Development Of Peptide-based Vaccines Research Articles

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

BCEDB: a linear B-cell epitopes database forSARS-CoV-2.

The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection. However, there is still no public repository to host the linear B-cell epitopes for facilitating the development vaccines against SARS-CoV-2. Therefore, we developed BCEDB, a linear B-cell epitopes database specifically designed for hosting, exploring and visualizing linear B-cell epitopes and their features. The database provides a comprehensive repository of computationally predicted linear B-cell epitopes from Spike protein; a systematic annotation of epitopes including sequence, antigenicity score, genomic locations of epitopes, mutations in different virus lineages, mutation sites on the 3D structure of Spike protein and a genome browser to visualize them in an interactive manner. It represents a valuable resource for peptide-based vaccine development. Database URL: http://www.oncoimmunobank.cn/bcedbindex.

Experimental cystic echinococcosis as a proof of concept for the development of peptide-based vaccines following a novel rational workflow

Vaccines are among the most important advances in medicine throughout the human history. However, conventional vaccines exhibit several drawbacks in terms of design and production costs. Peptide-based vaccines are attractive alternatives, since they can be designed mainly in silico, can be produced cheaply and safely, and are able to induce immune responses exclusively towards protective epitopes. Yet, a proper peptide design is needed, not only to generate peptide-specific immune responses, but also for them to recognize the native protein in the occurrence of a natural infection. Herein, we propose a rational workflow for developing peptide-based vaccines including novel steps that assure the cross-recognition of native proteins. In this regard, we increased the probability of generating efficient antibodies through the selection of linear B-cell epitopes free of post-translational modifications followed by analyzing the 3D-structure similarity between the peptide in-solution vs. within its parental native protein. As a proof of concept, this workflow was applied to a set of seven previously suggested potential protective antigens against the infection by Echinococcus granulosus sensu lato. Finally, two peptides were obtained showing the capacity to induce specific antibodies able to exert anti-parasite activities in different in vitro settings, as well as to provide significant protection in the murine model of secondary echinococcosis.

Liposomal Formulations of a Polyleucine-Antigen Conjugate as Therapeutic Vaccines against Cervical Cancer.

Human papilloma virus (HPV) is responsible for all cases of cervical cancer. While prophylactic vaccines are available, the development of peptide-based vaccines as a therapeutic strategy is still under investigation. In comparison with the traditional and currently used treatment strategies of chemotherapy and surgery, vaccination against HPV is a promising therapeutic option with fewer side effects. A peptide derived from the HPV-16 E7 protein, called 8Qm, in combination with adjuvants showed promise as a therapeutic vaccine. Here, the ability of polymerized natural amino acids to act as a self-adjuvating delivery system as a therapeutic vaccine was investigated for the first time. Thus, 8Qm was conjugated to polyleucine by standard solid-phase peptide synthesis and self-assembled into nanoparticles or incorporated in liposomes. The liposome bearing the 8Qm conjugate significantly increased mice survival and decreased tumor growth after a single immunization. Further, these liposomes eradicated seven-day-old well-established tumors in mice. Dendritic cell (DC)-targeting moieties were introduced to further enhance vaccine efficacy, and the newly designed liposomal vaccine was tested in mice bearing 11-day-old tumors. Interestingly, these DCs-targeting moieties did not significantly improve vaccine efficacy, whereas the simple liposomal formulation of 8Qm-polyleucine conjugate was still effective in tumor eradication. In summary, a peptide-based anticancer vaccine was developed that stimulated strong cellular immune responses without the help of a classical adjuvant.

Experimental Validation of MHC Class I and II Peptide-Based Potential Vaccine Candidates for Human Papilloma Virus Using Sprague-Dawly Models.

Human papilloma virus (HPV) causes cervical and many other cancers. Recent trend in vaccine design is shifted toward epitope-based developments that are more specific, safe, and easy to produce. In this study, we predicted eight immunogenic peptides of CD4+ and CD8+ T-lymphocytes (MHC class I and II as M1 and M2) including early proteins (E2 and E6), major (L1) and minor capsid protein (L2). Male and female Sprague Dawly rats in groups were immunized with each synthetic peptide. L1M1, L1M2, L2M1, and L2M2 induced significant immunogenic response compared to E2M1, E2M2, E6M1 and E6M2. We observed optimal titer of IgG antibodies (>1.25 g/L), interferon-γ (>64 ng/L), and granzyme-B (>40 pg/mL) compared to control at second booster dose (240 µg/500 µL). The induction of peptide-specific IgG antibodies in immunized rats indicates the T-cell dependent B-lymphocyte activation. A substantial CD4+ and CD8+ cell count was observed at 240 µg/500 µL. In male and female rats, CD8+ cell count for L1 and L2 peptide is 3000 and 3118, and CD4+ is 3369 and 3484 respectively compared to control. In conclusion, we demonstrated that L1M1, L1M2, L2M1, L2M2 are likely to contain potential epitopes for induction of immune responses supporting the feasibility of peptide-based vaccine development for HPV.

Analysis of LruC lipoprotein and identification of peptides candidates for vaccine development and diagnosis of leptospirosis.

Leptospirosis is a public health concern with lethality around 15% of the total cases. The current vaccines against Leptospira infection based on bacterins have several limitations, which require urgent development of new ones. In this context, groundbreaking approaches such as peptide-vaccines could be used to come around with promising results. Our goal was to identify conserved and immunogenic epitopes from the lipoprotein LruC that could interact with Major Histocompatibility Complex (MHC) I and II. LruC is a conserved lipoprotein expressed during leptospirosis that is considered among vaccine candidates and can be used as source for development of peptide-based vaccines. We searched for peptides that would be recognized by antibodies from either serum of hamsters previously immunized with low-LPS bacterin vaccines or from serum of patients diagnosed with leptospirosis. Immuno properties of seven peptides from LruC protein were evaluated in silico and by Dot Blot assay, and validate by ELISA. Preliminary results pointed one promising peptide that was recognized by the sera. In conclusion, the immunoinformatic approach helps the search and screening of peptides, while the Dot Blot assay, a simple and effective tool, helps to test and validate them. Thus, these prospective techniques together were validated to identify and validate potential peptides for further investigation as peptide-based vaccines or diagnostic methods.

HLA Class I Supertype Classification Based on Structural Similarity.

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.

Potential Antigenic Candidates for the Development of Peptide-Based Vaccines to Induce Immunization against Helicobacter pylori Infection in BALB/c Mice.

Helicobacter pylori (H. pylori) has been identified as a group-1 definite carcinogen. As of yet, there is no available vaccine for this microorganism. Our study aimed to identify antigenic peptides in H. pylori using an in silico proteomic approach, and to evaluate their effectiveness as potential vaccine candidates. Four different peptide sequences were prioritized using the reverse vaccinology, namely, CagA1, CagA2, VacA, and SabA. Peptides emulsified with Freunde's adjuvant were used to immunize BALB/C mice. Subcutaneously immunized mice were challenged by oral administration of H. pylori. IgG, IgA, IL4, and IL17 were detected in mice sera. Histopathology of the dissected stomach of vaccinated and control mice were assessed using H&E stain. IgG was significantly higher in mice vaccinated with SabA. IL-4 was significantly increased in CagA1, CagA2, VacA, and SabA vaccinated mice compared to the adjuvant group. Additionally, histopathological examination of gastric tissue showed a protective effect in the vaccinated groups compared to adjuvant and PBS groups. Our findings indicate a promising effect of the tested epitopes, particularly the SabA antigen, to induce an immune response against H. pylori.

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes.

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

Detection of SARS-CoV-2 peptide-specific antibodies in Syrian hamster serum by ELISA

Golden Syrian hamsters are increasingly used as a permissive animal model for SARS-CoV-2 virus studies, but the lack of immunological assays and other immunological reagents for hamsters limits its full potential. Herein, we developed an ELISA method to detect antibodies specific to peptides and proteins derived from SARS-CoV-2 virus in immunized golden Syrian hamsters. Under optimized conditions, this assay quantitates antibodies specific for individual viral peptides, peptide pools, and proteins. Hence, this ELISA method allows investigators to quantitatively assess humoral immune responses at the peptide and protein levels and has potential application in the development of peptide-based vaccines to be tested in hamsters.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani.

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

TN strain proteome mediated therapeutic target mapping and multi-epitopic peptide-based vaccine development for Mycobacterium leprae.

Leprosy is a significant universal health problem that is remarkably still a concern in developing countries due to infection frequency. New therapeutic molecules and next-generation vaccines are urgently needed to accelerate the leprosy-free world. In this direction, the present study was performed using two routes: proteome-mediated therapeutic target identification and mapping as well as multi-epitopic peptide-based novel vaccine development using state of the art of computational biology for the TN strain of M. leprae. The TN strain was selected from 65 Mycobacterium strains, and TN strain proteome mediated 83 therapeutic protein targets were mapped and characterized according to subcellular localization. Also, drug molecules were mapped with respect to protein targets localization. The Druggability potential of proteins was also evaluated. For multi-epitope peptide-based vaccine development, the four common types of B and T cell epitopes were identified (SLFQSHNRK, VVGIGQHAA, MMHRSPRTR, LGVDQTQPV) and combined with the suitable peptide linker. The vaccine component had an acceptable protective antigenic score (0.9751). The molecular docking of vaccine components with TLR4/MD2 complex exhibited a low ACE value (-244.12) which signifies the proper binding between the two molecules. The estimated free Gibbs binding energy ensured accurate protein-protein interactions (-112.46kcal/mol). The vaccine was evaluated through adaptive immunity stimulation as well as immune interactions. The molecular dynamic simulation was carried out by using CHARMM topology-based parameters to minimize the docked complex. Subsequently, the Normal Mode Analysis in the internal coordinates showed a low eigen-value (1.3982892e-05), which also signifies the stability of molecular docking. Finally, the vaccine components were adopted for reverse transcription and codon optimization in E. coli strain K12 for the pGEX-4T1 vector, which supports in silico cloning of the vaccine components against the pathogen. The study directs the experimental study for therapeutics molecules discovery and vaccine candidate development with higher reliability.

Development of Peptide-Based Vaccines for Cancer.

Peptides cancer vaccines are designed based on the epitope peptides that can elicit humoral and cellular immune responses targeting tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). In order to develop a clinically safe and more effective vaccine for the future, several issues need to be addressed, and these include the selection of optimal antigen targets, adjuvants, and immunization regimens. Another emerging approach involves the use of personalized peptide-based vaccines based on neoantigens to enhance antitumor response. Rationally designed combinatorial therapy is currently being investigated with chemotherapeutic drugs or immune checkpoint inhibitor therapies to improve the efficacy. This review discusses an overview of the development of peptide-based vaccines, the role of adjuvants, and the delivery systems for peptide vaccines as well as combinatorial therapy as potential anticancer strategies.

Peptide-Based Vaccines for Neurodegenerative Diseases: Recent Endeavors and Future Perspectives.

The development of peptide-based vaccines for treating human neurodegenerative diseases has been the eventual aim of many research endeavors, although no active immunotherapies have been approved for clinical use till now. A typical example of such endeavors is the effort to develop vaccines for Alzheimer’s disease based on the beta-amyloid peptide, which continues to be intensively investigated despite previous setbacks. In this paper, recent developments in peptide-based vaccines which target beta-amyloid as well as tau protein and α-synuclein are presented. Particular focus has been directed toward peptide epitopes and formulation systems selected/developed and employed to enhance vaccine efficacy and safety. Results from both, human clinical trials and animal preclinical studies conducted mainly in transgenic mice have been included. Future perspectives on the topic are also briefly discussed.

"Immunoinformatic Identification of T-Cell and B-Cell Epitopes From Giardia lamblia Immunogenic Proteins as Candidates to Develop Peptide-Based Vaccines Against Giardiasis".

Giardiasis is one of the most common gastrointestinal infections worldwide, mainly in developing countries. The etiological agent is the Giardia lamblia parasite. Giardiasis mainly affects children and immunocompromised people, causing symptoms such as diarrhea, dehydration, abdominal cramps, nausea, and malnutrition. In order to develop an effective vaccine against giardiasis, it is necessary to understand the host-Giardia interactions, the immunological mechanisms involved in protection against infection, and to characterize the parasite antigens that activate the host immune system. In this study, we identify and characterize potential T-cell and B-cell epitopes of Giardia immunogenic proteins by immunoinformatic approaches, and we discuss the potential role of those epitopes to stimulate the host´s immune system. We selected the main immunogenic and protective proteins of Giardia experimentally investigated. We predicted T-cell and B-cell epitopes using immunoinformatic tools (NetMHCII and BCPREDS). Variable surface proteins (VSPs), structural (giardins), metabolic, and cyst wall proteins were identified as the more relevant immunogens of G. lamblia. We described the protein sequences with the highest affinity to bind MHC class II molecules from mouse (I-Ak and I-Ad) and human (DRB1*03:01 and DRB1*13:01) alleles, as well as we selected promiscuous epitopes, which bind to the most common range of MHC class II molecules in human population. In addition, we identified the presence of conserved epitopes within the main protein families (giardins, VSP, CWP) of Giardia. To our knowledge, this is the first in silico study that analyze immunogenic proteins of G. lamblia by combining bioinformatics strategies to identify potential T-cell and B-cell epitopes, which can be potential candidates in the development of peptide-based vaccines. The bioinformatics analysis demonstrated in this study provides a deeper understanding of the Giardia immunogens that bind to critical molecules of the host immune system, such as MHC class II and antibodies, as well as strategies to rational design of peptide-based vaccine against giardiasis.

Cell-Penetrating Peptides-Based Liposomal Delivery System Enhanced Immunogenicity of Peptide-Based Vaccine against Group A Streptococcus.

Peptide-based vaccine development represents a highly promising strategy for preventing Group A Streptococcus (GAS) infection. However, these vaccines need to be administered with the help of a delivery system and/or immune adjuvant. Cell-penetrating peptides (CPPs) have been used as a powerful tool for delivering various therapeutic agents, including peptides, as they can overcome the permeability barrier of cell membranes. Here, we used CPPs to deliver our lead lipopeptide-based vaccine (LCP-1). CPPs were anchored through a spacer to LCP-1-bearing multilamellar and unilamellar liposomes and administered to Swiss outbred mice. Tat47–57 conjugated to two palmitic acids via a (Gly)6 spacer (to form a liposome-anchoring moiety) was the most efficient system for triggering immune responses when combined with multilamellar liposomes bearing LCP-1. The immunostimulatory potential of a variety of other CPPs was examined following intranasal administration in mice. Among them, LCP-1/liposomes/Tat47–57 and LCP-1/liposomes/KALA induced the highest antibody titers. The antibodies produced showed high opsonic activity against clinically isolated GAS strains D3840 and GC2 203. The use of the CPP-liposome delivery system is a promising strategy for liposome-based GAS vaccine development.

Peptide-Based Vaccines: Foot-and-Mouth Disease Virus, a Paradigm in Animal Health.

Vaccines are considered one of the greatest global health achievements, improving the welfare of society by saving lives and substantially reducing the burden of infectious diseases. However, few vaccines are fully effective, for reasons ranging from intrinsic limitations to more contingent shortcomings related, e.g., to cold chain transport, handling and storage. In this context, subunit vaccines where the essential antigenic traits (but not the entire pathogen) are presented in rationally designed fashion have emerged as an attractive alternative to conventional ones. In particular, this includes the option of fully synthetic peptide vaccines able to mimic well-defined B- and T-cell epitopes from the infectious agent and to induce protection against it. Although, in general, linear peptides have been associated to low immunogenicity and partial protection, there are several strategies to address such issues. In this review, we report the progress towards the development of peptide-based vaccines against foot-and-mouth disease (FMD) a highly transmissible, economically devastating animal disease. Starting from preliminary experiments using single linear B-cell epitopes, recent research has led to more complex and successful second-generation vaccines featuring peptide dendrimers containing multiple copies of B- and T-cell epitopes against FMD virus or classical swine fever virus (CSFV). The usefulness of this strategy to prevent other animal and human diseases is discussed.

Localization and function of a Plasmodium falciparum protein (PF3D7_1459400) during erythrocyte invasion

Plasmodium falciparum malaria is a global health problem. Erythrocyte invasion by P. falciparum merozoites appears to be a promising target to curb malaria. We have identified and characterized a novel protein that is involved in erythrocyte invasion. Our data on protein subcellular localization, stage-specific protein expression pattern, and merozoite invasion inhibition by α-peptide antibodies suggest a role for PF3D7_1459400 protein during P. falciparum erythrocyte invasion. Even more, the human immunoepidemiology data present PF3D7_1459400 protein as an immunogenic antigen which could be further exploited for the development of new anti-infective therapy against malaria.

Medicinal Chemistry and Methodological Advances in the Development of Peptide-Based Vaccines.

The evolution of rapidly proliferating infectious and tumorigenic diseases has resulted in an urgent need to develop new and improved intervention strategies. Among the many therapeutic strategies at our disposal, our immune system remains the gold-standard in disease prevention, diagnosis, and treatment. Vaccines have played an important role in eradicating or mitigating the spread of infectious diseases by bolstering our immunity. Despite their utility, the design and development of new, more effective vaccines remains a public health necessity. Peptide-based vaccines have been developed for a wide range of established and emerging infectious and tumorigenic diseases. New innovations in epitope design and selection, synthesis, and formulation as well as screening techniques against immunological targets have led to more effective peptide vaccines. Current and future work is geared toward the translation of peptide vaccines from preclinical to clinical utility.

Identification of Novel Candidate Epitopes on SARS-CoV-2 Proteins for South America: A Review of HLA Frequencies by Country.

Coronavirus disease (COVID-19), caused by the virus SARS-CoV-2, is already responsible for more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020. Ongoing efforts to control the pandemic include the development of peptide-based vaccines and diagnostic tests. In these approaches, HLA allelic diversity plays a crucial role. Despite its importance, current knowledge of HLA allele frequencies in South America is very limited. In this study, we have performed a literature review of datasets reporting HLA frequencies of South American populations, available in scientific literature and/or in the Allele Frequency Net Database. This allowed us to enrich the current scenario with more than 12.8 million data points. As a result, we are presenting updated HLA allelic frequencies based on country, including 91 alleles that were previously thought to have frequencies either under 5% or of an unknown value. Using alleles with an updated frequency of at least ≥5% in any South American country, we predicted epitopes in SARS-CoV-2 proteins using NetMHCpan (I and II) and MHC flurry. Then, the best predicted epitopes (class-I and -II) were selected based on their binding to South American alleles (Coverage Score). Class II predicted epitopes were also filtered based on their three-dimensional exposure. We obtained 14 class-I and four class-II candidate epitopes with experimental evidence (reported in the Immune Epitope Database and Analysis Resource), having good coverage scores for South America. Additionally, we are presenting 13 HLA-I and 30 HLA-II novel candidate epitopes without experimental evidence, including 16 class-II candidates in highly exposed conserved areas of the NTD and RBD regions of the Spike protein. These novel candidates have even better coverage scores for South America than those with experimental evidence. Finally, we show that recent similar studies presenting candidate epitopes also predicted some of our candidates but discarded them in the selection process, resulting in candidates with suboptimal coverage for South America. In conclusion, the candidate epitopes presented provide valuable information for the development of epitope-based strategies against SARS-CoV-2, such as peptide vaccines and diagnostic tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.