Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens.

While computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research.

Identification of Immunodominant Epitopes of Dengue Virus 2 Envelope and NS1 Proteins: Evaluating the Diagnostic Potential of a Synthetic Peptide.

BACKGROUND AND OBJECTIVE: Dengue is a major infectious disease with potential for outbreaks and epidemics. A specific and sensitive diagnosis is a prerequisite for clinical management of the disease. We designed our study to identify epitopes on the Dengue virus (DENV) envelope (E) and non-structural protein 1 (NS1) with potential for diagnosis. METHODS: Serology and immunoinformatic approaches were employed. We collected DENV-positive, DENV-negative and Japanese encephalitis virus-positive samples from collaborating hospitals in 2019 and 2022-2023. Seropositive peptides in 15-18 mer peptide arrays of E and NS1 proteins of DENV2 were determined by an indirect enzyme-linked immunosorbent assay. B-cell linear and conformational epitopes were predicted using BepiPred2.0 and ElliPro, respectively. A consensus recombinant peptide was designed, synthesised and evaluated for its diagnostic potential using patient sera. RESULTS: Eight peptides of E protein and six peptides of NS1 protein were identified to be the most frequently recognised by Dengue-positive patients. These peptide sequences were compared with B-cell epitope regions and found to be overlapped with predicted B-cell linear and conformational epitopes. EP11 and NSP15 showed a 100% amino acid sequence overlap with B-cell epitopes. EP1 and NSP15 had 14 whereas EP28, EP31, EP60 16, NSP12 and NSP32 had more than 15 interacting interface residues with a neutralising antibody, suggesting a strength of interaction. Interestingly, potential epitopes identified were localised on the surface of proteins as visualised by PyMOL. Validation with a recombined synthetic peptide yielded 92.3% sensitivity and 91.42% specificity. CONCLUSIONS: Immunodominant regions identified by serology and computationally predicted epitopes overlapped, thereby showing the robustness of the methodology and the peptide designed for diagnosis.

Predicting immune response targets in orthoflaviviruses through sequence homology and computational analysis.

CONTEXT: Flaviviruses cause severe encephalitic or hemorrhagic diseases in humans. Its members, Kyasanur forest disease virus (KFDV) and Alkhumra hemorrhagic fever virus (ALKV), cause hemorrhagic fever and are prevalent in India and Saudi Arabia, respectively, while the tick-borne encephalitis virus (TBEV) causes a dangerous encephalitic infection in Europe and Asia. However, little information is available about the targets of immune responses for these deadly viruses. Here, we predict potential antigenic peptide epitopes of viral envelope protein for inducing a cell-mediated and humoral immune response. METHODS: Using the Immune Epitope Database and Analysis Resource (IEDB-AR), we identified 13 MHC-I and two MHC-II dominant conserved epitopes in KFDV and ALKV and six MHC-I and three MHC-II epitopes in TBEV envelope proteins. Parallelly, we also predicted B-cell linear and discontinuous envelope protein epitopes for these viruses. Interestingly, the epitopes are conserved in all three viral envelope proteins. Further, the discontinuous epitopes are structurally compared with the available DENV, ZIKV, WNV, TBEV, and LIV envelope protein antibody structures. Overall structural comparison analyses highlight (i) lateral ridge epitope in the ED-III domain of E protein, and (ii) envelope dimer epitope (EDE) could be targeted for developing potent vaccine candidates as well as therapeutic antibody production. Moreover, existing structural and biochemical functions of the same epitopes in homologous viruses are predicted to have a reduced antibody-dependent enhancement (ADE) effect on flaviviral infection.

A composite enzyme derived from papain and chymotrypsin reduces the Allergenicity of Cow's Milk allergen casein by targeting T and B cell epitopes.

Casein, the major allergen in cow's milk, presents a significant challenge in providing nutritional support for children with allergies. To address this issue, we investigated a composite enzyme, comprising papain and chymotrypsin, to reduce the allergenicity of casein. Enzymatic hydrolysis induced substantial structural changes in casein, diminishing its affinity for specific IgE and IgG antibodies. Additionally, in a BALB/c mouse model, casein hydrolysate alleviated allergic symptoms, evidenced by lower serum IgE and IgG levels, reduced plasma histamine, and decreased Th2 cytokine release during cell co-culture. Peptidomic analysis revealed a 52.38% and 60% reduction in peptides containing IgE epitopes in casein hydrolyzed by the composite enzyme compared to papain and chymotrypsin, respectively, along with a notable absence of previously reported T cell epitopes. These results demonstrate the potential of enzyme combinations to enhance the efficiency of epitope destruction in allergenic proteins, providing valuable insights into the development of hypoallergenic dairy products.

Designing a Conserved Immunogenic Peptide Construct from the Nucleocapsid Protein of Puumala orthohantavirus.

Puumala orthohantavirus (PUUV) is an emerging zoonotic virus endemic to Europe and Russia that causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome (HFRS). There are limited options for treatment and diagnosis of orthohantavirus infection, making the search for potential immunogenic candidates crucial. In the present work, various bioinformatics tools were employed to design conserved immunogenic peptides containing multiple epitopes of PUUV nucleocapsid protein. Eleven conserved peptides (90% conservancy) of the PUUV nucleocapsid protein were identified. Three conserved peptides containing multiple T and B cell epitopes were selected using a consensus epitope prediction algorithm. Molecular docking using the HPEP dock server demonstrated strong binding interactions between the epitopes and HLA molecules (ten alleles for each class I and II HLA). Moreover, an analysis of population coverage using the IEDB database revealed that the identified peptides have over 90% average population coverage across six continents. Molecular docking and simulation analysis reveal a stable interaction with peptide constructs of chosen immunogenic peptides and Toll-like receptor-4. These computational analyses demonstrate selected peptides' immunogenic potential, which needs to be validated in different experimental systems.

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.

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT).

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

Identification and Validation of Peptides Specifying SARS-CoV-2 B-Cell Epitopes Eliciting Neutralizing Antibodies.

Vaccination is an effective means of inducing immune protection to prevent transmissible diseases. During the Covid-19 pandemic, immunizations using traditional and novel vaccine platforms such as the inactivated SARSCo-V-2 vaccine, adenoviral-vectored, and nucleic acid-based mRNA vaccines have been relatively successful in controlling the rates of infection and hospitalizations. Nevertheless, the danger posed by the emergence of SARS-CoV-2 variants would set the stage for the design of next generation vaccines. To overcome the lack of efficacy of current vaccines against emerging SARS-CoV-2 variants, new vaccines must be able to overcome the reduced effectiveness of the current vaccines. Since the current Covid-19 vaccines are dependent on the whole S-protein of Wuhan strain as the antigen, mutations have rendered the current Covid-19 vaccines less effective against variants of concern (VoCs). Instead of using the whole S-protein, peptide-based epitopes could be predicted using immunoinformatic approaches, simulation of the 3D structures, overlapping peptides covering the whole length of the S-protein or peptide arrays based on synthetic peptide combinatorial libraries comprising peptides recognizable by monoclonal antibodies. B-cell epitopes were predicted, and immunogenicity of peptides was validated in mice by immunizing mice with peptides conjugated to keyhole limpet hemocyanin (KLH) mixed with Montanide 51 as an adjuvant. The immunogenicity of epitopes that could elicit peptide specific IgGs was determined by peptide-based ELISA. Neutralizing activities were determined by cPass and pseudovirus-based neutralization assays.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

In Silico Tools for Predicting Novel Epitopes.

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Designing a smallpox B-cell and T-cell multi-epitope subunit vaccine using a comprehensive immunoinformatics approach.

Smallpox is a highly contagious human disease caused by the variola virus. Although the disease was eliminated in 1979 due to its highly contagious nature and historical pathogenicity, with a mortality rate of up to 30%, this virus is an important candidate for biological weapons. Currently, vaccines are the critical measures to prevent this virus infection and spread. In this study, we designed a peptide vaccine using immunoinformatics tools, which have the potential to activate human immunity against variola virus infection efficiently. The design of peptides derives from vaccine-candidate proteins showing protective potential in vaccinia WR strains. Potential non-toxic and nonallergenic T-cell and B-cell binding and cytokine-inducing epitopes were then screened through a priority prediction using special linkers to connect B-cell epitopes and T-cell epitopes, and an appropriate adjuvant was added to the vaccine construction to enhance the immunogenicity of the peptide vaccine. The 3D structure display, docking, and free energy calculation analysis indicate that the binding affinity between the vaccine peptide and Toll-like receptor 3 is high, and the vaccine receptor complex is highly stable. Notably, the vaccine we designed is obtained from the protective protein of the vaccinia and combined with preventive measures to avoid side effects. This vaccine is highly likely to produce an effective and safe immune response against the variola virus infection in the body. IMPORTANCE: In this work, we designed a vaccine with a cluster of multiple T-cell/B-cell epitopes, which should be effective in inducing systematic immune responses against variola virus infection. Besides, this work also provides a reference in vaccine design for preventing monkeypox virus infection, which is currently prevalent.

SEMA 2.0: web-platform for B-cell conformational epitopes prediction using artificial intelligence.

Prediction of conformational B-cell epitopes is a crucial task in vaccine design and development. In this work, we have developed SEMA 2.0, a user-friendly web platform that enables the research community to tackle the B-cell epitopes prediction problem using state-of-the-art protein language models. SEMA 2.0 offers comprehensive research tools for sequence- and structure-based conformational B-cell epitopes prediction, accurate identification of N-glycosylation sites, and a distinctive module for comparing the structures of antigen B-cell epitopes enhancing our ability to analyze and understand its immunogenic properties. SEMA 2.0 website https://sema.airi.net is free and open to all users and there is no login requirement. Source code is available at https://github.com/AIRI-Institute/SEMAi.

A Structural In Silico Analysis of the Immunogenicity of L-Asparaginase from Penicillium cerradense.

L-asparaginase is an essential drug used to treat acute lymphoid leukemia (ALL), a cancer of high prevalence in children. Several adverse reactions associated with L-asparaginase have been observed, mainly caused by immunogenicity and allergenicity. Some strategies have been adopted, such as searching for new microorganisms that produce the enzyme and applying protein engineering. Therefore, this work aimed to elucidate the molecular structure and predict the immunogenic profile of L-asparaginase from Penicillium cerradense, recently revealed as a new fungus of the genus Penicillium and producer of the enzyme, as a motivation to search for alternatives to bacterial L-asparaginase. In the evolutionary relationship, L-asparaginase from P. cerradense closely matches Aspergillus species. Using in silico tools, we characterized the enzyme as a protein fragment of 378 amino acids (39 kDa), including a signal peptide containing 17 amino acids, and the isoelectric point at 5.13. The oligomeric state was predicted to be a homotetramer. Also, this L-asparaginase presented a similar immunogenicity response (T- and B-cell epitopes) compared to Escherichia coli and Dickeya chrysanthemi enzymes. These results suggest a potentially useful L-asparaginase, with insights that can drive strategies to improve enzyme production.

In-silico formulation of a next-generation polyvalent vaccine against multiple strains of monkeypox virus and other related poxviruses.

Mpox (formerly known as monkeypox) virus and some related poxviruses including smallpox virus pose a significant threat to public health, and effective prevention and treatment strategies are needed. This study utilized a reverse vaccinology approach to retrieve conserved epitopes for monkeypox virus and construct a vaccine that could provide cross-protection against related viruses with similar antigenic properties. The selected virulent proteins of monkeypox virus, MPXVgp165, and Virion core protein P4a, were subjected to epitope mapping for vaccine construction. Two vaccines were constructed using selected T cell epitopes and B cell epitopes with PADRE and human beta-defensins adjuvants conjugated in the vaccine sequence. Both constructs were found to be highly antigenic, non-allergenic, nontoxic, and soluble, suggesting their potential to generate an adequate immune response and be safe for humans. Vaccine construct 1 was selected for molecular dynamic simulation studies. The simulation studies revealed that the TLR8-vaccine complex was more stable than the TLR3-vaccine complex. The lower RMSD and RMSF values of the TLR8 bound vaccine compared to the TLR3 bound vaccine suggested better stability and consistency of hydrogen bonds. The Rg values of the vaccine chain bound to TLR8 indicated overall stability, whereas the vaccine chain bound to TLR3 showed deviations throughout the simulation. These results suggest that the constructed vaccine could be a potential preventive measure against monkeypox and related viruses however, further experimental validation is required to confirm these findings.

Designing a Multiepitope Vaccine against the Foodborne Pathogenic Bacteria Listeria monocytogenes Using Subtractive Immunoinformatics Approaches.

BACKGROUND: Listeria monocytogenes, a Gram-positive bacterium, is a prominent foodborne pathogen that causes listeriosis and poses substantial health hazards worldwide. The continuing risk of listeriosis outbreaks underlies the importance of designing an effective prevention strategy and developing a robust immune response by reverse vaccinology approaches. This study aimed to provide a critical approach for developing a potent multiepitope vaccine against this foodborne disease. METHODS: A chimeric peptide construct containing 5 B-cell epitopes, 16 major histocompatibility complex I (MHC-I) epitopes, and 18 MHC-II epitopes were used to create a subunit vaccination against L. monocytogenes. The vaccine safety was evaluated by several online methods, and molecular docking was performed using ClusPro to determine the binding affinity. Immune simulation was performed using the C-ImmSimm server to demonstrate the immune response. RESULTS: The results validated the antigenicity, non-allergenicity, and nontoxicity of the chimeric peptide construct, confirming its suitability as a subunit vaccine. Molecular docking showed a good score of 1276.5 and molecular dynamics simulations confirmed the construct's efficacy, demonstrating its promise as a good candidate for listeriosis prophylaxis. The population coverage was as high as 91.04% with a good immune response, indicating good antigen presentation with dendritic cells and production of memory cells. CONCLUSIONS: The findings of this study highlight the potential of the designed chimeric peptide construct as an effective subunit vaccine against Listeria, paving the way for future advances in preventive methods and vaccine design.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31.

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Subtractive proteomics-guided vaccine targets identification and designing of multi-epitopes vaccine for immune response instigation against Burkholderia pseudomallei.

In this study, a methodical workflow using subtractive proteomics, vaccine designing, molecular simulation, and agent-based modeling approaches were used to annotate the whole proteome of Burkholderia pseudomallei (strain K96243) for vaccine designing. Among the total 5717 proteins in the whole proteome, 505 were observed to be essential for the pathogen's survival and pathogenesis predicted by the Database of Essential Genes. Among these, 23 vaccine targets were identified, of which fimbrial assembly chaperone (Q63UH5), Outer membrane protein (Q63UH1), and Hemolysin-like protein (Q63UE4) were selected for the subsequent analysis based on the systematic approaches. Using immunoinformatic approaches CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), IFN-positive, and B cell epitopes were predicted for these targets. A total of 9 CTL epitopes were added using the GSS linker, 6 HTL epitopes using the GPGPG linker, and 6 B cell epitopes using the KK linker. An adjuvant was added for enhanced antigenicity, an HIV-TAT peptide for improved delivery, and a PADRE sequence was added to form a 466 amino acids long vaccine construct. The construct was classified as non-allergenic, highly antigenic, and experimentally feasible. Molecular docking results validated the robust interaction of MEVC with immune receptors such as TLR2/4. Furthermore, molecular simulation revealed stable dynamics and compact nature of the complexes. The binding free energy results further validated the robust binding. In silico cloning, results revealed GC contents of 50.73 % and a CIA value of 0.978 which shows proper downstream processing. Immune simulation results reported that after the three injections of the vaccine a robust secondary immune response, improved antigen clearance, and effective immune memory generation were observed highlighting its potential for effective and sustained immunity. Future directions should encompass experimental validations, animal model studies, and clinical trials to substantiate the vaccine's efficacy, safety, and immunogenicity.

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

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

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

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

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