Development Of Peptide Vaccines Research Articles

FGL2172-220 peptides improve the antitumor effect of HCMV-IE1mut vaccine against glioblastoma by modulating immunosuppressive cells in the tumor microenvironment

ABSTRACT Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor characterized by poor prognosis and lack of effective treatments. In recent years, peptide vaccines that use sequences based on tumor-specific or tumor-associated antigens to activate immune responses against tumor cells have emerged as a new therapeutic strategy. In this study, we developed a novel therapeutic polypeptide vaccine targeting the tumor-associated antigen Fibrinogen-Like Protein 2 (FGL2), whose dominant epitope peptide was tandemly linked to the C-terminus of HCMV-IE1mut via a linker. We used this vaccine to compare the therapeutic efficacy of HCMV-IE1mut alone versus HCMV-IE1mut-FGL2172-220 and investigate the potential mechanism of action of HCMV-IE1mut-FGL2172-220 in glioma treatment. An in situ GBM model (GL261-IE1-luc cells) was used to determine the efficacy of the vaccine. Treatment with HCMV-IE1mut-FGL2172-220 exerted antitumor effects and extended the survival of the GL261 animal model. We observed reduced proportions of microglia, regulatory T cells (Treg), and myeloid-derived suppressor cells (MDSC) in the tumor microenvironment (TME) by immunofluorescence. Flow cytometry showed that compared to HCMV-IE1mut alone, treatment with HCMV-IE1mut-FGL2172-220 increased the proportion of CD8+ T cells and tissue-resident memory T cells (TRM). ELISA analysis showed that it improved the secretion of tumor-specific IFN-γ and TNF-α by these cells and downregulated the expression of IL-6 and IL-10. Our study demonstrates that the long-peptide FGL2172-220 improves the antitumor efficacy of HCMV-IE1mut, possibly by reshaping immune cells in the glioma microenvironment. These findings lay the groundwork for the development of therapeutic antigenic peptide vaccines to improve antitumor effects for cancer.

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 for potential epitopes prediction. The epitopes for B-cells, helper T-lymphocytes (HTL), and Cytotoxic Tlymphocytes (CTL) were 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 selected from the predicted epitope. The designed multi-epitope construct was potentially immunogenic, non-toxic, and non-allergenic. 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.

Optimizing sheep B-cell epitopes in Echinococcus granulosus recombinant antigen P29 for vaccine development.

Echinococcus granulosus is a widespread zoonotic parasitic disease, significantly impacting human health and livestock development; however, no vaccine is currently available for humans. Our preliminary studies indicate that recombinant antigen P29 (rEg.P29) is a promising candidate for vaccine. Sheep were immunized with rEg.P29, and venous blood was collected at various time points. Serum was isolated, and the presence of specific antibodies was detected using ELISA. We designed and synthesized a total of 45 B cell monopeptides covering rEg.P29 using the overlap method. ELISA was employed to assess the serum antibodies of the immunized sheep for recognition of these overlapping peptides, leading to the preliminary identification of B cell epitopes. Utilizing these identified epitopes, new single peptides were designed, synthesized, and used to optimize and confirm B-cell epitopes. rEg.P29 effectively induces a sustained antibody response in sheep, particularly characterized by high and stable levels of IgG. Eight B-cell epitopes of were identified, which were mainly distributed in three regions of rEg.P29. Finally, three B cell epitopes were identified and optimized: rEg.P2971-90, rEg.P29151-175, and rEg.P29211-235. These optimized epitopes were well recognized by antibodies in sheep and mice, and the efficacy of these three epitopes significantly increased when they were linked in tandem. Three B-cell epitopes were identified and optimized, and the efficacy of these epitopes was significantly enhanced by tandem connection, which indicated the feasibility of tandem peptide vaccine research. This laid a solid foundation for the development of epitope peptide vaccine for Echinococcus granulosus.

The Role and Development of Peptide Vaccines in Cervical Cancer Treatment

The Role and Development of Peptide Vaccines in Cervical Cancer Treatment

A Proof-of-Concept Study to Develop a Peptide-Based Vaccine against Salmon Lice Infestation in Atlantic Salmon (Salmo salar L.).

Proteins present in blood samples from Atlantic salmon (Salmo salar) infected with salmon lice (Lepeophtheirus salmonis) were analyzed using liquid chromatography-high-resolution mass spectrometry. Bioinformatic analyses revealed 1820 proteins, of which 58 were assigned to lice. Among these, peroxiredoxin-2, an antioxidant protein, was found relevant with respect to blood feeding of the parasite. The three-dimensional structure analysis of the protein revealed a surface amino acid sequence of interest. A 13-amino-acid peptide was selected as a potential antigen due to its predicted solubility, antigenicity, probable non-allergenic, and non-toxic nature. This peroxiredoxin-2-derived peptide was synthesized, combined with a commercially available adjuvant, and used for vaccination. The test vaccine demonstrated a 60-70% protection rate against early-stage Lepeophtheirus salmonis infection in a challenge trial in Norway. Additionally, the vaccine was tested against salmon lice (Caligus rogercresseyi) in Chile, where a remarkable 92% reduction in the number of adult lice was observed. Thus, in combination with the selected adjuvant, the peptide showed antigenic potential, making it a suitable candidate for future vaccine development. The approach described holds promise for the development of peptide vaccines against various ectoparasites feeding on blood or skin secretions of their hosts.

Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings

Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT.

Prediction of the 3D conformation of a small peptide vaccine targeting Aβ42 oligomers.

The original etiology of Alzheimer's disease (AD) is the deposition of amyloid-beta (Aβ) proteins, which starts from the aggregation of the Aβ oligomers. The optimal therapeutic strategy targeting Aβ oligomer aggregation is the development of AD vaccines. Despite the fact that positive progress has been made for experimental attempts at AD vaccines, the physicochemical and even structural properties of these AD vaccines remain unclear. In this study, through immunoinformatic and molecular dynamics (MD) simulations, we first designed and simulated an alternative of vaccine TAPAS and found that the structure of the alternative can reproduce the 3D conformation of TAPAS determined experimentally. Meanwhile, immunoinformatic methods were used to analyze the physicochemical properties of TAPAS, including immunogenicity, antigenicity, thermal stability, and solubility, which confirm well the efficacy and safety of the vaccine, and validate the scheme reliability of immunoinformatic and MD simulations in designing and simulating the TAPAS vaccine. Using the same scheme, we predicted the 3D conformation of the optimized ACI-24 peptide vaccine, an Aβ peptide with the first 15 residues of Aβ42 (Aβ1-15). The vaccine was verified once to be effective against both full-length Aβ1-42 and truncated Aβ4-42 aggregates, but an experimental 3D structure was absent. We have also explored the immune mechanism of the vaccine at the molecular level and found that the optimized ACI-24 and its analogues can block the growth of either full-length Aβ1-42 or truncated Aβ4-42 pentamer by contacting the hydrophobic residues within the N-terminus and β1 region on the contact surface of either pentamer. Additionally, residues (D1, D7, S8, H13, and Q15) were identified as the key residues of the vaccine to contact either of the two Aβ oligomers. This work provides a feasible implementation scheme of immunoinformatic and MD simulations for the development of AD small peptide vaccines, validating the power of the scheme as a parallel tool to the experimental approaches and injecting molecular-level information into the understanding and design of anti-AD vaccines.

Screening and identification of B cell epitope within the major capsid protein L1 of HPV 52, using monoclonal antibodies

The L1 protein of Human papillomavirus (HPV), the main capsid protein, induces the formation of neutralizing antibodies. In this study, HPV52 L1 protein was induced to be expressed. Monoclonal antibody (mAb) 6A7 against L1 protein were screened by cell fusion techniques. Western Blot and immunofluorescence assay (IFA) demonstrated the specificity of the mAb. The L1 protein was truncated for prokaryotic expression (N1∼N7) and Dot-ELISA showed that 6A7 recognized N3 (aa 200–350). The immunodominant regions were truncated again for expression, with 6A7 recognizing N6 (aa 251–305). The N6 proteins were further truncated and then were constructed an four-segment eukaryotic expression vector. IFA showed that 6A7 could recognize amino acid 262–279. Amino acid 262–279 was selected to be truncated into short peptides P1 and P2. Finally, Peptide-ELISA and Dot-ELISA showed that the epitope regions of mAb 6A7 were amino acid 262–273. The mAbs with defined epitopes can lay the foundation for the analysis of antigenic epitope characteristics and promote the development of epitope peptide vaccines.

Immunoinformatics and Evaluation of Peptide Vaccines Derived from Global Hepatitis B Viral HBx and HBc Proteins Critical for Covalently Closed Circular DNA Integrity.

The Hepatitis B virus (HBV) HBx and HBc proteins play a crucial role in associating with covalently closed circular DNA (cccDNA), the primary factor contributing to intrahepatic viral persistence and a major obstacle in achieving a cure for HBV. The cccDNA serves as a reservoir for viral persistence. Targeting the viral HBc and HBx proteins' interaction with cccDNA could potentially limit HBV replication. In this study, we present epitopes identified from global consensus sequences of HBx and HBc proteins that have the potential to serve as targets for the development of effective vaccine candidates. Furthermore, conserved residues identified through this analysis can be utilized in designing novel, site-specific anti-HBV agents capable of targeting all major genotypes of HBV. Our approach involved designing global consensus sequences for HBx and HBc proteins, enabling the analysis of variable regions and highly conserved motifs. These identified motifs and regions offer potent sites for the development of peptide vaccines, the design of site-specific RNA interference, and the creation of anti-HBV inhibitors. The epitopes derived from global consensus sequences of HBx and HBc proteins emerge as promising targets for the development of effective vaccine candidates. Additionally, the conserved residues identified provide valuable insights for the development of innovative, site-specific anti-HBV agents capable of targeting all major genotypes of HBV from A to J.

The Implementation of Response Surface Methodology in the Optimization of Lipid Nanoparticle Preparation for Vaccine Development

Lipid nanoparticles (LNPs) are the delivery system behind the success story of mRNA vaccines and the development of DNA and peptide vaccines. LNPs should have particle sizes ranging from 60 to 150 nm for such applications. Achieving such particle size criteria can be challenging in the LNP preparation using a micromixer system with different parameters: i.e., the N/P ratio, aqueous flow rate (AFR) and total flow rate (TFR). Such an issue can be addressed by applying response surface methodology (RSM). Therefore, in this study, we implemented RSM to optimize LNP preparation parameters, including the N/P ratio and AFR. TFR remained constant at 1 mL.min–1 since, from a previous study, it is not a critical parameter for achieving the desired LNP size. Here, we used the pDNA of ACE-2 as a dummy nucleotide. Particle sizes of the resulting LNPs were determined using a dynamic light scattering (DLS) method. Our results indicate that AFR is a significant factor in determining LNP size, whereas the N/P ratio does not have a significant impact on LNP size. We achieved an LNP size of 104.00 nm by applying the N/P ratio of 32.0000 and AFR of 0.9621 mL.min–1. Our results showed that RSM could ease the optimization process in the preparation of LNP, which may support the acceleration of vaccine development. HIGHLIGHTS The significance of lipid nanoparticles (LNPs) as critical delivery systems for mRNA vaccines, DNA vaccines, and peptide vaccines is highlighted The study focuses on addressing the challenge of achieving the desired particle size range during LNP preparation Response surface methodology (RSM) was employed to optimize the N/P ratio and aqueous flow rate for efficient LNP production The study identifies the aqueous flow rate (AFR) as a key factor influencing the size of LNPs Overall, the findings of this study contribute to accelerating vaccine development by streamlining the preparation of LNPs GRAPHICAL ABSTRACT

Peptide Vaccines as Therapeutic and Prophylactic Agents for Female-Specific Cancers: The Current Landscape.

Breast and gynecologic cancers are significant global threats to women's health and those living with the disease require lifelong physical, financial, and social support from their families, healthcare providers, and society as a whole. Cancer vaccines offer a promising means of inducing long-lasting immune response against the disease. Among various types of cancer vaccines available, peptide vaccines offer an effective strategy to elicit specific anti-tumor immune responses. Peptide vaccines have been developed based on tumor associated antigens (TAAs) and tumor specific neoantigens which can also be of viral origin. Molecular alterations in HER2 and non-HER2 genes are established to be involved in the pathogenesis of female-specific cancers and hence were exploited for the development of peptide vaccines against these diseases, most of which are in the latter stages of clinical trials. However, prophylactic vaccines for viral induced cancers, especially those against Human Papillomavirus (HPV) infection are well established. This review discusses therapeutic and prophylactic approaches for various types of female-specific cancers such as breast cancer and gynecologic cancers with special emphasis on peptide vaccines. We also present a pipeline for the design and evaluation of a multiepitope peptide vaccine that can be active against female-specific cancers.

Accelerating SARS-CoV-2 Vaccine Development: Leveraging Novel Hybrid Deep Learning Models and Bioinformatics Analysis for Epitope Selection and Classification

It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on the precise design of epitope regions that can elicit a strong immune response. Choosing epitope regions experimentally for the production of the SARS-CoV-2 vaccine can be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based on machine learning to find these regions, to cut down the number of candidate epitopes that might be tested in experiments, and, as a result, to lessen the time-consuming process of their mapping. However, the tools and approaches involved continue to have low accuracy. In this work, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for the classification of peptides into epitopes or non-epitopes. Numerous transfer learning strategies were utilized, and the fine-tuned method gave the best result, with an AUC of 0.979, an f1 score of 0.902, and 95.1% accuracy, which was far better than the performance of the model trained from scratch. The experimental results obtained show that this model has superior performance when compared to other methods trained on IEDB datasets. Using bioinformatics tools such as ToxinPred, VaxiJen, and AllerTop2.0, the toxicities, antigenicities, and allergenicities, respectively, of the predicted epitopes were determined. In silico cloning and codon optimization were used to successfully express the vaccine in E. coli. This work will help scientists choose the best epitope for the development of the COVID-19 vaccine, reducing cost and labor and thereby accelerating vaccine production.

Abstract LB168: Efficacy analysis of the DeepNeo neoantigen prediction tool for antitumor vaccine development

Abstract Antitumor peptide vaccine is an alternative and attractive way to eradicate tumors by the aid of host immune system. The current peptide vaccine development is hampered by several hurdles. These include heterogeneity of tumor cells, applicable in vivo stability of peptides, and others. One of the major obstacles is the precise prediction of cancer antigen, which can be loaded efficiently onto antigen presenting cells. We present here a neoantigen prediction algorithm DeepNeo comprised of DeepNeo-MHC and DeepNeo-TCR, which calculates the antigenicity of neoantigen based on the MHC binding and TCR activation potential. We examined the efficacy of DeepNeo using ELISPOT and two allogenic mouse tumor models. By treating the combination of long peptides targeting MC38 colon or B16F10 melanoma cell lines, we have shown the DeepNeo effectively determines neoantigens for the development of antitumor peptide vaccine. Analysis of tumor-infiltrated lymphocytes (TIL) supported our results. Undergoing study using patient-derived xenograft model treated with autologous, trained PBMC with personalized peptide vaccine will be discussed further. Citation Format: Eun Ji Lee, Min Ji Park, Soo Jin Kim, Seung-Jae Noh, Inkyung Shin, Jeong Yeon Kim, Incheol Shin, Jung Kyoon Choi, Dae-Yeon Cho, Suhwan Chang. Efficacy analysis of the DeepNeo neoantigen prediction tool for antitumor vaccine development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB168.

A Synthetic SARS-CoV-2-Derived T-Cell and B-Cell Peptide Cocktail Elicits Full Protection against Lethal Omicron BA.1 Infection in H11-K18-hACE2 Mice.

Emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developing the capacity for immune evasion and resistance to existing vaccines and drugs. To address this, development of vaccines against coronavirus disease 2019 (COVID-19) has focused on universality, strong T cell immunity, and rapid production. Synthetic peptide vaccines, which are inexpensive and quick to produce, show low toxicity, and can be selected from the conserved SARS-CoV-2 proteome, are promising candidates. In this study, we evaluated the effectiveness of a synthetic peptide cocktail containing three murine CD4+ T-cell epitopes from the SARS-CoV-2 nonspike proteome and one B-cell epitope from the Omicron BA.1 receptor-binding domain (RBD), along with aluminum phosphate (Al) adjuvant and 5' cytosine-phosphate-guanine 3' oligodeoxynucleotide (CpG-ODN) adjuvant in mice. The peptide cocktail induced good Th1-biased T-cell responses and effective neutralizing-antibody titers against the Omicron BA.1 variant. Additionally, H11-K18-hACE2 transgenic mice were fully protected against lethal challenge with the BA.1 strain, with a 100% survival rate and reduced pulmonary viral load and pathological lesions. Subcutaneous administration was found to be the superior route for synthetic peptide vaccine delivery. Our findings demonstrate the effectiveness of the peptide cocktail in mice, suggesting the feasibility of synthetic peptide vaccines for humans. IMPORTANCE Current vaccines based on production of neutralizing antibodies fail to prevent the infection and transmission of SARS-CoV-2 Omicron and its subvariants. Understanding the critical factors and avoiding the disadvantages of vaccine strategies are essential for developing a safe and effective COVID-19 vaccine, which would include a more effective and durable cellular response, minimal effects of viral mutations, rapid production against emerging variants, and good safety. Peptide-based vaccines are an excellent alternative because they are inexpensive, quick to produce, and very safe. In addition, human leukocyte antigen T-cell epitopes could be targeted at robust T-cell immunity and selected in the conserved region of the SARS-CoV-2 variants. Our study showed that a synthetic SARS-CoV-2-derived peptide cocktail induced full protection against lethal infection with Omicron BA.1 in H11-K18-hACE2 mice for the first time. This could have implications for the development of effective COVID-19 peptide vaccines for humans.

Biochemical detection system for intermediates of endogenous antigen and elucidation of the role of molecular chaperones

Cytotoxic T lymphocytes recognize antigen-derived peptides (epitopes) bound to MHC class I presented on the cell surface of virus-infected cells and cancer cells. To date, numerous pathogen-derived epitopes and cancer cell-specific epitopes have been identified and used in the development of mRNA and peptide vaccines, but much remains unknown regarding the intracellular mechanisms that generate these antigen epitopes. These mechanisms are essential for cytotoxic T cell immunity. In this paper, I outline an innovation pioneered by Professor Nilabh Shastri and me, in which we developed a biochemical system to detect antigen intermediates and illuminated the role of molecular chaperones in antigen processing.

NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes

Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.

Bioinformatics-based SARS-CoV-2 epitopes design and the impact of spike protein mutants on epitope humoral immunities

BackgroundEpitope selection is the key to peptide vaccines development. Bioinformatics tools can efficiently improve the screening of antigenic epitopes and help to choose the right ones. ObjectiveTo predict, synthesize and testify peptide epitopes at spike protein, assess the effect of mutations on epitope humoral immunity, thus provide clues for the design and development of epitope peptide vaccines against SARS-CoV-2. MethodsBioinformatics servers and immunological tools were used to identify the helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes on the S protein of SARS-CoV-2. Physicochemical properties of candidate epitopes were analyzed using IEDB, VaxiJen, and AllerTOP online software. Three candidate epitopes were synthesized and their antigenic responses were evaluated by binding antibody detection. ResultsA total of 20 antigenic, non-toxic and non-allergenic candidate epitopes were identified from 1502 epitopes, including 6 helper T-cell epitopes, 13 cytotoxic T-cell epitopes, and 1 linear B cell epitope. After immunization with antigen containing candidate epitopes S206-221, S403-425, and S1157-1170 in rabbits, the binding titers of serum antibody to the corresponding peptide, S protein, receptor-binding domain protein were (415044, 2582, 209.3), (852819, 45238, 457767) and (357897, 10528, 13.79), respectively. The binding titers to Omicron S protein were 642, 12,878 and 7750, respectively, showing that N211L, DEL212 and K417N mutations cause the reduction of the antibody binding activity. ConclusionsBioinformatic methods are effective in peptide epitopes design. Certain mutations of the Omicron would lead to the loss of antibody affinity to Omicron S protein.

Layer-by-Layer Delivery of Multiple Antigens Using Trimethyl Chitosan Nanoparticles as a Malaria Vaccine Candidate.

Developing a safe and effective malaria vaccine is critical to reducing the spread and resurgence of this deadly disease, especially in children. In recent years, vaccine technology has seen expanded development of subunit protein, peptide, and nucleic acid vaccines. This is due to their inherent safety, the ability to tailor their immune response, simple storage requirements, easier production, and lower expense compared to using attenuated and inactivated organism-based approaches. However, these new vaccine technologies generally have low efficacy. Subunit vaccines, due to their weak immunogenicity, often necessitate advanced delivery vectors and/or the use of adjuvants. A new area of vaccine development involves design of synthetic micro- and nano-particles and adjuvants that can stimulate immune cells directly through their physical and chemical properties. Further, the unique and complex life cycle of the Plasmodium organism, with multiple stages and varying epitopes/antigens presented by the parasite, is another challenge for malaria vaccine development. Targeting multistage antigens simultaneously is therefore critical for an effective malaria vaccine. Here, we rationally design a layer-by-layer (LbL) antigen delivery platform (we called LbL NP) specifically engineered for malaria vaccines. A biocompatible modified chitosan nanoparticle (trimethyl chitosan, TMC) was synthesized and utilized for LbL loading and release of multiple malaria antigens from pre-erythrocytic and erythrocytic stages. LbL NP served as antigen/protein delivery vehicles and were demonstrated to induce the highest Plasmodium falciparum Circumsporozoite Protein (PfCSP) specific T-cell responses in mice studies as compared to multiple controls. From immunogenicity studies, it was concluded that two doses of intramuscular injection with a longer interval (4 weeks) than traditional malaria vaccine candidate dosing would be the vaccination potential for LbL NP vaccine candidates. Furthermore, in PfCSP/Py parasite challenge studies we demonstrated protective efficacy using LbL NP. These LbL NP provided a significant adjuvant effect since they may induce innate immune response that led to a potent adaptive immunity to mediate non-specific anti-malarial effect. Most importantly, the delivery of CSP full-length protein stimulated long-lasting protective immune responses even after the booster immunization 4 weeks later in mice.

Current Peptide Vaccine and Immunotherapy Approaches Against Alzheimer's Disease.

Peptide vaccines and immunotherapies against aggregating proteins involved in the pathogenesis and progression of Alzheimer's disease (AD) - the β-amyloid peptide (Aβ) and tau - are promising therapeutic avenues against AD. Two decades of effort has led to the controversial FDA approval of the monoclonal antibody Aducanumab (Aduhelm), which has subsequentially sparked the revival and expedited review of promising monoclonal antibody immunotherapies that target Aβ. In this review, we explore the development of Aβ and tau peptide vaccines and immunotherapies with monoclonal antibodies in clinical trials against AD.

Identification of B-Cell Epitopes for Eliciting Neutralizing Antibodies against the SARS-CoV-2 Spike Protein through Bioinformatics and Monoclonal Antibody Targeting.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global public health crisis. Effective COVID-19 vaccines developed by Pfizer-BioNTech, Moderna, and Astra Zeneca have made significant impacts in controlling the COVID-19 burden, especially in reducing the transmission of SARS-CoV-2 and hospitalization incidences. In view of the emergence of new SARS-CoV-2 variants, vaccines developed against the Wuhan strain were less effective against the variants. Neutralizing antibodies produced by B cells are a critical component of adaptive immunity, particularly in neutralizing viruses by blocking virus attachment and entry into cells. Therefore, the identification of protective linear B-cell epitopes can guide epitope-based peptide designs. This study reviews the identification of SARS-CoV-2 B-cell epitopes within the spike, membrane and nucleocapsid proteins that can be incorporated as potent B-cell epitopes into peptide vaccine constructs. The bioinformatic approach offers a new in silico strategy for the mapping and identification of potential B-cell epitopes and, upon in vivo validation, would be useful for the rapid development of effective multi-epitope-based vaccines. Potent B-cell epitopes were identified from the analysis of three-dimensional structures of monoclonal antibodies in a complex with SARS-CoV-2 from literature mining. This review provides significant insights into the elicitation of potential neutralizing antibodies by potent B-cell epitopes, which could advance the development of multi-epitope peptide vaccines against SARS-CoV-2.