, with a value of 22.94%. The DNA sequence of the vaccine construct was optimized and successfully cloned into a pET-28a (+) plasmid vector. Analysis of binding interactions indicated the stability of the complex when docked with Toll-like receptor-2 (TLR-2). The subunit vaccine construct was predicted to induce and boost sufficient host cellular and humoral responses in silico. However, further experimental research and analysis is required to validate the findings.

Limitation

This study is purely computational, and further experimental validation of these findings through in vivo and in vitro conditions is required.

In Silico design of a multi-epitope vaccine for Human Parechovirus: Integrating immunoinformatics and computational techniques

Human parechovirus (HPeV) is widely recognized as a severe viral infection affecting infants and neonates. Belonging to the Picornaviridae family, HPeV is categorized into 19 distinct genotypes. Among them, HPeV-1 is the most prevalent genotype, primarily associated with respiratory and digestive symptoms. Considering HPeV's role as a leading cause of life-threatening viral infections in infants and the lack of effective antiviral therapies, our focus centered on developing two multi-epitope vaccines, namely HPeV-Vax-1 and HPeV-Vax-2, using advanced immunoinformatic techniques. Multi-epitope vaccines have the advantage of protecting against various virus strains and may be preferable to live attenuated vaccines. Using the NCBI database, three viral protein sequences (VP0, VP1, and VP3) from six HPeV strains were collected to construct consensus protein sequences. Then the antigenicity, toxicity, allergenicity, and stability were analyzed after discovering T-cell and linear B-cell epitopes from the protein sequences. The fundamental structures of the vaccines were produced by fusing the selected epitopes with appropriate linkers and adjuvants. Comprehensive physicochemical, antigenic, allergic assays, and disulfide engineering demonstrated the effectiveness of the vaccines. Further refinement of secondary and tertiary models for both vaccines revealed promising interactions with toll-like receptor 4 (TLR4) in molecular docking, further confirmed by molecular dynamics simulation. In silico immunological modeling was employed to assess the vaccine's capacity to stimulate an immune reaction. In silico immunological simulations were employed to evaluate the vaccines' ability to trigger an immune response. Codon optimization and in silico cloning analyses showed that Escherichia coli (E. coli) was most likely the host for the candidate vaccines. Our findings suggest that these multi-epitope vaccines could be the potential HPeV vaccines and are recommended for further wet-lab investigation.

Refolding dynamics and immunoinformatic insights into Vibrio cholerae OmpA, OmpK, and OmpV for vaccine applications

OmpA, OmpK, and OmpV are crucial for the pathogenesis of Vibrio cholerae, functioning within the bacterium's outer membrane; they present significant potential as candidates for vaccine development. Due to their intrinsic β-sheet richness, these OMPs tend to form inclusion bodies whenever overexpression is attempted. To achieve a native-like structure, detergents can be utilized during the refolding of OMPs from inclusion bodies. The impact of different detergents is examined on the renaturation of these OMPs, specifically non-ionic and zwitterionic detergents. The findings provide valuable insights into detergent selection, with LDAO and DDM emerging as the best protein refolding agents, facilitating successful structural and functional studies of these OMPs. Furthermore, using immunoinformatics it is established that OmpA, OmpK, and OmpV carry B- and T-cell epitopes in their exposed extracellular regions. The presence of immunodominant regions makes it easier to employ these proteins as vaccine candidates as they are stable, non-allergenic, and likely to stimulate successful innate and active immune responses. Overall, with all three OMPs harboring numerous immunogenic epitopes, they can be employed in subunit vaccines against Vibrio spp. and contribute to the development of diagnostic tools for effective disease mitigation.

Unlocking the Mycobacteroides abscessus pan-genome using computational tools: insights into evolutionary dynamics and lifestyle

Mycobacteroides abscessus is a non-tuberculous mycobacteria implicated in causing lung infections. It is difficult to control owing to resistance to antibiotics and disinfectants. This work was aimed at comprehending: the pan-genome architecture, evolutionary dynamics, and functionalities of pan-genome components linked to COGs and KEGG. Around 2802 core genes were present in each strain of the M. abscessus genome. The number of accessory genes ranged from 1615 to 2481. The open pan-genome of M. abscessus was attributed to the accessory genes underlining its adaptability in the host. Phylogenetic analysis revealed cluster-based relationships and highlighted factors shaping variability and adaptive capabilities. Transcription, metabolism, and pathogenic genes were vital for M. abscessus lifestyle. The accessory genes contributed to the diverse metabolic capability. The incidence of a significant portion of secondary metabolite biosynthesis genes provided insights for investigating their biosynthetic gene clusters. Additionally, a high proportion of xenobiotic biodegradation genes highlighted potential metabolic capabilities. In silico screening identified a potential vaccine candidate among hypothetical proteins in COGs. Functional analysis of M. abscessus pan-genome components unveiled factors associated with virulence, pathogenicity, infection establishment, persistence, and resistance. Notable amongst them were: MMPL family transporters, PE-PPE domain-containing proteins, TetR family transcriptional regulators, ABC transporters, Type-I, II, III, VII secretion proteins, DUF domain-containing proteins, cytochrome P450, VapC family toxin, virulence factor Mce family protein, type II toxin-antitoxin system. Overall, these results enhanced understanding of the metabolism, host-pathogen dynamics, pathogenic lifestyle, and adaptations. This will facilitate further investigations for combating infections and designing suitable therapies.

Immunoinformatics Design of a Multiepitope Vaccine (MEV) Targeting Streptococcus mutans: A Novel Computational Approach

Dental caries, a persistent oral health challenge primarily linked to Streptococcus mutans, extends its implications beyond dental decay, affecting over 4 billion individuals globally. Despite its historical association with childhood, dental caries often persists into adulthood with prevalence rates ranging from 60 to 90% in children and 26 to 85% in adults. Currently, there is a dearth of multiepitope vaccines (MEVs) specifically designed to combat S. mutans. To address this gap, we employed an immunoinformatics approach for MEV design, identifying five promising vaccine candidates (PBP2X, PBP2b, MurG, ATP-F, and AGPAT) based on antigenicity and conservation using several tools including CELLO v.2.5, Vaxign, v2.0, ANTIGENpro, and AllerTop v2.0 tools. Subsequent identification of linear B-cell and T-cell epitopes by SVMTrip and NetCTL/NetMHC II tools, respectively, guided the construction of a MEV comprising 10 Cytotoxic T Lymphocyte (CTL) epitopes, 5 Helper T Lymphocyte (HTL) epitopes, and 5 linear B-cell epitopes, interconnected by suitable linkers. The resultant MEV demonstrated high antigenicity, solubility, and structural stability. In silico immune simulations showcased the MEV's potential to elicit robust humoral and cell-mediated immune responses. Molecular docking studies revealed strong interactions between the MEV construct and Toll-Like Receptors (TLRs) and Major Histocompatibility Complex (MHC) molecules. Remarkably, the MEV-TLR-4 complexes exhibited a low energy score, high binding affinity, and a low dissociation constant. The Molecular Dynamic (MD) simulation analysis suggested that MEV-TLR-4 complexes had the highest stability and minimal conformational changes indicating equilibrium within 40 nanosecond time frames. Comprehensive computational analyses strongly support the potential of the proposed MEV to combat dental caries and associated infections. The study's computational assays yielded promising results, but further validation through in vitro and in vivo experiments is needed to assess its efficacy and safety.

Design of an Epitope-Based Vaccine Against MERS-CoV

Background and Objectives: Middle East Respiratory Syndrome (MERS) is a viral respiratory illness caused by a coronavirus called Middle East respiratory syndrome. In the current study, immunoinformatics studies were applied to design an epitope-based vaccine construct against Middle East Respiratory Syndrome. Materials and Methods: In this study, epitopes base vaccine construct was designed against MERS using immunoinformatics approach. Results: In this approach, the targeted proteins were screened, and probable antigenic, non-allergenic, and good water-soluble epitopes were selected for vaccine construction. In vaccine construction, the selected epitopes were joined by GPGPG linkers, and a linear multi-epitope vaccine was constructed. The vaccine construct underwent a physiochemical property analysis. The 3D structure of the vaccine construct was predicted and subjected to refinement. After the refinement, the 3D model was subjected to a molecular docking analysis, TLRs (TLR-3 and TLR-9) were selected as receptors for vaccine construct, and the molecular docking analysis study determined that the vaccine construct has binding ability with the targeted receptor. Conclusions: The docking analysis also unveils that the vaccine construct can properly activate immune system against the target virus however experimental validation is needed to confirm the in silico findings further.

In silico design of Mycobacterium tuberculosis multi-epitope adhesin protein vaccines

Mycobacterium tuberculosis (Mtb) adhesin proteins are promising candidates for subunit vaccine design. Multi-epitope Mtb vaccine and diagnostic candidates were designed using immunoinformatic tools. The antigenic potential of 26 adhesin proteins were determined using VaxiJen 2.0. The truncated heat shock protein 70 (tnHSP70), 19 kDa antigen lipoprotein (lpqH), Mtb curli pili (MTP), and Phosphate transport protein S1 (PstS1) were selected based on the number of known epitopes on the Immune Epitope Database (IEDB). B- and T-cell epitopes were identified using BepiPred2.0, ABCpred, SVMTriP, and IEDB, respectively. Population coverage was analysed using prominent South African specific alleles on the IEDB. The allergenicity, physicochemical characteristics and tertiary structure of the tri-fusion proteins were determined. The in silico immune simulation was performed using C-ImmSim. Three truncated sequences, with predicted B and T cell epitopes, and without allergenicity or signal peptides were linked by three glycine-serine residues, resulting in the stable, hydrophilic molecules, tnlpqH-tnPstS1-tnHSP70 (64,86 kDa) and tnMTP-tnPstS1-tnHSP70 (63,96 kDa). Restriction endonuclease recognition sequences incorporated at the N- and C-terminal ends of each construct, facilitated virtual cloning using Snapgene, into pGEX6P-1, resulting in novel, highly immunogenic vaccine candidates (0,912-0,985). Future studies will involve the cloning, recombinant protein expression and purification of these constructs for downstream applications.

Computational Workflow to Design Novel Vaccine Candidates and Small-Molecule Therapeutics for Schistosomiasis

Human schistosomiasis, caused by the Schistosoma trematode, is a neglected parasitic disease affecting over 250 million people worldwide. There is no vaccine, and the single available drug is threatened by drug resistance. This study presents a computational approach to designing multiepitope vaccines (MEVs) targeting the cercarial (CMEV) and schistosomular (SMEV) stages of schistosomes, and identifies potential schistosomicidal compounds from the Medicine for Malaria Ventures (MMV) and SuperNatural Database (SND) libraries. The designed vaccines (CMEV and SMEV) are engineered to provoke robust immune responses by incorporating a blend of T- and B-cell epitopes. Structural and immunoinformatics evaluations predicted robust interactions of CMEV and SMEV with key immune receptors and prolonged immune responses. In addition, molecular docking identified several compounds from the MMV and SND libraries with strong binding affinities to vital Schistosoma cathepsin proteases, indicating their potential as schistosomicidal agents. Our findings contribute to the potential development of effective vaccines and drugs against schistosomiasis.

Exploring glutathione transferase and Cathepsin L-like proteinase for designing of epitopes-based vaccine against Fasciola hepatica by immunoinformatics and biophysics studies

Fasciolosis is a zoonotic infection and is considered a developing deserted tropical illness threatening ruminant productivity and causing financial losses. Herein, we applied immunoinformatics and biophysics studies to develop an epitopes vaccine against Fasciola hepatica using glutathione transferase and Cathepsin L-like proteinase as possible vaccine candidates. Using the selected proteins, B- and T-cell epitopes were predicted. After epitopes prediction, the epitopes were clarified over immunoinformatics screening, and only five epitopes, EFGRWQQEKCTIDLD, RRNIWEKNVKHIQEH, FKAKYLTEMSRASDI, TDMTFEEFKAKYLTE, and YTAVEGQCR were selected for vaccine construction; selected epitopes were linked with the help of a GPGPG linker and attached with an adjuvant through another linker, EAAAK linker. Cholera toxin B subunit was used as an adjuvant. The ExPASy ProtParam tool server predicted 234 amino acids, 25.86257 kDa molecular weight, 8.54 theoretical pI, 36.86 instability index, and -0.424 grand average of hydropathicity. Molecular docking analysis predicted that the vaccine could activate the immune system against F. hepatica. We calculated negative binding energy values. A biophysics study, likely molecular docking molecular dynamic simulation, further validated the docking results. In molecular dynamic simulation analysis, the top hit docked compounds with the lowest binding energy values were subjected to MD simulation; the simulation analysis showed that the vaccine and immune cell receptors are stable and can activate the immune system. MMGBSA of -146.27 net energy (kcal/mol) was calculated for the vaccine-TLR2 complex, while vaccine-TLR4 of -148.11 net energy (kcal/mol) was estimated. Furthermore, the C-ImmSim bioinformatics tool predicted that the vaccine construct can activate the immune system against F. hepatica, eradicate the infection caused by F. hepatica, and reduce financial losses that need to be spent while protecting against infections of F. hepatica. The computational immune simulation unveils that the vaccine model can activate the immune system against F. hepatica; hence, the experimental scientist can validate the finding accomplished through computational approaches.

Investigation of therapeutic potential of the Il24-p20 fusion protein against breast cancer: an in-silico approach

Targeted delivery of therapeutic anticancer chimeric molecules enhances drug efficacy. Numerous studies have focused on developing novel treatments by employing cytokines, particularly interleukins, to inhibit the growth of cancer cells. In the present study, we fused interleukin 24 with the tumor-targeting peptide P20 through a rigid linker to selectively target cancer cells. The secondary structure, tertiary structure, and physicochemical characteristics of the constructed chimeric IL-24-P20 protein were predicted by using bioinformatics tools. In-silico analysis revealed that the fusion construct has a basic nature with 175 amino acids and a molecular weight of 20 kDa. By using the Rampage and ERRAT2 servers, the validity and quality of the fusion protein were evaluated. The results indicated that 93% of the chimeric proteins contained 90.1% of the residues in the favoured region, resulting in a reliable structure. Finally, docking and simulation studies were conducted via ClusPro and Desmond Schrödinger, respectively. Our results indicate that the constructed fusion protein exhibits excellent quality, interaction capabilities, validity, and stability. These findings suggest that the fusion protein is a promising candidate for targeted cancer therapy.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

Design of an epitope-based peptide vaccine against Cryptococcus neoformans

Cryptococcus neoformans is the highest-ranked fungal pathogen in the Fungal Priority Pathogens List (FPPL) released by the World Health Organization (WHO). In this study, through in silico simulations, a multi-epitope vaccine against Cryptococcus neoformans was developed using the mannoprotein antigen (MP88) as a vaccine candidate. Following the retrieval of the MP88 protein sequences, these were used to predict antigenic B-cell and T-cell epitopes via the bepipred tool and the artificial neural network, respectively. Conserved B-cell epitopes AYSTPA, AYSTPAS, PASSNCK, and DSAYPP were identified as the most promising B-cell epitopes. While YMAADQFCL, VSYEEWMNY, and FQQRYTGTF were identified as the best candidates for CD8+ T-cell epitopes; and YARLLSLNA, ISYGTAMAV, and INQTSYARL were identified as the most promising CD4+ T-cell epitopes. The vaccine construct was modeled along with adjuvant and peptide linkers and the expasy protparam tool was used to predict the physiochemical properties. According to this, the construct vaccine was predicted to be antigenic, nontoxic, nonallergenic, soluble, stable, hydrophilic, and thermostable. Furthermore, the three-dimensional structure was also used in docking analyses with Toll-like receptor (TLR4). Finally, the cDNA of vaccine was successfully cloned into the E. coli pET-28a (+) expression vector. The results presented here could contribute towards the design of an effective vaccine against Cryptococcus neoformans.

An In Silico Design of a Vaccine against All Serotypes of the Dengue Virus Based on Virtual Screening of B-Cell and T-Cell Epitopes

Dengue virus poses a significant global health challenge, particularly in tropical and subtropical regions. Despite the urgent demand for vaccines in the control of the disease, the two approved vaccines, Dengvaxia and TV003/TV005, there are current questions regarding their effectiveness due to an increased risk of antibody-dependent enhancement (ADE) and reduced protection. These challenges have underscored the need for further development of improved vaccines for Dengue Virus. This study presents a new design using an in silico approach to generate a more effective dengue vaccine. Initially, our design process began with the collection of Dengue polyprotein sequences from 10 representative countries worldwide. And then conserved fragments of viral proteins were retrieved as the bases for epitope screening. The selection of epitopes was then carried out with criteria such as antigenicity, immunogenicity, and binding affinity with MHC molecules, while the exclusion criteria were according to their allergenicity, toxicity, and potential for antibody-dependent enhancement. We then constructed a core antigen with the selected epitopes and linked the outcomes with distinct adjuvant proteins, resulting in three candidate vaccines: PSDV-1, PSDV-2, and PSDV-3. Among these, PSDV-2 was selected for further validation due to its superior physicochemical and structural properties. Extensive simulations demonstrated that PSDV-2 exhibited strong binding to pattern recognition receptors, high stability, and robust immune induction, confirming its potential as a high-quality vaccine candidate. For its recombinant expression, a plasmid was subsequently designed. Our new vaccine design offers a promising additional option for Dengue virus protection. Further experimental validations will be conducted to confirm its protective efficacy and safety.

Reverse vaccinology approaches to design a potent multiepitope vaccine against the HIV whole genome: immunoinformatic, bioinformatics, and molecular dynamics approaches

Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico.

A rationally designed antigen elicits protective antibodies against multiple nosocomial Gram-positive pathogens

ESKAPE pathogens are responsible for complicated nosocomial infections worldwide and are often resistant to commonly used antibiotics in clinical settings. Among ESKAPE, vancomycin-resistant Enterococcus faecium (VREfm) and methicillin-resistant Staphylococcus aureus (MRSA) are two important Gram-positive pathogens for which non-antibiotic alternatives are urgently needed. We previously showed that the lipoprotein AdcA of E. faecium elicits opsonic and protective antibodies against E. faecium and E. faecalis. Prompted by our observation, reported here, that AdcA also elicits opsonic antibodies against MRSA and other clinically relevant Gram-positive pathogens, we identified the dominant epitope responsible for AdcA cross-reactive activity and designed a hyper-thermostable and multi-presenting antigen, Sc(EH)3. We demonstrate that antibodies raised against Sc(EH)3 mediate opsonic killing of a wide-spectrum of Gram-positive pathogens, including VREfm and MRSA, and confer protection both in passive and active immunisation models. Our data indicate that Sc(EH)3 is a promising antigen for the development of vaccines against different Gram-positive pathogens.

Advanced vaccinomic, immunoinformatic, and molecular modeling strategies for designing Multi- epitope vaccines against the Enterobacter cloacae complex

The increasing and ongoing issue of antibiotic resistance in bacteria is of huge concern globally, mainly to healthcare facilities. It is now crucial to develop a vaccine for therapeutic and preventive purposes against the bacterial species causing hospital-based infections. Among the many antibiotic- resistant bacterial pathogens, the Enterobacter cloacae complex (ECC) including six species, E. Colcae, E. absuriae, E. kobie, E. hormaechei, E. ludwigii, and E. nimipressuralis, are dangerous to public health and may worsen the situation. Vaccination plays a vital role in the prevention of infections and infectious diseases. This research highlighted the construction and design of a multi-epitope vaccine for the E. cloacae complex by retrieving their complete sequenced proteome. The retrieved proteome was assessed to opt for potential vaccine candidates using immunoinformatic tools. Both B and T-cell epitopes were predicted in order to create both humoral and cellular immunity and further scrutinized for antigenicity, allergenicity, water solubility, and toxicity analysis. The final potential epitopes were subjected to population coverage analysis. Major histocompatibility complex (MHC) class combined, and MHC Class I and II world population coverage was obtained as 99.74%, and 98.55% respectively while a combined 81.81% was covered. A multi-epitope peptide-based vaccine construct consisting of the adjuvant, epitopes, and linkers was subjected to the ProtParam tool to calculate its physiochemical properties. The total amino acids were 236, the molecular weight was 27.64kd, and the vaccine construct was stable with an instability index of 27.01. The Grand Average of Hydropathy (GRAVY) (hydrophilicity) value obtained was -0.659, being more negative and depicting the hydrophilic character. It was non-allergen antigenic with an antigenicity of 0.8913. The vaccine construct was further validated for binding efficacy with immune cell receptors MHC-I, MHC-II, and Toll-like receptor (TLR)-4. The molecular docking results depict that the designed vaccine has good binding potency with immune receptors crucial for antigen presentation and processing. Among the Vaccine-MHC-I, Vaccine-MHC-II, and Vaccine-TLR-4 complexes, the best-docked poses were identified based on their lowest binding energy scores of -886.8, -995.6, and -883.6, respectively. Overall, we observed that the designed vaccine construct can evoke a proper immune response and the construct could help experimental researchers in the formulation of a vaccine against the targeted pathogens.

Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: Prioritizing epitope-based candidates

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

In silico analysis of the interaction of de novo peptides derived from Salvia hispanica with anticancer targetsEvaluation of the anticancer potential of de novo peptides derived from Salvia hispanica through molecular docking

Cancer is one of the leading causes of death worldwide. Conventional cancer therapies are not selective to cancer cells resulting in serious side effects on patients. Thus, the need for complementary treatments that improve the patient's response to cancer therapy is highly important. To predict and evaluate the physicochemical characteristics and potential anticancer activity of the peptides identified from S. hispanica protein fraction <1 kDa through the use of in silico tools. Peptides derived from Salvia hispanica's protein fraction <1 kDa were identified and analyzed for the prediction of their physicochemical properties. The characterized peptide sequences were then submitted to a multi-criteria decision analysis to identify the peptides that possess the characteristics to potentially exert anticancer activity. Through molecular docking analysis, the potential anticancer activity of the Potentially Anticancer Peptide (PAP)-1, PAP-2, PAP-3, PAP-4, and PAP-5 was estimated by their binding interactions with cancer and apoptosis-related molecules. All five evaluated PAPs exhibited strong binding interactions (< -100 kcal/mol). However, PAP-3 showed the lowest binding free energies with several of the targets. Thus, PAP-3 shows potential to be used as a nutraceutical or ingredient for functional foods that adjuvate in cancer treatment. Conclusions: Through the molecular docking studies, the binding of the PAPs to target molecules of interest for cancer treatment was successfully simulated, from which PAP-3 exhibited the lowest binding free energies. Further in vitro and in vivo studies are required to validate the predictions obtained by the in silico analysis.Communicated by Ramaswamy H. Sarma.

Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus

Monkeypox virus (MPXV) is an orthopoxvirus, causing zoonotic infections in humans with smallpox-like symptoms. The WHO reported MPXV cases in May 2022 and the outbreak caused significant morbidity threats to immunocompromised individuals and children. Currently, no clinically validated therapies are available against MPXV infections. The present study is based on immunoinformatics approaches to design mRNA-based novel vaccine models against MPXV. Three proteins were prioritized based on high antigenicity, low allergenicity, and toxicity values to predict T- and B-cell epitopes. Lead T- and B-cell epitopes were used to design vaccine constructs, linked with epitope-specific linkers and adjuvant to enhance immune responses. Additional sequences, including Kozak sequence, MITD sequence, tPA sequence, Goblin 5', 3' UTRs, and a poly(A) tail were added to design stable and highly immunogenic mRNA vaccine construct. High-quality structures were predicted by molecular modeling and 3D-structural validation of the vaccine construct. Population coverage and epitope-conservancy speculated broader protection of designed vaccine model against multiple MPXV infectious strains. MPXV-V4 was eventually prioritized based on its physicochemical and immunological parameters and docking scores. Molecular dynamics and immune simulations analyses predicted significant structural stability and binding affinity of the top-ranked vaccine model with immune receptors to elicit cellular and humoral immunogenic responses against the MPXV. The pursuance of experimental and clinical follow-up of these prioritized constructs may lay the groundwork to develop safe and effective vaccine against MPXV.Communicated by Ramaswamy H. Sarma.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

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.

A novel vaccine construct against Zika virus fever: insights from epitope-based vaccine discovery through molecular modeling and immunoinformatics approaches

The Zika virus (ZIKV) is an emerging virus associated with the Flaviviridae family that mainly causes infection in pregnant women and leads to several abnormalities during pregnancy. This virus has unique properties that may lead to pathological diseases. As the virus has the ability to evade immune response, a crucial effort is required to deal with ZIKV. Vaccines are a safe means to control different pathogenic infectious diseases. In the current research, a multi-epitope-based vaccination against ZIKV is being designed using in silico methods. For the epitope prediction and prioritization phase, ZIKV polyprotein (YP_002790881.1) and flavivirus polyprotein (>YP_009428568.1) were targeted. The predicted B-cell epitopes were used for MHC-I and MHC-II epitope prediction. Afterward, several immunoinformatics filters were applied and nine (REDLWCGSL, MQDLWLLRR, YKKSGITEV, TYTDRRWCF, RDAFPDSNS, KPSLGLINR, ELIGRARVS, AITQGKREE, and EARRSRRAV) epitopes were found to be probably antigenic in nature, non-allergenic, non-toxic, and water soluble without any toxins. Selected epitopes were joined using a particular GPGPG linker to create the base vaccination for epitopes, and an extra EAAAK linker was used to link the adjuvant. A total of 312 amino acids with a molecular weight (MW) of 31.62762 and an instability value of 34.06 were computed in the physicochemical characteristic analysis, indicating that the vaccine design is stable. The molecular docking analysis predicted a binding energy of -329.46 (kcal/mol) for TLR-3 and -358.54 (kcal/mol) for TLR-2. Moreover, the molecular dynamics simulation analysis predicted that the vaccine and receptor molecules have stable binding interactions in a dynamic environment. The C-immune simulation analysis predicted that the vaccine has the ability to generate both humoral and cellular immune responses. Based on the design, the vaccine construct has the best efficacy to evoke immune response in theory, but experimental analysis is required to validate the in silico base approach and ensure its safety.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features

Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

Computational design of novel chimeric multiepitope vaccine against bacterial and viral disease in tilapia (Oreochromis sp.)

Regarding several infectious diseases in fish, multiple vaccinations are not favorable. The chimeric multiepitope vaccine (CMEV) harboring several antigens for multi-disease prevention would enhance vaccine efficiency in terms of multiple disease prevention. Herein, the immunogens of tilapia's seven pathogens including E. tarda, F. columnare, F. noatunensis, S. iniae, S. agalactiae, A. hydrophila, and TiLV were used for CMEV design. After shuffling and annotating the B-cell epitopes, 5,040 CMEV primary protein structures were obtained. Secondary and tertiary protein structures were predicted by AlphaFold2 creating 25,200 CMEV. Proper amino acid alignment in the secondary structures was achieved by the Ramachandran plot. In silico determination of physiochemical and other properties including allergenicity, antigenicity, glycosylation, and conformational B-cell epitopes were determined. The selected CMEV (OSLM0467, OSLM2629, and OSLM4294) showed a predicted molecular weight (MW) of 70 kDa, with feasible sites of N- and O-glycosylation, and a number of potentially conformational B-cell epitope residues. Molecular docking, codon optimization, and in-silico cloning were tested to evaluate the possibility of protein expression. Those CMEVs will further elucidate in vitro and in vivo to evaluate the efficacy and specific immune response. This research will highlight the new era of vaccines designed based on in silico structural vaccine design.

Evaluation of humoral and cellular immune responses against Vibrio cholerae using oral immunization by multi-epitope-phage-based vaccine

INTRODUCTION

Cholera is a severe gastrointestinal disease that manifests with rapid onset of diarrhea, vomiting, and high mortality rates. Due to its widespread occurrence in impoverished communities with poor water sanitation, there is an urgent demand for a cost-effective and highly efficient vaccine. Multi-epitope vaccines containing dominant immunological epitopes and adjuvant compounds have demonstrated potential in boosting the immune response.

MATERIAL AND METHODS

B and T epitopes of OMPU, OMPW, TCPA, CTXA, and CTXB proteins were predicted using bioinformatics methods. Subsequently, highly antigenic multi-epitopes that are non-allergenic and non-toxic were synthesized. These multi-epitopes were then cloned into the pCOMB phagemid. A plasmid M13KO7ΔpIII containing all helper phage proteins except pIII was created to produce the recombinant phage. Female Balb/c mice were divided into three groups and immunized accordingly. The mice received the helper phage, recombinant phage or PBS via gavage feeding thrice within two weeks. Serum samples were collected before and after immunization for the ELISA test as well as evaluating immune system induction through ELISpot testing of spleen lymphocytes.

RESULTS

The titer of the recombinant phage was determined to be 1011 PFU/ml. The presence of the recombinant phage was confirmed through differences in optical density between sample and control groups in the ELISA phage technique, as well as by observing transduction activity, which demonstrated successful production of a recombinant phage displaying the Vibrio multi-epitope on M13 phage pIII. ELISA results revealed significant differences in phage antibodies before and after inoculation, particularly notable in the negative control mice. Mice treated with multi-epitope phages exhibited antibodies against Vibrio cholerae lysate. Additionally, ELISpot results indicated activation of cellular immunity in mice receiving both Vibrio and helper phage.

CONCLUSION

This study emphasizes the potential of multi-epitope on phage to enhance both cellular and humoral immunity in mice, demonstrating how phages can be used as adjuvants to stimulate mucosal immunity and act as promising candidates for oral vaccination.

From proteome to candidate vaccines: target discovery and molecular dynamics-guided multi-epitope vaccine engineering against kissing bug

Introduction

Trypanosoma cruzi is a protozoan parasite that causes the tropical ailment known as Chagas disease, which has its origins in South America. Globally, it has a major impact on health and is transported by insect vector that serves as a parasite. Given the scarcity of vaccines and the limited treatment choices, we conducted a comprehensive investigation of core proteomics to explore a potential reverse vaccine candidate with high antigenicity.

Methods

To identify the immunodominant epitopes, T. cruzi core proteomics was initially explored. Consequently, the vaccine sequence was engineered to possess characteristics of non-allergenicity, antigenicity, immunogenicity, and enhanced solubility. After modeling the tertiary structure of the human TLR4 receptor, the binding affinities were assessed employing molecular docking and molecular dynamics simulations (MDS).

Results

Docking of the final vaccine design with TLR4 receptors revealed substantial hydrogen bond interactions. A server-based methodology for immunological simulation was developed to forecast the effectiveness against antibodies (IgM + IgG) and interferons (IFN-g). The MDS analysis revealed notable levels of structural compactness and binding stability with average RMSD of 5.03 Aring;, beta-factor 1.09e+5 Å, Rg is 44.7 Aring; and RMSF of 49.50 Aring;. This is followed by binding free energies calculation. The system stability was compromised by the complexes, as evidenced by their corresponding Gibbs free energies of -54.6 kcal/mol.

Discussion

Subtractive proteomics approach was applied to determine the antigenic regions of the T cruzi. Our study utilized computational techniques to identify B- and T-cell epitopes in the T. cruzi core proteome. In current study the developed vaccine candidate exhibits immunodominant features. Our findings suggest that formulating a vaccine targeting the causative agent of Chagas disease should be the initial step in its development.

An Immunoinformatic Approach to Designing a Multi-epitope Vaccine against Helicobacter pylori with the VacA Toxin and BabA Adhesion

Objective:

Helicobacter pylori, as a carcinogen, has been related to the development of gastric cancer, particularly in developing countries. The main challenge with therapy is the recurrence of antibiotic-resistant bacteria, and vaccination is still a problem. Therefore, the objective of the current study was to rationally design a multi-epitope vaccine using two immunogenic proteins found in H. pylori.

Methods:

Promising epitopes for the Leb-binding adhesin A (BabA) and vacuolating cytotoxin (VacA) proteins were characterized through an immunoinformatics approach. Epitope-rich fragments were selected based on high-binding affinities with HLA classes I and II to be specifically presented to B and T lymphocytes and to selectively elicit both humoral and cellular immune responses.

Results:

Six constructs were planned by fusing these fragments in different arrangements with the help of GPGPG linkers. The most stable three-dimensional structure was found in Construct 6 during molecular dynamics. To improve immunogenicity and stability, an adjuvant called human β- defensin 2 (hBD-2) was joined to the N-terminus of Construct 6. Following molecular docking, the final vaccine reacted appropriately with each toll-like receptor 2 (TLR-2), TLR3, and TLR-4. The final DNA sequence was optimized for expression in E. coli K12 and in silico cloned into a pET-28a(+) plasmid. As a result of the vaccination in silico, substantial responses were developed against H. pylori.

Conclusion:

According to the immune response simulation, activated B and T lymphocytes and memory cell production increased. Macrophages and dendritic cells proliferated continuously, and IFN-γ and Cytokines, such as IL-2 were raised.

Systems approach to design multi-epitopic peptide vaccine candidate against fowl adenovirus structural proteins for Gallus gallus domesticus

Introduction

Fowl adenovirus (FAdV) is a significant pathogen in poultry, causing various diseases such as hepatitis-hydropericardium, inclusion body hepatitis, and gizzard erosion. Different serotypes of FAdV are associated with specific conditions, highlighting the need for targeted prevention strategies. Given the rising prevalence of FAdV-related diseases globally, effective vaccination and biosecurity measures are crucial. In this study, we explore the potential of structural proteins to design a multi-epitope vaccine targeting FAdV.

Methods

We employed an in silico approach to design the multi-epitope vaccine. Essential viral structural proteins, including hexon, penton, and fiber protein, were selected as vaccine targets. T-cell and B-cell epitopes binding to MHC-I and MHC-II molecules were predicted using computational methods. Molecular docking studies were conducted to validate the interaction of the multi-epitope vaccine candidate with chicken Toll-like receptors 2 and 5.

Results

Our in silico methodology successfully identified potential T-cell and B-cell epitopes within the selected viral structural proteins. Molecular docking studies revealed strong interactions between the multi-epitope vaccine candidate and chicken Toll-like receptors 2 and 5, indicating the structural integrity and immunogenic potential of the designed vaccine.

Discussion

The designed multi-epitope vaccine presents a promising approach for combating FAdV infections in chickens. By targeting essential viral structural proteins, the vaccine is expected to induce a robust immunological response. The in silico methodology utilized in this study provides a rapid and cost-effective means of vaccine design, offering insights into potential vaccine candidates before experimental validation. Future studies should focus on in vitro and in vivo evaluations to further assess the efficacy and safety of the proposed vaccine.

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.

Lactobacilli-derived adjuvants combined with immunoinformatics-driven multi-epitope antigens based approach protects against Clostridium perfringens in a mouse model

Clostridium perfringens is ubiquitously distributed and capable of secreting toxins, posing a significant threat to animal health. Infections caused by Clostridium perfringens, such as Necrotic Enteritis (NE), result in substantial economic losses to the livestock industry annually. However, there is no effective commercial vaccine available. Hence, we set out to propose an effective approach for multi-epitope subunit vaccine construction utilizing biomolecules. We utilized immunoinformatics to design a novel multi-epitope antigen against C. perfringens (CPMEA). Furthermore, we innovated novel bacterium-like particles (BLPs) through thermal acid treatment of various Lactobacillus strains and selected BLP23017 among them. Then, we detailed the structure of CPMEA and BLPs and utilized them to prepare a multi-epitope vaccine. Here, we showed that our vaccine provided full protection against C. perfringens infection after a single dose in a mouse model. Additionally, BLP23017 notably augmented the secretion of secretory immunoglobulin A (sIgA) and enhanced antibody production. We conclude that our vaccine possess safety and high efficacy, making it an excellent candidate for preventing C. perfringens infection. Moreover, we demonstrate our approach to vaccine construction and the preparation of BLP23017 with distinct advantages may contribute to the prevention of a wider array of diseases and the novel vaccine development.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

A computational quest for identifying potential vaccine candidates against Moraxella lacunata: a multi-pronged approach

Moraxella lacunata is an emerging gram-negative bacterium that is responsible for multiple nosocomial infections. The bacterium is evolving resistance to several antibiotics, and currently, no effective licensed vaccines are available, which warrants the search for new therapeutics. A multi-epitope-based vaccine has been designed for M. lacunata. The complete proteome of M. lacunata contains 10,110 core proteins. Subcellular localization analysis revealed the presence of five proteins in the extracellular matrix, while 19 proteins were predicted to be located in the outer membrane, and 21 proteins were predicted to be located in the periplasmic region. Only two proteins, the type VI secretion system tube protein (Hcp) and the transporter substrate-binding domain-containing protein, were selected for epitope prediction as they fulfilled all the criteria for being potential vaccine candidates. Shortlisted epitopes from the selected proteins were fused together using "GPGPG" linkers to overcome the limitations of single-epitope vaccines. Next, the cholera toxin-B adjuvant was attached to the peptide epitope using an EAAAK linker. Docking analysis was performed to examine the interaction between the vaccine and immune cell receptors, revealing robust intermolecular interactions and a stable binding conformation. Molecular dynamics simulation findings revealed no drastic changes in the binding conformation of complexes during the simulation period. The net binding free energy of vaccine-receptor complexes was estimated using the molecular mechanics energies combined with the Poisson-Boltzmann and surface area continuum solvation (MM-PBSA) method. The reported values were -586.38 kcal/mol, -283.74 kcal/mol, and -296.88 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. Furthermore, the molecular mechanics energies combined with the generalized Born and surface area continuum solvation (MM-GBSA) analysis predicted binding free energies of -596.69 kcal/mol, -287.39 kcal/mol, and -298.28 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. The theoretical vaccine design proposed in the study could potentially serve as a powerful therapeutic against targeted pathogens, subject to validation through experimental studies.Communicated by Ramaswamy H. Sarma.

Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets

Pancreatic cancer is a highly aggressive and often lethal malignancy with limited treatment options. Its late-stage diagnosis and resistance to conventional therapies make it a significant challenge in oncology. Immunotherapy, particularly cancer vaccines, has emerged as a promising avenue for treating pancreatic cancer. Multi-epitope vaccines, designed to target multiple epitopes derived from various antigens associated with pancreatic cancer, have gained attention as potential candidates for improving therapeutic outcomes. In this study, we have explored transcriptomics and protein expression databases to identify potential upregulated proteins in pancreatic cancer cells. After examining a total of 21,054 proteins from various databases, it was discovered that 143 proteins expressed differently in malignant and healthy cells. The CTL, HTL and BCE epitopes were predicted for the shortlisted proteins. 51,840 vaccine constructs were created by concatenating CTL, HTL, and B-cell epitopes in the respective sequences. The best 86 structures were selected from a set of 51,840 designs after they were analyzed for vaxijenicity, allergenicity, toxicity, and antigenicity scores. In further simulation of the immune response using constructs, it was found that 41417, 37961, and 40841 constructs could produce a strong immune response when injected. Further, it was found that construct 37961 showed stronger interaction and stability with TLR-9 as determined from the large-scale molecular dynamics simulations. Moreover, the 37961 construct has shown interactions with TLR-9 suggests its potential in inducing immune response. In addition, construct 37961 has shown 100% predicted solubility in the E. coli expression system. Overall, the study indicates the designed construct 37961 has the potential to induce an anti-tumor immune response and long-standing protection pending further studies.

Leveraging the immunoinformatics approach for designing the SARS-CoV-2 omicron-specific antigenic cassette of mRNA vaccine

Emergence of SARS-CoV-2 Omicron variant has presented a significant challenge to global health, demanding rapid development of mRNA-based vaccines. The mRNA-guided vaccine platforms offer various advantages over traditional vaccine platforms. The mRNA by nature is a short-lived molecule that guides the cells to manufacture antigenic proteins. In the present work, we have created an omicron spike antigenic protein sequence characterized by base composition analysis, modeling, and docking with the ACE-2 receptor. Further, we predicted the B-cell and T-cell epitopes followed by antigenicity, toxicity, and allergenicity. Finally, the protein was reverse translated, codon-optimized, and encoding mRNA sequence was checked for its stability by predicting the secondary structures. A comprehensive examination of in-silico data revealed 628.2 as a potent antigenic candidate that was finally used in Gemcovac®-OM, a heterologous booster mRNA vaccine for COVID-19.

Designing, cloning and simulation studies of cancer/testis antigens based multi-epitope vaccine candidates against cutaneous melanoma: An immunoinformatics approach

Background

Melanoma is the most fatal kind of skin cancer. Among its various types, cutaneous melanoma is the most prevalent one. Melanoma cells are thought to be highly immunogenic due to the presence of distinct tumor-associated antigens (TAAs), which includes carcinoembryonic antigen (CEA), cancer/testis antigens (CTAs) and neo-antigens. The CTA family is a group of antigens that are only expressed in malignancies and testicular germ cells.

Methods

We used integrative framework and systems-level analysis to predict potential vaccine candidates for cutaneous melanoma involving epitopes prediction, molecular modeling and molecular docking to cross-validate the binding affinity and interaction between potential vaccine agents and major histocompatibility molecules (MHCs) followed by molecular dynamics simulation, immune simulation and in silico cloning.

Results

In this study, three cancer/testis antigens were targeted for immunotherapy of cutaneous melanoma. Among many CTAs that were studied for their expression in primary and malignant melanoma, NY-ESO-1, MAGE1 and SSX2 antigens are most prevalent in cutaneous melanoma. Cytotoxic and Helper epitopes were predicted, and the finest epitopes were shortlisted based on binding score. The vaccine construct was composed of the four epitope-rich domains of antigenic proteins, an appropriate adjuvant, His tag and linkers. This potential multi-epitope vaccine was further evaluated in terms of antigenicity, allergencity, toxicity and other physicochemical properties. Molecular interaction estimated through protein-protein docking unveiled good interactions characterized by favorable binding energies. Molecular dynamics simulation ensured the stability of docked complex and the predicted immune response through immune simulation revealed elevated levels of antibodies titer, cytokines, interleukins and immune cells (NK, DC and MA) population.

Conclusion

The findings indicate that the potential vaccine candidates could be effective immunotherapeutic agents that modify the treatment strategies of cutaneous melanoma.

Peptide-ligand conjugate based immunotherapeutic approach for targeted dismissal of non-structural protein 1 of dengue virus: A novel therapeutic solution for mild and severe dengue infections

Dengue virus infection has significantly increased, with reported cases soaring from 505,430 in 2000 to 2,809,818 in 2022, emphasizing the need for effective treatments. Among the eleven structural and non-structural proteins of DENV, Non-structural protein 1 (NS1) has emerged as a promising target due to its diverse role in modulating the immune response, inducing vascular leakage, and facilitating viral replication and assembly. Monoclonal antibodies are the sole therapeutics to target NS1, but concerns about their cross-reactivity persist. Given these concerns, our study focuses on designing a novel Peptide Ligand Conjugate (PLC) as a potential alternative immunotherapeutic agent against NS1. This PLC aims to mediate the immune elimination of soluble NS1 and NS1-presenting DENV-infected host cells by pre-existing vaccine-induced immunity. By employing the High Throughput Virtual Screening (HTVS) method, QikProp analysis, and Molecular Dynamics studies, we identified three hits from Asinex Biodesigned Ligands out of 220,177 compounds that show strong binding affinity towards the monoclonal binding site of NS1 protein. After a rigorous analysis of physicochemical characteristics, antigenicity, allergenicity, and toxicity using various servers, we selected two peptides: the minimum epitopic region of the Diphtheria and Tetanus toxins as the peptide components of the PLCs. A non-cleavable, non-reactive oxime linker connected the ligand with the peptide through oxime and amide bonds. DPT vaccine is widely used in dengue-endemic countries, and it has been reported that antibodies titer against MER of Diphtheria toxin and Tetanus toxins persist lifelong in DPT-vaccinated people. Therefore, once the rationally designed PLCs bind to NS1 through the ligands, the peptide will induce an immune response against NS1 by triggering pre-existing DPT antibodies and activating memory cells. This orchestrated immune response will destroy soluble NS1 and NS1-expressing DENV-infected cells, thereby reducing the illness of severe dengue hemorrhagic fever and the DENV infection, respectively. Given the increasing demand for new therapeutics for DENV treatment, further investigation into this novel immune-therapeutic strategy may offer a new avenue for treating mild and severe dengue infections.

Development of a novel multi‑epitope vaccine against the pathogenic human polyomavirus V6/7 using reverse vaccinology

BACKGROUND

Human polyomaviruses contribute to human oncogenesis through persistent infections, but currently there is no effective preventive measure against the malignancies caused by this virus. Therefore, the development of a safe and effective vaccine against HPyV is of high priority.

METHODS

First, the proteomes of 2 polyomavirus species (HPyV6 and HPyV7) were downloaded from the NCBI database for the selection of the target proteins. The epitope identification process focused on selecting proteins that were crucial, associated with virulence, present on the surface, antigenic, non-toxic, and non-homologous with the human proteome. Then, the immunoinformatic methods were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes from the target antigens, which could be used to create epitope-based vaccine. The physicochemical features of the designed vaccine were predicted through various online servers. The binding pattern and stability between the vaccine candidate and Toll-like receptors were analyzed through molecular docking and molecular dynamics (MD) simulation, while the immunogenicity of the designed vaccines was assessed using immune simulation.

RESULTS

Online tools were utilized to forecast the most optimal epitope from the immunogenic targets, including LTAg, VP1, and VP1 antigens of HPyV6 and HPyV7. A multi-epitope vaccine was developed by combining 10 CTL, 7 HTL, and 6 LBL epitopes with suitable linkers and adjuvant. The vaccine displayed 98.35% of the world's population coverage. The 3D model of the vaccine structure revealed that the majority of residues (87.7%) were located in favored regions of the Ramachandran plot. The evaluation of molecular docking and MD simulation revealed that the constructed vaccine exhibits a strong binding (-1414.0 kcal/mol) towards the host's TLR4. Moreover, the vaccine-TLR complexes remained stable throughout the dynamic conditions present in the natural environment. The immune simulation results demonstrated that the vaccine design had the capacity to elicit robust immune responses in the host.

CONCLUSION

The multi-parametric analysis revealed that the designed vaccine is capable of inducing sustained immunity against the selected polyomaviruses, although further in-vivo investigations are needed to verify its effectiveness.

In silico designing a novel TLR4-mediating multiepitope vaccine against monkeypox via advanced immunoinformatics and bioinformatics approaches

Monkeypox virus is a member of the Poxviridae family, which causes monkeypox zoonotic disease. Since July 2022, the prevention of monkeypox have become more considerable due to the new outbreak, making it a global concern. Therefore, we used an in silico-based method, including immunoinformatics, bioinformatics, molecular docking, and gene cloning approaches to design a novel multiepitope vaccine against monkeypox. Three immunogenic envelope proteins of monkeypox virus, including G10R, E8L, and A30L, were selected to predict appropriate immune system stimulator epitopes. The A30L is common between smallpox and monkeypox virus, so the proposed vaccine may be effective against smallpox too. There is no evidence of allergenicity and toxicity of the vaccine epitopes. To boost the immunogenicity of the designed vaccine, we used the helper epitope of PADRE and RS01as adjuvants. Furthermore, some linkers are used to link epitopes and adjuvants together. The physicochemical futures of the designed vaccine were assessed. The tertiary structure of the vaccine was modeled and then refined to improve its structure and physicochemical properties. To analyze the vaccine construct and TLR4 complex affinity, they were docked to gather. Besides, the vaccine was cloned into E.coli. pET-21b(+) plasmid to reveal that it can be expressed and stimulate the immune system. Immune stimulation evaluation showed that the candidate vaccine could induce the production of IgM, IgG1, and IgG2 antibodies. Overall, we suggested an effective vaccine candidate against monkeypox. However, Future studies and clinical trials should be done to ensure the efficacy and safety of this vaccine.Communicated by Ramaswamy H. Sarma.

Self-assembled peptide/polymer hybrid nanoplatform for cancer immunostimulating therapies

Integrating peptide epitopes in self-assembling materials is a successful strategy to obtain nanovaccines with high antigen density and improved efficacy. In this study, self-assembling peptides containing MAGE-A3/PADRE epitopes were designed to generate functional therapeutic nanovaccines. To achieve higher stability, peptide/polymer hybrid nanoparticles were formulated by controlled self-assembly of the engineered peptides. The nanoparticles showed good biocompatibility to both human red blood- and dendritic cells. Incubation of the nanoparticles with immature dendritic cells triggered immune effects that ultimately activated CD8 + cells. The antigen-specific and IgG antibody responses of healthy C57BL/6 mice vaccinated with the nanoparticles were analyzed. The in vivo results indicate a specific response to the nanovaccines, mainly mediated through a cellular pathway. This research indicates that the immunogenicity of peptide epitope vaccines can be effectively enhanced by developing self-assembled peptide-polymer hybrid nanostructures.

Identification and prioritisation of potential vaccine candidates using subtractive proteomics and designing of a multi-epitope vaccine against Wuchereria bancrofti

This study employed subtractive proteomics and immunoinformatics to analyze the Wuchereria bancrofti proteome and identify potential therapeutic targets, with a focus on designing a vaccine against the parasite species. A comprehensive bioinformatics analysis of the parasite's proteome identified 51 probable therapeutic targets, among which "Kunitz/bovine pancreatic trypsin inhibitor domain-containing protein" was identified as the most promising vaccine candidate. The candidate protein was used to design a multi-epitope vaccine, incorporating B-cell and T-cell epitopes identified through various tools. The vaccine construct underwent extensive analysis of its antigenic, physical, and chemical features, including the determination of secondary and tertiary structures. Docking and molecular dynamics simulations were performed with HLA alleles, Toll-like receptor 4 (TLR4), and TLR3 to assess its potential to elicit the human immune response. Immune simulation analysis confirmed the predicted vaccine's strong binding affinity with immunoglobulins, indicating its potential efficacy in generating an immune response. However, experimental validation and testing of this multi-epitope vaccine construct would be needed to assess its potential against W. bancrofti and even for a broader range of lymphatic filarial infections given the similarities between W. bancrofti and Brugia.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.