Cytotoxic T Lymphocytes Epitopes Research Articles

In silico Identification of MHC Displayed Tumor Associated Peptides in Ovarian Cancer for Multi-Epitope Vaccine Construct.

Recognizing the potential of the immune system, immunotherapies have brought about a revolution in the treatment of cancer. Low tumour mutational burden and strong immunosuppression in the peritoneal tumor microenvironment (TME) lead to poor outcomes of immune checkpoint inhibition (ICI) and CART cell therapy in ovarian cancer. Alternative immunotherapeutic strategies are of utmost importance to achieve sound clinical success. The development of peptide vaccines based on tumor-associated antigens (TAAs) for ovarian cancer cells can be a potential target to provoke an anti-tumor immune response and subsequent clearance of tumour cells. The purpose of this in-silico study was to find potential epitopes for a multi-epitope vaccine construct using the immunopeptidomics landscape of ovarian carcinoma. The four TAAs (MUC16, IDO1, FOLR1, and DDX5) were selected as potential epitopes for B-cells, helper T-lymphocytes (HTLs), and cytotoxic T-lymphocytes (CTLs) predicted on the basis of antigenic, allergenic, and toxic properties. These epitopes were combined with suitable linkers and an adjuvant to form a multi-epitope construct. Four HTLs, 13 CTLs, and 6 potential B-cell epitopes were predicted from the TAAs. The designed multi-epitope construct was potentially immunogenic, non-toxic, and nonallergenic. Physicochemical properties and higher-order structural analyses of the final construct revealed a potential vaccine candidate. The designed vaccine construct has the potential to trigger both humoral and cellular immune responses and may be employed as a therapeutic immunization candidate for ovarian malignancies. However, further in vitro and animal experimentation is required to establish the efficacy of the vaccine candidate.

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 be eliminated 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 lymphocytes epitopes (CTLEs), helper T lymphocytes 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 had 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.

An immunoinformatic investigation on Rift Valley fever virus protein reveals possible epitopes for vaccines.

This immunoinformatic study identified potential epitopes from the envelopment polyprotein (Gn/Gc) of Rift Valley fever virus (RVFV), a pathogenic virus causing severe fever in humans and livestock. Effective vaccination is crucial for controlling RVFV outbreaks. The identification of suitable epitopes is crucial for the development of safe and effective vaccines. Protein sequences were obtained from the UniProt database, and evaluated through VaxiJen v2.0 to predict the B and T-cell epitopes within the RVFV glycoprotein. Gn/Gc protein sequences were analyzed with bioinformatics tools and algorithms. The predicted T-cell and B-cell epitopes were evaluated for antigenicity, allergenicity, and toxicity by the VaxiJen v2.0 system, AllerTop v2.0, and ToxinPred server, respectively. We employed computational methods to screen the RVFV envelopment polyprotein encompassing N-terminal and C-terminal glycoprotein segments, to discover antigenic T- and B-cell epitopes. Our analysis unveiled multiple potential epitopes within the RVFV glycoprotein, specifically within the Gn/Gc protein sequences. Subsequently, we selected eleven cytotoxic T-lymphocytes (CTL) and four helper T-lymphocytes (HTL) for population coverage analysis, which collectively extended to cover 97.04% of the world's population, representing diverse ethnicities and regions. Notably, the CTL epitope VQADLTLMF exhibited binding affinity to numerous human leukocyte antigen (HLA) alleles. The identification of glycoprotein (Gn/Gc) epitopes through this immunoinformatic study bears significant implications for advancing the development of an effective RVFV vaccine. These findings provide valuable insights into the immunological aspects of the disease and may contribute towards the development of broad-spectrum antiviral therapies targeting other RNA viruses with similar polymerase enzymes.

A Multi-epitope Subunit Vaccine Identification and Development Against Scrub Typhus (Orientia tsutsugamushi) Using Immunoinformatics Approaches.

Background The pathogen Orientia tsutsugamushi, which causes scrub typhus, is rapidlyspreading throughout the tropics. As a measure to improve public health, the development of a vaccine for human use is essential. Scrub typhus is listed as one of the underdiagnosed and underreported febrile infections. This vector-borne zoonotic infection appears as eschar on the patient's skin. Methods Immunoinformaticswas employed to predict the multi-epitope subunit vaccine that will activate both B and T cells. The final vaccine includes lipoprotein LprA as an adjuvant at the N-terminus along with B-cell, helperTlymphocyte (HTL), and cytotoxic T lymphocyte (CTL)-binding epitopes to boost immunogenicity. Assessing the vaccine's physiochemistry demonstrates that it is both antigenic and non-allergic. The vaccine structure was developed, enhanced, confirmed, and disulfide-engineered to provide the best possible model. Using molecular docking, the interaction of the produced vaccine with toll-like receptor 2 (TLR2) was analyzed, and the vaccine-receptor complex was stabilized by molecular dynamics (MD) simulation. According to in silico cloning, Escherichia coli can efficiently produce the recommended vaccine. Additionally, the efficacy of the in silico-developed vaccine must be evaluated in an in vitro and in vivo experiment. Results The developed vaccine successfully stimulates cellular and humoral immune responses. The vaccine, which has three B-cell epitopes, three HCL epitopes, and nine CTL epitopes, can bind firmly to immunological receptors. Dynamic investigations of the vaccine-receptor complex show a strong interaction and stable conformation. Conclusion In this study, the vaccine candidate demonstrated strong antigenicity, stability, and solubility while also being non-allergenic to host cells. The vaccine candidate's stability with the TLR2 immune receptor is established by binding studies, and in silico cloning verifies efficient and stable expression in the bacterial system.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.

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.

Immunoinformatics Design and In Vivo Immunogenicity Evaluation of a Conserved CTL Multi-Epitope Vaccine Targeting HPV16 E5, E6, and E7 Proteins.

Human papillomavirus type 16 (HPV16) infection is responsible for more than 50% of global cervical cancer cases. The development of a vaccine based on cytotoxic T-lymphocyte (CTL) epitopes is a promising strategy for eliminating pre-existing HPV infections and treating patients with cervical cancer. In this study, an immunoinformatics approach was used to predict HLA-I-restricted CTL epitopes in HPV16 E5, E6, and E7 proteins, and a set of conserved CTL epitopes co-restricted by human/murine MHCs was screened and characterized, with the set containing three E5, four E6, and four E7 epitopes. Subsequently, the immunogenicity of the epitope combination was assessed in mice, and the anti-tumor effects of the multi-epitope peptide vaccine E5E6E7pep11 and the recombinant protein vaccine CTB-Epi11E567 were evaluated in the TC-1 mouse tumor model. The results demonstrated that mixed epitope peptides could induce antigen-specific IFN-γ secretion in mice. Prophylactic immunization with E5E6E7pep11 and CTB-Epi11E567 was found to provide 100% protection against tumor growth in mice. Moreover, both types of the multi-epitope vaccine significantly inhibited tumor growth and prolonged mouse survival. In conclusion, in this study, a multi-epitope vaccine targeting HPV16 E5, E6, and E7 proteins was successfully designed and evaluated, demonstrating potential immunogenicity and anti-tumor effects and providing a promising strategy for immunotherapy against HPV-associated tumors.

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches.

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.

Identification of a new HLA-A*0201-restricted cytotoxic T lymphocyte epitope from TC2N.

Identification of cytotoxic T lymphocyte (CTL) epitopes from tumor related antigens is a promising approach for malignant tumor immunotherapy. TC2N, a recently identified tumor associated antigen from human glioblastoma, is regarded as a promising target of tumor-specific immunotherapy. As one of the most widely used histocompatibility molecules in Chinese is HLA-A*0201, we were able to identify the TC2N peptides that are provided by this molecular type. A panel of antigenic peptides produced from TC2N were predicted by using a computer tool. The binding affinities of three peptides with the highest predicted score to the HLA-A*0201 molecule were evaluated after synthesis. In vitro and in vivo stimulation of the main T-cell response against the predicted peptides. The results demonstrated that TC2N (152-160) was able to release IFN-γ and lyse U251 cells in vitro as well as in vivo by eliciting peptide-specific CTLs. Our results indicated that peptide TC2N (152-160) (RLYGSVCDL) was a novel HLA-A2.1-restricted CTL epitope capable of inducing TC2N specific CTLs in vitro. As TC2N might qualify as a viable target for immunotherapeutic approaches for patients with GBM, we speculated that the newly identified epitope RLYGSVCDL would be of potential use in peptide-based, cancer-specific immunotherapy against GBM.

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

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. 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. 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. 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.

Immunoinformatic Analysis of Leishmania Major gp46 Protein and Potential Targets for Vaccination against Leishmaniasis.

Cutaneous leishmaniasis (CL) is a parasitic disease with a significant burden in the Old World countries. In the current study, some of the primary biochemical properties and IFN-γ inducing epitopes with specific binding capacity to human and mouse MHC alleles were predicted for Leishmania major gp46 antigenic protein. Several online servers were used to predict physico-chemical traits, allergenicity, antigenicity, transmembrane domain and signal peptide, subcellular localization, post-translational modifications (PTMs), secondary and tertiary structures, tertiary model refining with validations. Also, IEDB web server was used to predict mouse/human cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes. The 33.25 kDa protein was stable, hydrophilic, antigenic, while non-allergenic, with enhanced thermotolerance and 45 PTM sites. The secondary structure encompassed a random coil, followed by extended strands and helices. Ramachandran-based analysis of the refined model showed 73.1%, 21.6%, 3.4% and 1.9% of residues in the most favored, additional allowed, generously-allowed and disallowed regions, respectively. Epitope screening demonstrated 4 HTL epitopes against seemingly protective HLA alleles, 5 HTL epitopes against the HLA reference set, 3 human CTL epitopes and a number of mouse MHC-restricted epitopes. This paper provides insights into the bioinformatics characteristics of the L. major gp46 protein as a promising vaccine candidate.

Designing of a Novel Candidate Multi-epitope Vaccine to boost Immune Responses against SARS‐COV‐2 using Immunoinformatics and Machine Learning based Approach

Background: The fast development of an effective vaccine is the major demand for protection against the SARS-COV-2 virus outbreak. With the vast volume of data and the requirement for automatic abstract property learning, machine learning (ML) as a branch of artificial intelligence (AI) has a significant contribution in areas of vaccine discovery. The rise of ML has greatly accelerated the often lengthy vaccine approval process. ML models for COVID-19 vaccine development focus on the prediction of potential epitopes by using a variety of techniques, such as artificial neural networks, gradient boosting decision trees and deep neural networks. : In this regard, immuno-informatics tools are time-saving and cost-effective methods to hasten the design and establishment of a proficient multi-peptide candidate vaccine. The utilization of multi-epitope-based vaccines has been demonstrated to be a promising immunization approach against viruses due to the induction of long-term protective immunity. Methods: In the present study, a comprehensive computational and machine learning based approach was conducted to design a multi-epitope-based potential candidate vaccine composed of cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes of conserved regions of Spike and Nucleocapsid proteins. The potential viral peptides as the candidate vaccine were screened regarding convenient features like hydrophilicity, flexibility, antigenicity, and charging properties. In the next step, the vaccine efficacy needs to be improved by an immune adjuvant. For this purpose, the C-terminal domain of the heat shock protein gp96 (CT-GP96) was applied as a potent adjuvant for enhancing immunity. The five assembled constructs with different peptide orders were generated and fused with the assistance of suitable linkers. All five assembled candidate vaccine constructs were modeled and their 3D structures were assessed in terms of strong immune responses stimulation and their structural stability and immune processing for response induction. Finally, the best refined model was docked to toll-like receptor 4 (TLR4). Furthermore, Molecular Dynamics (MD) simulation of the vaccine-receptor complex was done to assess the stability and related physical movements of the vaccine-TLR4 docking complex. The final candidate vaccine was computationally cloned in E. coli expression host to guarantee its high level of production. Results: Following a comprehensive immune-informatics and machine learning-based approach, the best conserved CTL and HTL immune stimulant epitopes were selected and assembled in different orders to build five different constructs. The final validated candidate vaccine construct was selected according to its efficacy, stability, and exposure ability, molecular docking analysis with TLR4. The molecular simulations by iMODS software also confirmed the stability of the binding interface. Additionally, the computational cloning of the final assembled candidate vaccine with pET28a plasmid showed the possibility of high level production of the vaccine construct post transformation in an E. coli host. Conclusion: The computational analysis indicated that this construct can be proposed as a potent prophylactic and therapeutic candidate multi-epitope vaccine against SARS-COV-2 once its effectiveness is verified by experimental and animal studies.

Potential SLA Hp-4.0 haplotype-restricted CTL epitopes identified from the membrane protein of PRRSV induce cell immune responses.

Swine leukocyte antigen (SLA) class I molecule-restricted T-cell epitopes, which induce cytotoxic T lymphocyte (CTL) responses, play a critical role in the clearance of porcine reproductive and respiratory syndrome virus (PRRSV) and the development of efficient protective vaccines. The SLA-1*04:01:01, SLA-2*04:01, and SLA-3*04:01 alleles, assigned the Hp-4.0 haplotype, are highly prevalent and usually present in all pig breeds. However, the SLA Hp-4.0 haplotype-restricted CTL epitopes in the structural membrane (M) protein of PRRSV are still unknown. In this study, we predicted 27 possible 9-mer epitope peptides in M protein with high binding scores for SLA-1*04:01:01 using CTL epitope prediction tools. In total, 45 SLA class I complexes, comprising the predicted peptide, extracellular region of the SLA-I molecules, and β2-microglobulin, were constructed in vitro to detect the specific binding of these peptides to SLA-1*04:01:01 (27 complexes), SLA-2*04:01 (9 complexes), and SLA-3*04:01 (9 complexes), respectively. Our results showed that the M27 (T91WKFITSRC), M39 (N130HAFVVRRP), and M49 (G158RKAVKQGV) peptides bind specifically to SLA-1*04:01:01, SLA-2*04:01, and SLA-3*04:01, respectively. Subsequently, using peripheral blood mononuclear cells (PBMCs) isolated from the homozygous Hp-4.0 and Hp-26.0 haplotype piglets vaccinated with commercial PRRSV HuN4-F112 strain, we determined the capacities of these 27 potential peptides to stimulate their proliferation with a Cell Counting Kit-8 and their secretion and expression of interferon gamma (IFN-γ) with an ELISpot assay and real-time qPCR, respectively. The immunological activities of M27, M39, and M49 were therefore confirmed when they efficiently induced PBMC proliferation and IFN-γ secretion in PBMCs from piglets with the prevalent SLA Hp-4.0 haplotype. The amino acid sequence alignment revealed that M27, M39, and M49 are highly conserved among 248 genotype II PRRSV strains collected between 1998 and 2019. These findings contribute to the understanding of the mechanisms of cell-mediated immune responses to PRRSV. Our study also provides a novel strategy for identifying and confirming potential SLA haplotype-restricted CTL epitopes that could be used to develop novel peptide-based vaccines against swine diseases.

Enzyme-Linked Immunosorbent Assay for Antibodies Against the Tumor-Associated Antigen-Derived Cytotoxic T-Lymphocyte Epitope.

Antibodies serve as crucial indicators of the immune system in clinical tests. In therapeutic cancer vaccines, IgG antibodies against target antigens are vital for immune monitoring. Additionally, assessing baseline antigen-specific immune responses before cancer vaccine administration is possible by measuring IgM and IgG antibodies against the target antigen. To this end, we have developed an enzyme-linked immunosorbent assay (ELISA) system that detects and quantifies serum levels of IgG and IgM antibodies against the WT1 cytotoxic T-lymphocyte epitope peptide. The assay immobilizes the epitope peptide in a microplate to capture antigen-specific antibodies. Here, this article presents the details of our ELISA system to detect and measure antibodies against a tumor-associated antigen-derived cytotoxic T-lymphocyte epitope with high reproducibility. Detecting these antibodies has novel significance in the context of emerging critical roles of B lineage-cells in tumor immunity.

Development of a multi-epitope vaccine candidate for leishmanial parasites applying immunoinformatics and in vitro approaches.

Leishmaniasis is a neglected tropical disease, and its severity necessitates the development of a potent and efficient vaccine for the disease; however, no human vaccine has yet been approved for clinical use. This study aims to design and evaluate a multi-epitope vaccine against the leishmanial parasite by utilizing helper T-lymphocyte (HTL), cytotoxic T-lymphocyte (CTL), and linear B-lymphocyte (LBL) epitopes from membrane-bound acid phosphatase of Leishmania donovani (LdMAcP). The designed multi-epitope vaccine (LdMAPV) was highly antigenic, non-allergenic, and non-toxic, with suitable physicochemical properties. The three-dimensional structure of LdMAPV was modeled and validated, succeeded by molecular docking and molecular dynamics simulation (MDS) studies that confirmed the high binding affinity and stable interactions between human toll-like receptors and LdMAPV. In silico disulfide engineering provided improved stability to LdMAPV, whereas immune simulation displayed the induction of both immune responses, i.e., antibody and cell-mediated immune responses, with a rise in cytokines. Furthermore, LdMAPV sequence was codon optimized and cloned into the pET-28a vector, followed by its expression in a bacterial host. The recombinant protein was purified using affinity chromatography and subjected to determine its effect on cytotoxicity, cytokines, and nitric oxide generation by mammalian macrophages. Altogether, this report provides a multi-epitope vaccine candidate from a leishmanial protein participating in parasitic virulence that has shown its potency to be a promising vaccine candidate against leishmanial parasites.

In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.

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

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

A new multi-epitope vaccine candidate based on S and M proteins is effective in inducing humoral and cellular immune responses against SARS-CoV-2 variants: an in silico design approach

Available COVID-19 vaccines are primarily based on SARS-CoV-2 spike protein (S). Due to the emergence of new SARS-CoV-2 variants, other virus proteins with more conservancy, such as Membrane (M) protein, are desired for vaccine development. The reverse vaccinology approach was employed to design a multi-epitope SARS-CoV-2 vaccine candidate based on S and M proteins. Cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), linear B-lymphocyte (LBL) and conformational B-lymphocyte (CBL) of S and M proteins were predicted and screened to choose the best epitopes. A multi-epitope vaccine candidate was constructed using selected CTL, HTL and LBL epitopes. The efficiency of the construct in binding to some immune receptors and an RBD-potent neutralizing monoclonal antibody (bebtelovimab) was predicted, and its immunogenicity was simulated. Finally, in silico cloning of the constructed gene was performed. The potency of our construct as a SARS-CoV-2 vaccine was validated using several bioinformatics tools. The simulation results showed that the construct can induce both cellular and humoral immune responses by producing appropriate cytokines, and it can even create an excellent immune memory response. Furthermore, the designed construct interacts with innate immune receptors such as TLR2 and TLR4 and the terminal variable domain of bebtelovimab with high affinity. We developed a multi-epitope construct based on the S and M proteins of the SARS-CoV-2 virus with high immunogenicity potential using the most up-to-date immunoinformatics and computational biology approaches. The actual efficiency of this multi-epitope vaccine should be further evaluated via in vitro and in vivo studies. Communicated by Ramaswamy H. Sarma

Coarse-grained molecular dynamics-guided immunoinformatics to explain the binder and non-binder classification of Cytotoxic T-cell epitope for SARS-CoV-2 peptide-based vaccine discovery.

Epitope-based peptide vaccine can elicit T-cell immunity against SARS-CoV-2 to clear the infection. However, finding the best epitope from the whole antigen is challenging. A peptide screening using immunoinformatics usually starts from MHC-binding peptide, immunogenicity, cross-reactivity with the human proteome, to toxicity analysis. This pipeline classified the peptides into three categories, i.e., strong-, weak-, and non-binder, without incorporating the structural aspect. For this reason, the molecular detail that discriminates the binders from non-binder is interesting to be investigated. In this study, five CTL epitopes against HLA-A*02:01 were identified from the coarse-grained molecular dynamics-guided immunoinformatics screening. The strong binder showed distinctive activities from the non-binder in terms of structural and energetic properties. Furthermore, the second residue from the nonameric peptide was most important in the interaction with HLA-A*02:01. By understanding the nature of MHC-peptide interaction, we hoped to improve the chance of finding the best epitope for a peptide vaccine candidate.

InflANNet: a neural network predictor for Influenza A CTL and HTL epitopes to aid robust vaccine design

BackgroundAn efficient and reliable data-driven method is essential to aid robust vaccine design, particularly in the case of an epidemic like Influenza A. Although various prediction tools are existing, most of them focus on the MHC-peptide binding affinity predictions. A tool which can incorporate more features other than binding affinity which characterizes the T-cell epitopes as vaccine candidates would be of much value in this scenario. The objective of this study is to develop two separate neural network models for the predictions of CTLs (cytotoxic T lymphocyte) and HTLs (helper T lymphocyte) with the manually curated datasets as a part of this study from the raw viral sequences of Influenza A.ResultsThe epitope datasets curated from the raw sequences of the broadly protective Neuraminidase protein were utilized for building and training the models for CTLs and HTLs. Each set consisted of nearly a balanced mix of vaccine candidates and non-vaccine candidates for both CTLs and HTLs. These were fed to neural networks as they are proven to be powerful for the predictions when compared with the other machine/deep learning algorithms. A set of epitopes experimentally proved were chosen to validate the model which was also tested through mutational analysis and cross-reactivity. The prepared dataset gave some valuable insights into the epitope distribution statistics and their conservancy in various outbreaks. An idea about the most probable range of peptide-MHC binding affinities was also obtained. Both the models performed well giving high accuracies when validated. These epitopes were checked for cross-reactivity with other antigens upon which it proved to be highly conservative and ideal for vaccine formulation.ConclusionsThe combination of various features and the resulting model efficiencies in turn proved that the collected features are valuable in the easy identification of the vaccine candidates. This suggests that our proposed models have more potential for conserved epitope prediction compared to other existing models trained on similar data and features. The possibility of refining the model with more set threshold values based on more parameters is an added feature that makes it more user driven. Furthermore, the uniqueness of the model due to exclusive set of Neuraminidase epitopes paves a robust way for rapid vaccine design.Graphical abstract