Human immunoglobulin repertoire analysis guides design of vaccine priming immunogens targeting HIV V2-apex broadly neutralizing antibody precursors.

Broadly neutralizing antibodies (bnAbs) to the HIV envelope (Env) V2-apex region are important leads for HIV vaccine design. Most V2-apex bnAbs engage Env with an uncommonly long heavy-chain complementarity-determining region 3 (HCDR3), suggesting that the rarity of bnAb precursors poses a challenge for vaccine priming. We created precursor sequence definitions for V2-apex HCDR3-dependent bnAbs and searched for related precursors in human antibody heavy-chain ultradeep sequencing data from 14 HIV-unexposed donors. We found potential precursors in a majority of donors for only two long-HCDR3 V2-apex bnAbs, PCT64 and PG9, identifying these bnAbs as priority vaccine targets. We then engineered ApexGT Env trimers that bound inferred germlines for PCT64 and PG9 and had higher affinities for bnAbs, determined cryo-EM structures of ApexGT trimers complexed with inferred-germline and bnAb forms of PCT64 and PG9, and developed an mRNA-encoded cell-surface ApexGT trimer. These methods and immunogens have promise to assist HIV vaccine development.

A computational approach to developing a multi-epitope vaccine for combating Pseudomonas aeruginosa-induced pneumonia and sepsis.

Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine's composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa-related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model's authenticity once the protein's three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information. Our model achieved Areas Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance. A comprehensive comparison to alternative methods using distinct benchmark data sets was also employed, with our model outperforming state-of-the-art tools. epitope1D represents not only a significant advance in predictive performance, but also allows biologically meaningful features to be combined and used for model interpretation. epitope1D has been made available as a user-friendly web server interface and application programming interface at https://biosig.lab.uq.edu.au/epitope1d/.

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires.

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

The adaptive immune response to foreign antigens is initiated by T-cell receptor (TCR) recognition on the antigens. Recent experimental advances have enabled the generation of a large amount of TCR data and their cognate antigenic targets, allowing machine learning models to predict the binding specificity of TCRs. In this work, we present TEINet, a deep learning framework that utilizes transfer learning to address this prediction problem. TEINet employs two separately pretrained encoders to transform TCR and epitope sequences into numerical vectors, which are subsequently fed into a fully connected neural network to predict their binding specificities. A major challenge for binding specificity prediction is the lack of a unified approach to sampling negative data. Here, we first assess the current negative sampling approaches comprehensively and suggest that the Unified Epitope is the most suitable one. Subsequently, we compare TEINet with three baseline methods and observe that TEINet achieves an average AUROC of 0.760, which outperforms baseline methods by 6.4-26%. Furthermore, we investigate the impacts of the pretraining step and notice that excessive pretraining may lower its transferability to the final prediction task. Our results and analysis show that TEINet can make an accurate prediction using only the TCR sequence (CDR3$\beta $) and the epitope sequence, providing novel insights to understand the interactions between TCRs and epitopes.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

The T-cell repertoire of Spanish patients with COVID-19 as a strategy to link T-cell characteristics to the severity of the disease.

BACKGROUND: The architecture and dynamics of T cell populations are critical in orchestrating the immune response to SARS-CoV-2. In our study, we used T Cell Receptor sequencing (TCRseq) to investigate TCR repertoires in 173 post-infection COVID-19 patients. METHODS: The cohort included 98 mild and 75 severe cases with a median age of 53. We amplified and sequenced the TCR ß chain Complementary Determining Region 3 (CDR3b) and performed bioinformatic analyses to assess repertoire diversity, clonality, and V/J allelic usage between age, sex and severity groups. CDR3b amino acid sequence inference was performed by clustering structural motifs and filtering validated reactive CDR3b to COVID-19. RESULTS: Our results revealed a pronounced decrease in diversity and an increase in clonal expansion in the TCR repertoires of severe COVID-19 patients younger than 55 years old. These results reflect the observed trends in patients older than 55 years old (both mild and severe). In addition, we identified a significant reduction in the usage of key V alleles (TRBV14, TRBV19, TRBV15 and TRBV6-4) associated with disease severity. Notably, severe patients under 55 years old had allelic patterns that resemble those over 55 years old, accompanied by a skewed frequency of COVID-19-related motifs. CONCLUSIONS: Present results suggest that severe patients younger than 55 may have a compromised TCR repertoire contributing to a worse disease outcome.

Diagnosing Viral Infections Through T-Cell Receptor Sequencing of Activated CD8+ T Cells.

T-cell-based diagnostic tools identify pathogen exposure but lack differentiation between recent and historical exposures in acute infectious diseases. Here, T-cell receptor (TCR) RNA sequencing was performed on HLA-DR+/CD38+CD8+ T-cell subsets of hospitalized coronavirus disease 2019 (COVID-19) patients (n = 30) and healthy controls (n = 30; 10 of whom had previously been exposed to severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]). CDR3α and CDR3ß TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2-associated CDR3α and CDR3ß sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2-associated CDR3α/ß sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic area under the curve of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historical exposure. In essence, this work presents a new paradigm for applying the T-cell repertoire to accomplish TCR-based diagnostics.

Design of multi-epitope chimeric vaccine against Monkeypox virus and SARS-CoV-2: A vaccinomics perspective.

The current era we experience is full with pandemic infectious agents that no longer threatens the major local source but the whole globe. Almost the most emerging infectious agents are severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), followed by monkeypox virus (MPXV). Since no approved antiviral drugs nor licensed active vaccines are yet available, we aimed to utilize immunoinformatics approach to design chimeric vaccine against the two mentioned viruses. This is the first study to deal with design divalent vaccine against SARS-CoV-2 and MPXV. ORF8, E and M proteins from Omicron SARS-CoV-2 and gp182 from MPXV were used as the protein precursor from which multi-epitopes (inducing B-cell, helper T cells, cytotoxic T cells and interferon-É£) chimeric vaccine was contrived. The structure of the vaccine construct was predicted, validated, and docked to toll-like receptor-2 (TLR-2). Moreover, its sequence was also used to examine the immune simulation profile and was then inserted into the pET-28a plasmid for in silico cloning. The vaccine construct was probable antigen (0.543) and safe (non-allergen) with strong binding energy to TLR-2 (-1169.8 kcal/mol) and found to have significant immune simulation profile. In conclusion, the designed chimeric vaccine was potent and safe against SARS-CoV-2 and MPXV, which deserves further consideration.

Epitope screening and vaccine molecule design of PRRSV GP3 and GP5 protein based on immunoinformatics.

Porcine reproductive and respiratory syndrome (PRRS) is a respiratory disease in pigs that causes severe economic losses. Currently, live PRRSV vaccines are commonly used but fail to prevent PRRS outbreaks and reinfection. Inactivated PRRSV vaccines have poor immunogenicity, making PRRSV a significant threat to swine health globally. Therefore, there is an urgent need to develop an effective PRRSV vaccine. This study used immunoinformatics to predict, screen, design and construct a candidate vaccine that fused B-cell epitopes, CTL- and HTL-dominant protective epitopes of PRRSV strain's GP3 and GP5 proteins. The study identified 12 B-cell epitopes, 6 CTL epitopes and 5 HTL epitopes of GP3 and GP5 proteins. The candidate vaccine was constructed with 50S ribosomal protein L7/L1 molecular adjuvant, which has antigenicity, solubility, stability, non-allergenicity and a high affinity for its target receptor, TLR-3. The C-ImmSim immunostimulation results showed significant increases in cellular and humoral responses (B cells and T cells) and production of TGF-ß, IL-2, IL-10, IFN-γ and IL-12. The constructed vaccine was stable and immunogenic, and it can effectively induce strong T-cell and B-cell immune responses against PRRSV. Therefore, it is a promising candidate vaccine for controlling and preventing PRRSV outbreaks.

In silico design and evaluation of a multiepitope vaccine targeting the nucleoprotein of Puumala orthohantavirus.

The Puumala orthohantavirus is present in the body of the bank vole (Myodes glareolus). Humans infected with this virus may develop hemorrhagic fever accompanying renal syndrome. In addition, the infection may further lead to the failure of an immune system completely. The present study aimed to propose a possible vaccine by employing bioinformatics techniques to identify B and T-cell antigens. The best multi-epitope of potential immunogenicity was generated by combining epitopes. Additionally, the linkers EAAAK, AAY, and GPGPG were utilized in order to link the epitopes successfully. Further, C-ImmSim was used to perform in silico immunological simulations upon the vaccine. For the purpose of conducting expression tests in Escherichia coli, the chimeric protein construct was cloned using Snapgene into the pET-9c vector. The designed vaccine showed adequate results, evidenced by the global population coverage and favorable immune response. The developed vaccine was found to be highly effective and to have excellent population coverage in a number of computer-based assessments. This work is fully dependent on the development of nucleoprotein-based vaccines, which would constitute a significant step forward if our findings were used in developing a global vaccination to combat the Puumala virus.

Phylogenetic analysis and antigenic epitope prediction for E6 and E7 of Alpha-papillomavirus 9 in Taizhou, China.

BACKGROUND: Alpha-papillomavirus 9 (α-9) is a member of the human papillomavirus (HPV) α genus, causing 75% invasive cervical cancers worldwide. The purpose of this study was to provide data for effective treatment of HPV-induced cervical lesions in Taizhou by analysing the genetic variation and antigenic epitopes of α-9 HPV E6 and E7. METHODS: Cervical exfoliated cells were collected for HPV genotyping. Positive samples of the α-9 HPV single type were selected for E6 and E7 gene sequencing. The obtained nucleotide sequences were translated into amino acid sequences (protein primary structure) using MEGA X, and positive selection sites of the amino acid sequences were evaluated using PAML. The secondary and tertiary structures of the E6 and E7 proteins were predicted using PSIPred, SWISS-MODEL, and PyMol. Potential T/B-cell epitopes were predicted by Industrial Engineering Database (IEDB). RESULTS: From 2012 to 2023, α-9 HPV accounted for 75.0% (7815/10423) of high-risk HPV-positive samples in Taizhou, both alone and in combination with other types. Among these, single-type-positive samples of α-9 HPV were selected, and the entire E6 and E7 genes were sequenced, including 298 HPV16, 149 HPV31, 185 HPV33, 123 HPV35, 325 HPV52, and 199 HPV58 samples. Compared with reference sequences, 34, 12, 10, 2, 17, and 17 nonsynonymous nucleotide mutations were detected in HPV16, 31, 33, 35, 52, and 58, respectively. Among all nonsynonymous nucleotide mutations, 19 positive selection sites were selected, which may have evolutionary significance in rendering α-9 HPV adaptive to its environment. Immunoinformatics predicted 57 potential linear and 59 conformational B-cell epitopes, many of which are also predicted as CTL epitopes. CONCLUSION: The present study provides almost comprehensive data on the genetic variations, phylogenetics, positive selection sites, and antigenic epitopes of α-9 HPV E6 and E7 in Taizhou, China, which will be helpful for local HPV therapeutic vaccine development.

Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Design a novel of Brucellosis preventive vaccine based on IgV_CTLA-4 and multiple epitopes via immunoinformatics approach.

Brucellosis is a zoonotic disease caused by Brucella, which is difficult to eliminate by conventional drugs. Therefore, a novel multi-epitope vaccine (MEV) was designed to prevent human Brucella infection. Based on the method of "reverse vaccinology", cytotoxic T lymphocyte epitopes (CTLEs), helper T lymphocyte epitopes (HTLEs), linear B-cell epitopes (LBEs) and conformational B-cell epitopes (CBEs) of four Brucella proteins (VirB9, VirB10, Omp 19 and Omp 25) were obtained. In order to keep the correct protein folding, the multiple epitopes was constructed by connecting epitopes through linkers. In view of the significant connection between human leukocyte antigen CTLA-4 and B7 molecules found on antigen presenting cells (APCs), a new vaccine (V_C4MEV) for preventing brucellosis was created by combining CTLA-4 immunoglobulin variable region (IgV_CTLA-4) with MEV protein. Immunoinformatics analysis showed that V_C4MEV has a good secondary and tertiary structure. Additionally, molecular docking and molecular dynamics simulation (MD) revealed a robust binding affinity between IgV_ CTLA-4 and the B7 molecule. Notably, the vaccine V_C4MEV was demonstrated favorable immunogenicity and antigenicity in both in vitro and in vivo experiments. V_C4MEV had the potential to activate defensive cells and immune responses, offering a hopeful approach for developing vaccines against Brucella in the upcoming years.

A computational approach to design a multiepitope vaccine against H5N1 virus.

Since 1997, highly pathogenic avian influenza viruses, such as H5N1, have been recognized as a possible pandemic hazard to men and the poultry business. The rapid rate of mutation of H5N1 viruses makes the whole process of designing vaccines extremely challenging. Here, we used an in silico approach to design a multi-epitope vaccine against H5N1 influenza A virus using hemagglutinin (HA) and neuraminidase (NA) antigens. B-cell epitopes, Cytotoxic T lymphocyte (CTL) and Helper T lymphocyte (HTL) were predicted via IEDB, NetMHC-4 and NetMHCII-2.3 respectively. Two adjuvants consisting of Human ß-defensin-3 (HßD-3) along with pan HLA DR-binding epitope (PADRE) have been chosen to induce more immune response. Linkers including KK, AAY, HEYGAEALERAG, GPGPGPG and double EAAAK were utilized to link epitopes and adjuvants. This construct encodes a protein having 350 amino acids and 38.46 kDa molecular weight. Antigenicity of ~ 1, the allergenicity of non-allergen, toxicity of negative and solubility of appropriate were confirmed through Vaxigen, AllerTOP, ToxDL and DeepSoluE, respectively. The 3D structure of H5N1 was refined and validated with a Z-Score of - 0.87 and an overall Ramachandran of 99.7%. Docking analysis showed H5N1 could interact with TLR7 (docking score of - 374.08 and by 4 hydrogen bonds) and TLR8 (docking score of - 414.39 and by 3 hydrogen bonds). Molecular dynamics simulations results showed RMSD and RMSF of 0.25 nm and 0.2 for H5N1-TLR7 as well as RMSD and RMSF of 0.45 nm and 0.4 for H5N1-TLR8 complexes, respectively. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) confirmed stability and continuity of interaction between H5N1-TLR7 with the total binding energy of - 29.97 kJ/mol and H5N1-TLR8 with the total binding energy of - 23.9 kJ/mol. Investigating immune response simulation predicted evidence of the ability to stimulate T and B cells of the immunity system that shows the merits of this H5N1 vaccine proposed candidate for clinical trials.

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

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

Design of Cryptococcus neoformans multi-epitope vaccine based on immunoinformatics method.

Cryptococcus neoformans is a widely distributed opportunistic pathogenic fungus. While C. neoformans commonly infects immunocompromised individuals, it can also affect those who are immunocompetent. Transmission of C. neoformans primarily occurs through the respiratory tract, leading to the development of meningitis. The mortality rate of Cryptococcal meningitis is high, and treatment options are limited. Cryptococcus neoformans infections pose a significant public health threat and currently lack targeted and effective response strategies. This study aimed to screen T lymphocyte (cytotoxic T lymphocyte and helper T lymphocyte) and B lymphocyte epitopes derived from four C. neoformans antigens and develop two multi-epitope vaccines by combining them with various adjuvants. Molecular docking results demonstrated that the vaccines bind stably to Toll-like receptor 4 ( and induce innate immunity. The credibility of the molecular docking results was validated through subsequent molecular dynamics simulations. Furthermore, the results of immune simulation analyses underscored the multi-epitope vaccine's capability to effectively induce robust humoral and cellular immune responses within the host organism. These two vaccines have demonstrated theoretical efficacy against C. neoformans infection as indicated by computer analysis. Nevertheless, additional experimental validation is essential to substantiate the protective efficacy of the vaccines.

A multi-epitope Cryptococcus neoformans vaccine covering the most common A and D phenotypes was designed using bioinformatics methods.

Development of a novel multi­epitope 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.