T-cell Epitope Prediction Research Articles

Characterisation of EfbA from the Endodontic Pathogen Enterococcus faecalis and Prediction of Immunodominant EfbA Epitope Peptides: An In-vitro and In-silico Study

Introduction: Enterococcus faecalis (E. faecalis) belongs to Group D Streptococci and causes recalcitrant infections such as urinary tract infections, wound infections, intra-abdominal and pelvic infections, bacteraemia, and endocarditis. The emergence of multiple drug resistance, including to the last-resort drug vancomycin, is a major concern with E. faecalis infections. Therefore, it is necessary to implement alternative strategies to combat E. faecalis infections in dental settings. An immunoinformatics approach is one such strategy that can predict and assess the immunodominant B-cell and T-cell epitopes. Thus, there is a need to identify novel vaccine candidates as immunodominant epitope peptides from the Enterococcal Fibronectin Binding Protein-A (EfbA) protein for E. faecalis. Aim: To predict the immunodominant B-cell and T-cell epitopes from the EfbA protein of E. faecalis. Materials and Methods: An in-vitro and in-silico pilot study was conducted in the Department of Microbiology at Saveetha Dental College and Hospitals, Chennai, Tamil Nadu, India. Total of 20 carious scrapings were collected from patients with root caries and were phenotypically characterised for E. faecalis and the EfbA genetic determinant by Polymerase Chain Reaction (PCR) amplification. The EfbA protein was retrieved from the National Center for Biotechnology Information (NCBI) database, after which prediction of antigenicity and allergenicity was performed via the VaXiJen and Algpred servers, respectively. The prediction of T-cell epitopes was carried out with the help of the EpiDOCK server, and B-cell predictions were made using the Kolaskar and Tongaonkar tool. Docking of the epitopes with reference to Human Leukocyte Antigens (HLA) alleles was assessed using the ClusPro server, and the results were evaluated for the T-cell dominant epitopes. Results: Among the 20 samples, E. faecalis was characterised in 5 (25%) patients, with three strains exhibiting Multidrug Resistant (MDR) traits. Among the three MDR strains, two strains showed the presence of the EfbA gene by PCR. In-silico analysis of the EfbA protein yielded a total of 17 epitopes, and based on the assessments, a final selection of two epitopes (IRAKGKNHK and LLLSAHPSY) was made, showing promising docking scores with HLA alleles and TLR2. Conclusion: Among the two epitopes, LLLSAHPSY was identified as the most significant immune-dominant epitope predicted, which needs to be further evaluated for vaccine synthesis and experimentation.

Structural analyses of Cryptosporidium parvum epitopes reveal a novel scheme of decapeptide binding to H-2Kb.

Cryptosporidium has gained much attention as a major cause of diarrhea worldwide. Here, we present the first structure of H-2Kb complexed with a decapeptide from Cryptosporidium parvum Gp40/15 protein (Gp40/15-VTF10). In contrast to all published structures, the aromatic residue P3-Phe of Gp40/15-VTF10 is anchored in pocket C rather than the canonical Y/F at P5 or P6 reported for octapeptides and nonapeptides. The results of in vitro refolding assays and circular dichroism experiments showed that the side chains of P3 and P5 play key roles in Gp40/15-VTF10 peptide binding. However, functional analysis of decapeptide epitopes revealed that the Gp40/15-VTF10 peptide did not elicit a strong CD8+T immune response, whereas the decapeptide epitope MEDLE2-INF10 induced a significant CD8+ T-cell response in peptide-immunized C57BL/6 mice. Using a model structure of H-2Kb-INF10 complex, we found that the antigenic decapeptide INF10 exhibits a completely different conformation, with the aromatic anchors P3F and P7F docked into the D and C pockets, respectively, while similar peptide conformation and hydrogen bond interactions between the peptide and major histocompatibility complex were found in the resolved H-2Kb-SVF9 complex. As the H-2Kb molecule predominantly prefers octapeptides with a strong anchor of P5 Y/F (or P6 Y/F for nonapeptides) binding to the C pocket, we propose that P7 Y/F in the C pocket may represent a novel binding mode for decapeptides. The results should increase the accuracy of T-cell epitope prediction and support the development of T-cell epitope vaccines against cryptosporidiosis.

Predicting Immunogenic Epitopes Variation of Envelope 2 Gene Among Chikungunya Virus Clonal Lineages by an In Silico Approach.

Chikungunya virus (CHIKV), responsible for a mosquito-borne viral illness, has rapidly spread worldwide, posing a significant global health threat. In this study, we explored the immunogenic variability of CHIKV envelope 2 (E2), a pivotal component in the anti-CHIKV immune response, using an in silico approach. After extracting the representative sequence types of the CHIKV E2 antigen, we predicted the structure-based B-cell epitopes and MHC I and II binding T-cell epitopes. Variations in key T-cell epitopes were further analyzed using molecular docking simulations. We extracted 258 E2 gene sequences from a pool of 1660 blast hits, displaying homology levels ranging from 93.6% to 100%. This revealed 44 sequence types, each representing a unique genetic variant. Phylogenetic analysis revealed distinct geographically distributed clonal lineages (clades I-IV). The B-cell linear and discontinuous epitopes demonstrated a similar distribution across the E2 protein of different strains, spanning domains A, B, and C, with some slight variations. Moreover, T-cell epitope prediction revealed eight conserved MHC class I hot spots and three MHC II hot spots, displaying variations among lineages. Among clade II strains, there were significant variations (N5H, S118G, G194S, L248F/S, and I255V/T) observed in epitopes, distinct from strains belonging to other lineages. Additionally, molecular docking showed that variations in MHC I epitopes across clonal lineages induced changes in the structure of the peptide-MHC complexes, potentially resulting in immunogenic disparities. We expect that this in silico approach will serve as a complementary tool to experimental platforms for exploring immunogenic variation or developing biomarkers for vaccine design and other related studies.

Computational insights in design of Crimean-Congo hemorrhagic fever virus conserved immunogenic nucleoprotein peptides containing multiple epitopes.

Crimean-Congo hemorrhagic fever virus (CCHFV) belongs to Nairoviridae family and has tripartite RNA genome. It is endemic in various countries of Asia, Africa, and Europe and is primarily transmitted by Hyalomma ticks but nosocomial transmission also been reported. Vaccines for CCHF are in early phase of clinical trial; therefore, this work is centered on identification of potential immunogenic peptide as vaccine candidates with application of different immunoinformatics approaches. Eleven conserved (>90%) peptides of CCHFV nucleoprotein were selected for CD8+ T-cell (NetMHCpan 4.1b and NetCTLpan 1.1 server) and CD4+ T-cell (NetMHCIIpan-4.0 server and Tepitool) epitope prediction. Three peptides containing multiple CD8+ and CD4+ T-cell and B-cell epitopes were identified on basis of consensus prediction approach. Peptides displayed good antigenicity score of 0.45-0.68 and predicted to bind with diverse human leukocyte antigen (HLA) alleles. Molecular docking was performed with epitopes to HLA and HLA-epitopes complex to T-cell receptor (TCR). In most of the cases, docked complex of HLA-epitope and HLA-epitopes-TCR have the binding energy close to respective natural bound peptide complex with HLA and TCR. Molecular dynamic simulation also revealed that HLA-peptide complexes have minimum fluctuation and deviation than HLA-peptide-TCR docked over 50ns simulation run. Considering these findings, identified peptides can serve as potential vaccine candidates for CCHFV disease.

Designing a multi-epitope construct using immuno-informatic tools to prepare a messenger RNA vaccine against Rhipicephalus microplus ticks

Background and Aim: Ticks are blood-feeding ectoparasites that transmit pathogens to animals and humans. One of the most important hard ticks in animals is Rhipicephalus microplus, which transmits Babesia and Anaplasma spp. Although many potential tick vaccine candidates have been identified, no effective vaccine that can provide sterile immunity against R. microplus tick infestations has been developed. This study aimed to design a construct using different computational tools to identify and predict immunogenic epitopes within protein sequences and to prepare a messenger RNA (mRNA) vaccine against R. microplus based on lipid nanoparticles (LNPs). Materials and Methods: The R. microplus proteins (Bm86, Subolesin, and ATAQ) were selected and their consensus sequence was obtained from the National Center for Biotechnology Information in FASTA format. The Immune Epitope Database and Analysis Resource (IEBD) server was used for the prediction of helper T-cell epitopes, the NetCTL 1.2 server was used to predict cytotoxic T-cell epitopes, and the ABCpred server was used for B-cell epitope prediction. Antigenicity testing, allergenicity assessment, and toxicity screening were immuno-informatic techniques used to identify potent epitopes within protein sequences. The multi-epitope construct was prepared and cloned into the pVAX1 plasmid. Plasmids were transformed in compatible competent cells, and restriction analysis was performed. After restriction analysis of the transformed plasmid, in vitro transcription was performed to prepare mRNA. The mRNA was purified, quantified, and converted into complementary DNA, and gene-specific primers were used to confirm the in vitro transcription of mRNA. A mixture of four lipids containing 1,2-dioleoyl-3-dimethylammonium-propane (DODAP), Distearoylphosphatidylcholine (DSPC, cholesterol, and 1,2-Dimyristoyl-sn-glycero-3-methoxypolyethylene glycol-2000 (DMG PEG-2000 was used to prepare LNPs. LNPs were characterized using a scanning electron microscope, Zeta potential, and Zeta Sizer tests. Results: More than 1000 epitopes were predicted, from which only nine helper T-lymphocytes, 18 cytotoxic T-lymphocytes, and nine B-cell epitopes of all three proteins were selected with high antigenic scores of 0.958 for Bm86, 0.752 for Subolesin, and 0.964 for ATAQ, respectively. An adjuvant was used to enhance immune responses, all of which were linked to one another using GPGPG, AAY, and KK linkers, respectively. The physiochemical properties predicted that the instability index of the construct would be <40%, indicating that the construct is stable. Plasmids were transformed in compatible competent cells, and white-transformed colonies were observed. Restriction analysis was performed, DNA was transcribed into mRNA, and LNPs were prepared and characterized. Conclusion: More than 1000 epitopes were predicted using immune informatic tools, and only high-scoring epitopes were selected. A multi-epitope construct was designed using bio-informatic tools, and its physicochemical properties were predicted. The design construct was inserted into the pVAX1 plasmid, and in vitro transcription was performed to prepare the mRNA. LNPs of mRNA were prepared and characterized to be used as vaccines. It was found that LNPs were stable and nanometer-sized. Keywords: immuno-informatic tools, lipid nanoparticles, multiepitope construct, Rhipicephalus microplus.

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.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells.

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

Design of a multi-epitope-based vaccine candidate against Bovine Genital Campylobacteriosis using a reverse vaccinology approach

BackgroundBovine Genital Campylobacteriosis (BGC), a worldwide distributed venereal disease caused by Campylobacter fetus subsp. venerealis (Cfv), has a relevant negative economic impact in cattle herds. The control of BGC is hampered by the inexistence of globally available effective vaccines. The present in silico study aimed to develop a multi-epitope vaccine candidate against Cfv through reverse vaccinology.ResultsThe analysis of Cfv strain NCTC 10354 proteome allowed the identification of 9 proteins suitable for vaccine development. From these, an outer membrane protein, OmpA, and a flagellar protein, FliK, were selected for prediction of B-cell and T-cell epitopes. The top-ranked epitopes conservancy was assessed in 31 Cfv strains. The selected epitopes were integrated to form a multi-epitope fragment of 241 amino acids, which included 2 epitopes from OmpA and 13 epitopes from FliK linked by GPGPG linkers and connected to the cholera toxin subunit B by an EAAAK linker. The vaccine candidate was predicted to be antigenic, non-toxic, non-allergenic, and soluble upon overexpression. The protein structure was predicted and optimized, and the sequence was successfully cloned in silico into a plasmid vector. Additionally, immunological simulations demonstrated the vaccine candidate’s ability to stimulate an immune response.ConclusionsThis study developed a novel vaccine candidate suitable for further in vitro and in vivo experimental validation, which may become a useful tool for the control of BGC.

Introduction of protein vaccine candidate based on AP65, AP33, and α-actinin proteins against Trichomonas vaginalis parasite: an immunoinformatics design

Background Trichomonas vaginalis is the most common nonviral sexually transmitted disease (STI) worldwide. Vaccination is generally considered to be one of the most effective methods of preventing infectious diseases. Using AP65, AP33 and α-actinin proteins, this research aims to develop a protein vaccine against Trichomonas vaginalis. MethodsBased on the B-cell and T-cell epitope prediction servers, the most antigenic epitopes were selected, and with the necessary evaluations, epitope-rich domains of three proteins, AP65, AP33, and α-actinin, were selected and linked. Subsequently, the ability of the vaccine to interact with toll-like receptors 2 and 4 (TLR2 and TLR4) was assessed. The stability of the interactions was also studied by molecular dynamics for a duration of 100 nanoseconds. ResultsThe designed protein consists of 780 amino acids with a molecular weight of 85247.31 daltons. The results of the interaction of the vaccine candidate with TLR2 and TLR4 of the immune system also showed that there are strong interactions between the vaccine candidate protein with TLR2 (-890.7 kcal mol-1) and TLR4 (-967.3 kcal mol-1). All parameters studied to evaluate the stability of the protein structure and the protein-TLR2 and protein-TLR4 complexes showed that the structure of the vaccine candidate protein is stable alone and in complex with the immune system receptors. Investigation of the ability of the designed protein to induce an immune response using the C-ImmSim web server also showed that the designed protein is capable of stimulating B- and T-cell lymphocytes to produce the necessary cytokines and antibodies against Trichomonas vaginalis. ConclusionsOverall, our vaccine may have potential protection against Trichomonas vaginalis. However, for experimental in vivo and in vitro studies, it may be a good vaccine candidate.Graphical

A Mixture of T-Cell Epitope Peptides Derived from Human Respiratory Syncytial Virus F Protein Conferred Protection in DR1-TCR Tg Mice.

Human respiratory syncytial virus (HRSV) poses a significant disease burden on global health. To date, two vaccines that primarily induce humoral immunity to prevent HRSV infection have been approved, whereas vaccines that primarily induce T-cell immunity have not yet been well-represented. To address this gap, 25 predicted T-cell epitope peptides derived from the HRSV fusion protein with high human leukocyte antigen (HLA) binding potential were synthesized, and their ability to be recognized by PBMC from previously infected HRSV cases was assessed using an ELISpot assay. Finally, nine T-cell epitope peptides were selected, each of which was recognized by at least 20% of different donors' PBMC as potential vaccine candidates to prevent HRSV infection. The protective efficacy of F-9PV, a combination of nine peptides along with CpG-ODN and aluminum phosphate (Al) adjuvants, was validated in both HLA-humanized mice (DR1-TCR transgenic mice, Tg mice) and wild-type (WT) mice. The results show that F-9PV significantly enhanced protection against viral challenge as evidenced by reductions in viral load and pathological lesions in mice lungs. In addition, F-9PV elicits robust Th1-biased response, thereby mitigating the potential safety risk of Th2-induced respiratory disease during HRSV infection. Compared to WT mice, the F-9PV mice exhibited superior protection and immunogenicity in Tg mice, underscoring the specificity for human HLA. Overall, our results demonstrate that T-cell epitope peptides provide protection against HRSV infection in animal models even in the absence of neutralizing antibodies, indicating the feasibility of developing an HRSV T-cell epitope peptide-based vaccine.

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.

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.

Expansion of Proteome-wide Coxiella burnetii Comparative T-cell Epitope Prediction to Include Small Ruminant Hosts

Expansion of Proteome-wide Coxiella burnetii Comparative T-cell Epitope Prediction to Include Small Ruminant Hosts

Bioinformatics and immunoinformatics assisted multiepitope vaccine construct against Burkholderia anthina

Burkholderia anthina is a pathogenic bacterial species belonging to the Burkholderiaceae family and it is mainly considered the etiological agent of chronic obstructive pulmonary diseases associated with cystic fibrosis, due to being intrinsic antibiotic resistant making it difficult to treat pulmonary infections. Hence increased rate of antibiotic-resistant bacterial species vaccine development is the priority to tackle this problem. In research work, we designed a multi-epitope-based vaccine construct against B. anthina using reverse vaccinology immunoinformatics and biophysical approaches. Based on the subtractive proteomic screening of core proteins we identified 3 probable antigenic proteins and good vaccine targets namely, type VI secretion system tube protein hcpBurkholderia, fimbria/pilus periplasmic chaperone and fimbrial biogenesis outer membrane usher protein. The selected 3 proteins were used for B and B cells B-derived T-cell epitopes prediction. In epitopes prediction, different epitopes were predicted with various lengths and percentile scores and subjected to further immunoinformatics analysis. In immunoinformatics screening a total number of 06, IDDGNANAL, KTVKPDPRY, SEVESGSAP, YGGDLTVEV, SVSHDTNGR, and GSKADGYQR epitopes were considered good vaccine target candidates and shortlisted for vaccine construct designing. The vaccine construct was designed by joining selected epitopes with the help of a GPGPG linker and additionally linked with cholera toxin b subunit adjuvant to increase the efficacy of the vaccine construct the sequence of the said adjuvant were retrieved from protein data bank through its (PDB ID: 5ELD). The designed vaccine construct was evaluated for its physiochemical properties analysis in which we reported that the vaccine construct comprises 216 amino acids with a molecular weight of 22.37499 kilo Dalton, 15.55 instability index (II) is computed, and this classifies that the vaccine construct is properly stable. VaxiJen v2.0 web server predicted that the vaccine construct is probable antigenic in nature with 0.6320 predicted value. Furthermore AllerTOP v. 2.0 tool predicted that the designed vaccine construct is non allergic in nature. Molecular docking analysis was done for analysis of the binding affinity of the vaccine construct with TLR-2 (PDB ID: 6NIG), the docking results predicted 799.2 kcal/mol binding energy score that represents the vaccine construct has a good binding ability with TLR-2. Moreover, molecular dynamic simulation analysis results revealed that the vaccine construct and immune cell receptor has proper binding stability over various environmental condition, i.e. change in pressure range, temperature, and motion. After each analysis, we observed that the vaccine construct is safe stable, and probably antigenic and could generate an immune response against the target pathogen but in the future, experimental analysis is still needed to verify in silico base results.

Immunoinformatics Prediction and Protective Efficacy of Vaccine Candidate PiuA-PlyD4 Against Streptococcus Pneumoniae.

This study was designed to evaluate the immune protective efficacy of the novel Streptococcus pneumoniae (S. pneumoniae) protein vaccine PiuA-PlyD4 through immunoinformatics prediction and in vitro and in vivo experiments. In this study, we conducted immunoinformatics prediction and protection analysis on the fusion protein PiuA-PlyD4. The epitope composition of the vaccine was analyzed based on the prediction of B-cell and helper T-cell epitopes. Meanwhile, the molecular docking of PiuA and TLR2/4 was simulated. After immunizing C57BL/6 mice with the prepared vaccine, the biological safety, immunogenicity and conservation were evaluated. By constructing different infection models and from the aspects of adhesion inhibition and cytokines, the protective effect of the fusion protein vaccine PiuA-PlyD4 on S. pneumoniae infection was explored. PiuA-PlyD4 has abundant B-cell and helper T-cell epitopes and shows a high antigenicity score and structural stability. Molecular docking analysis suggested the potential interaction between PiuA and TLR2/4. The specific antibody titer of fusion protein antiserum was as high as (7.81±2.32) ×105. The protective effect of the immunized mice on nasal and lung colonization was significantly better than that of the control group, and the survival rate against S. pneumoniae infection of serotype 3 reached 50%. Cytokine detection showed that the humoral immune response, Th1, Th2 and Th17 cellular immune pathways were all involved in the process. The study indicates that PiuA-PlyD4, whether the results are predicted by immunoinformatics or experimentally validated in vivo and in vitro, has good immunogenicity and immunoreactivity and can provide effective protection against S. pneumoniae infection. Therefore, it can be considered a promising prophylactic vaccine candidate for S. pneumoniae.

Pangenomic and immunoinformatics based analysis of Nipah virus revealed CD4+ and CD8+ T-Cell epitopes as potential vaccine candidates.

Introduction: Nipah (NiV) is the zoonotic deadly bat-borne virus that causes neurological and respiratory infections which ultimately lead to death. There are 706 infected cases reported up till now especially in Asia, out of which 409 patients died. There is no vaccine and effective treatment available for NiV infections and we have to timely design such strategies as world could not bear another pandemic situation. Methods: In this study, we screened viral proteins of NiV strains based on pangenomics analysis, antigenicity, molecular weight, and sub-cellular localization. The immunoproteomics based approach was used to predict T-cell epitopes of MHC class-I and II as potential vaccine candidates. These epitopes are capable to activate CD4+, CD8+, and T-cell dependent B-lymphocytes. Results: The two surface proteins including fusion glycoprotein (F) and attachment glycoprotein (G) are antigenic with molecular weights of 60kDa and 67kDa respectively. Three epitopes of F protein (VNYNSEGIA, PNFILVRNT, and IKMIPNVSN) were ranked and selected based on the binding affinity with MHC class-I, and 3 epitopes (VILNKRYYS, ILVRNTLIS, and VKLQETAEK) with MHC-II molecules. Similarly, for G protein, 3 epitopes each for MHC-I (GKYDKVMPY, ILKPKLISY, and KNKIWCISL) and MHC-II (LRNIEKGKY, FLIDRINWI, and FLLKNKIWC) with substantial binding energies were predicted. Based on the physicochemical properties, all these epitopes are non-toxic, hydrophilic, and stable. Conclusion: Our vaccinomics and system-level investigation could help to trigger the host immune system to prevent NiV infection.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of −4.37 kcal/mol, −0.20 kcal/mol and −22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes. Communicated by Ramaswamy H. Sarma

Vaccine candidates for cellulitis from Staphylococcus aureus and Streptococcus pyogenes – In silico approach

Staphylococcus aureus and Streptococcus pyogenes are the causative agents of cellulitis, a bacterial skin infection. Cellulitis primarily affects the skin, and it later spreads to the other parts of the body, especially through the lymph nodes and bloodstream. Without proper medication, it becomes life-threatening. The main symptoms include inflammation, tenderness, tight and swollen skin, and fever. Vaccines using epitopes as antigens trigger quick immune responses; in addition, they are cost effective. These antigens are derived from bacterial proteins. In this study, virulence factors from membrane proteins such as sak, tst, isdA, clfB, and can fromS. aureus (Fig. 1) and SPy, scpA, and hylp1 from S. pyogenes (Fig. 2) were selected for predicting the vaccine epitopes. VaxiJen server was used to evaluate the antigenicity of the selected proteins; BCPred, AAPPred, and ABCpred were used for B-cell epitope prediction, while ProPred and ProPred I were used for T-cell epitope prediction. MHCPred was used for the selected alleles, DRB1*0101 and DRB1*0401. Pepitope server was used for epitope mapping of the selected peptides. Epitopes that are common among those from BCPred, AAPPred, and ABCpred were selected: LKYGPKFDK (tst) and MTFDDKNGK (cna) from S. aureus and YTNSDKGGS (SPy), FKIEPDTTV (SPy), MTPSERLDL (scpA), VKTDDQQDK (scpA), and LKFKPAATV (hylp1) from S. pyogenes. Among them, YTNSDKGGS, MTPSERLDL, and MTFDDKNGK, were identified as suitable vaccine candidates for eliciting immune responses. These results of this study can be used to create a peptide-based vaccine for preventing cellulitis.

Characterization of T-Cell Epitopes in Food Allergens by Bioinformatic Tools.

The identification of T-cell epitopes is a critical step in the understanding of theimmunologic mechanisms such as food allergy. Epitope screening in silico by bioinformatic tools can be used to identify T-cell epitopes, which can save time and resources. In this chapter, a multiparametric approach to predict and assess major histocompatibility complex (MHC) class II binding T-cell epitopes using bioinformatics was introduced for food allergens. Furthermore, the ability of predicted T-cell epitopes to induce interleukin (IL)-4, as well as the allergenicity potential based on the sequence analysis and population coverage of epitopes were also determined. The molecular docking approach was further used to explore the binding ability between epitopes and human leukocyte antigen (HLA) class II molecules. The amino acids that might be responsible for binding to HLA class II molecules and their binding interactions were analyzed.

A measles virus-based vaccine induces robust chikungunya virus-specific CD4+ T-cell responses in a phase II clinical trial

Chikungunya virus (CHIKV) is an alphavirus transmitted by mosquitos that causes a debilitating disease characterized by fever and long-lasting polyarthralgia. To date, no vaccine has been licensed, but multiple vaccine candidates are under evaluation in clinical trials. One of these vaccines is based on a measles virus vector encoding for the CHIKV structural genes C, E3, E2, 6K, and E1 (MV-CHIK), which proved safe in phase I and II clinical trials and elicited CHIKV-specific antibody responses in adult measles seropositive vaccine recipients. Here, we predicted T-cell epitopes in the CHIKV structural genes and investigated whether MV-CHIK vaccination induced CHIKV-specific CD4+ and/or CD8+ T-cell responses. Immune-dominant regions containing multiple epitopes in silico predicted to bind to HLA class II molecules were found for four of the five structural proteins, while no such regions were predicted for HLA class I. Experimentally, CHIKV-specific CD4+ T-cells were detected in six out of twelve participants after a single MV-CHIK vaccination and more robust responses were found 4 weeks after two vaccinations (ten out of twelve participants). T-cells were mainly directed against the three large structural proteins C, E2 and E1. Next, we sorted and expanded CHIKV-specific T cell clones (TCC) and identified human CHIKV T-cell epitopes by deconvolution. Interestingly, eight out of nine CD4+ TCC recognized an epitope in accordance with the in silico prediction. CHIKV-specific CD8+ T-cells induced by MV-CHIK vaccination were inconsistently detected. Our data show that the MV-CHIK vector vaccine induced a functional transgene-specific CD4+ T cell response which, together with the evidence of neutralizing antibodies as correlate of protection for CHIKV, makes MV-CHIK a promising vaccine candidate in the prevention of chikungunya.