Design Of Peptide-based Vaccines Research Articles

Abstract 4090: Gut microbiota mimics as a source of cross-reactive tumor rejection antigens

Abstract The crosstalk between gut microbiome and anti-tumor immunity has recently gained attention in clinical settings. The administration of antibiotics during immune checkpoint blockade (ICB) therapies has been correlated with poorer outcomes in cancer patients, suggesting that microbiome alterations affect overall responses to ICB. The establishment of microbiota-specific T cell clones with tumor cross-reactivity potential was proposed as one of the mechanisms linking the microbiome to anti-tumor responses. Vaccine-based strategies aimed at reactivating these dormant pools of T cells to help eliminate tumors have been proposed as an alternate approach, but the parameters required for successful responses remain poorly understood. Here, we performed in silico selection of bacterial peptides derived from healthy mice based on their mimicry of either tumor-associated antigens (TAAs) or neoantigens expressed by murine melanoma cells and not healthy tissue. We show that in silico predicted peptide binding affinity to MHC I correlates with experimental MHC I binding, a key factor associated with strong immune responses. Moreover, we confirmed that a significant proportion of selected peptide candidates can provoke an immune reaction in a vaccination setting. From this group of peptides, we selected candidates which triggered cross-reactive immune responses against TAAs or neoantigens. Data from preliminary tumor challenge experiments with prophylactic peptide vaccination and ICB allowed us to define key parameters such as target gene expression levels which can predict the cross-reactive potential of bacterial peptides that mimic tumor antigens. These findings provide novel insights into the design of gut microbiome peptide-based tumor vaccines and pave the way for establishing combinatorial therapies with immunomodulatory drugs. Citation Format: Vincent Panneton, Mathieu Gigoux, Abderezak Zebboudj, Norah Sadek, Hyejin Choi, Levi M. Mangarin, Mariam M. George, Guillaume Kulakowski, Joao G. Magalhaes, Jean-Marie Carpier, Thibaud Dugat, Camille Gaal, Francesco Strozzi, Lorenzo Tibaldi, Laurent Chene, Jedd D. Wolchok, Vinod P. Balachandran, Taha Merghoub. Gut microbiota mimics as a source of cross-reactive tumor rejection antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4090.

RNA Origami Functions as a Self-Adjuvanted Nanovaccine Platform for Cancer Immunotherapy.

Peptide-based vaccines have been widely investigated in cancer immunotherapy. Despite their high specificity, safety, and low production cost, these vaccines have shown limited success in clinical studies, owing to their poor immunogenicity. Extensive efforts have been devoted to increasing the immunogenicity of peptide vaccines by mixing peptides with adjuvants and/or promoting their delivery to tumor-draining lymph nodes (TdLNs) for better antigen presentation by and maturation of dendritic cells. Among these efforts, the exploration of various nanoparticles has been at the forefront of the rational design and construction of peptide-based vaccines. Here, we present a nanovaccine platform that is built on a self-assembled RNA origami (RNA-OG) nanostructure. As previously reported, this RNA-OG nanostructure is a potent toll-like receptor (TLR)3 agonist. In addition, due to its robust synthesis and versatility in modification, RNA-OG could be readily linked to peptides of interest. Thus, these RNA-OG nanostructures function as adjuvanted nanocarriers to construct RNA-OG-peptide nanovaccines that are uniform in size, consistent in peptide loading, and highly stable. Here, we demonstrate that the assembled RNA-OG-peptide nanovaccines induced dendritic cell maturation, reduced tumor-mediated immunosuppression, and mobilized tumor-specific CD8+ T cell responses at the tumor site. Together, these actions led to the elicitation of an effective antitumor immunity that increased the survival of tumor-bearing mice. The combination of RNA-OG-based nanovaccines with the α-PD-1 immune checkpoint blockade further enhanced the immunity. Hence, our RNA-OG nanostructures represent a robust, simple, and highly effective platform to empower peptide-based vaccines for cancer immunotherapy.

Evaluating the Spike–hACE2 interactions in the wild type and variants of concern of SARS -CoV-2 at different temperatures

The effect of temperature on SARS-CoV-2 is frequently debated upon. There is evidence of temperature sensitivity of the viral proteins; however, how heat influences the protein–protein interaction between a SARS-CoV-2 protein and the human angiotensin-converting enzyme 2 (ACE2) receptor remains to be elucidated. Here, we studied the receptor-binding domain of the surface glycoprotein of SARS-CoV-2 wild type and variants of concern bound to the human ACE2 receptor at different temperatures through atomistic simulations. We found that although there were no major conformation changes in the protein complexes at high temperatures, the dynamics of the proteins significantly increased. There was loss of protein–protein contacts and interaction energies. Thus, the protein–protein interaction was found to be rather strong. This study would be useful for viral protein studies and the design of peptide-based vaccines and therapeutics.

Identification of T cell and linear B cell epitopes on African horse sickness virus serotype 4 proteins VP1-1, VP2, VP4, VP7 and NS3

The viral proteins VP1-1, VP2, VP4, VP7 and NS3, of African horse sickness virus serotype 4 (AHSV4), have previously been identified to contain CD8+ T cell epitopes. In this study, overlapping peptides spanning the entire sequences of these AHSV4 proteins were synthesized and used to map epitopes. Peripheral blood mononuclear cells (PBMC) isolated from five horses immunized with an attenuated AHSV4 were stimulated in vitro with the synthesized peptides. Various memory immune assays were used to identify the individual peptides that contain CD8+ T cell epitopes, CD4+ T cell epitopes and linear B cell epitopes. The newly discovered individual peptides of AHSV4 proteins VP1-1, VP4, VP7 and/or NS3 that contain CD8+ T cell, CD4+ T cell or linear B cell epitopes could contribute to the design and development of new generation AHS peptide-based vaccines and therapeutics.

Immuno-informatics design of a multimeric epitope peptide-based vaccine against dengue virus serotype-2

IntroductionDengue viral infection affects approximately 130 countries worldwide. According to WHO reports, 40% of the global population lives in rural areas with a high risk of contracting dengue. Researchers have identified four distant strains of the dengue virus, and a single vaccine has not permanently controlled the emergence of all four distant strains. Therefore, a vaccine is required for each of the four strains to address the current situation. ObjectivesThe objective of this study was to design a multi-epitope-based vaccine for the dengue virus-2 strain that elicits a robust immune response while being safe and non-allergenic. ResultsFirstly, we analyzed the envelope protein for its physicochemical and antigenic properties. Next, we predicted MHC I, MHC II, and B-cell epitopes with high accuracy and evaluated their properties. Then, we constructed a vaccine using a suitable adjuvant and linkers, and predicted the secondary and tertiary structure of the vaccine, and the tertiary structure was validated. After conducting molecular docking with toll-like receptors, we utilized the best-docked result for molecular stimulation. Finally, we analyzed the immune stimulation against the vaccine, and the results showed positive immune responses from macrophages, DC cells, T-cells, and B-cells. Additionally, we found that the vaccine was excreted from the human body. ConclusionsOur study demonstrates the potential of using immunoinformatic tools and immunological knowledge to design a multi-epitope-based vaccine for the dengue virus-2 strain. This approach could be applied to designing vaccines for other diseases, and further studies are required to validate its effectiveness in vivo.

Immuno-informatics design of a multimeric epitope peptide-based vaccine against dengue virus serotype-2

IntroductionDengue viral infection affects approximately 130 countries worldwide. According to WHO reports, 40% of the global population lives in rural areas with a high risk of contracting dengue. Researchers have identified four distant strains of the dengue virus, and a single vaccine has not permanently controlled the emergence of all four distant strains. Therefore, a vaccine is required for each of the four strains to address the current situation. ObjectivesThe objective of this study was to design a multi-epitope-based vaccine for the dengue virus-2 strain that elicits a robust immune response while being safe and non-allergenic. ResultsFirstly, we analyzed the envelope protein for its physicochemical and antigenic properties. Next, we predicted MHC I, MHC II, and B-cell epitopes with high accuracy and evaluated their properties. Then, we constructed a vaccine using a suitable adjuvant and linkers, and predicted the secondary and tertiary structure of the vaccine, and the tertiary structure was validated. After conducting molecular docking with toll-like receptors, we utilized the best-docked result for molecular stimulation. Finally, we analyzed the immune stimulation against the vaccine, and the results showed positive immune responses from macrophages, DC cells, T-cells, and B-cells. Additionally, we found that the vaccine was excreted from the human body. ConclusionsOur study demonstrates the potential of using immunoinformatic tools and immunological knowledge to design a multi-epitope-based vaccine for the dengue virus-2 strain. This approach could be applied to designing vaccines for other diseases, and further studies are required to validate its effectiveness in vivo.

Immunoinformatics Approaches in Designing Vaccines Against COVID-19.

Since the onset of the COVID-19 pandemic, a number of approaches have been adopted by the scientific communities for developing efficient vaccine candidate against SARS-CoV-2. Conventional approaches of developinga vaccine require a long time and a series of trials and errors which indeed limit the feasibility of such approaches for developing a dependablevaccine in an emergency situation like theCOVID-19 pandemic. Hitherto, most of the available vaccines have been developed against a particular antigen of SARS-CoV, spike protein in most of the cases, and intriguingly, these vaccines are not effective against all the pathogenic coronaviruses. In this context, immunoinformatics-based reverse vaccinology approaches enable a robust design of efficacious peptide-based vaccines against all the infectious strains of coronaviruses within a short frame of time. In this chapter, we enumerate the methodological trajectory of developing a universal anti-SARS-CoV-2 vaccine, namely, "AbhiSCoVac," through advanced computational biology-based immunoinformatics approach and itsin-silico validation using molecular dynamics simulations.

Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein.

Haemophilus influenzae is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of Haemophilus influenzae. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein FtsN that specifically combat all the Haemophilus influenzae strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein-protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the Toll-like receptor 4 protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.

Rational design of a novel multi-epitope peptide-based vaccine against Onchocerca volvulus using transmembrane proteins

Almost a decade ago, it was recognized that the global elimination of onchocerciasis by 2030 will not be feasible without, at least, an effective prophylactic and/or therapeutic vaccine to complement chemotherapy and vector control strategies. Recent advances in computational immunology (immunoinformatics) have seen the design of novel multi-epitope onchocerciasis vaccine candidates which are however yet to be evaluated in clinical settings. Still, continued research to increase the pool of vaccine candidates, and therefore the chance of success in a clinical trial remains imperative. Here, we designed a multi-epitope vaccine candidate by assembling peptides from 14O. volvulus(Ov) proteins using an immunoinformatics approach. An initial 126 Ov proteins, retrieved from the Wormbase database, and at least 90% similar to orthologs in related nematode species of economic importance, were screened for localization, presence of transmembrane domain, and antigenicity using different web servers. From the 14 proteins retained after the screening, 26 MHC-1 and MHC-II (T-cell) epitopes, and linear B-lymphocytes epitopes were predicted and merged using suitable linkers. TheMycobacterium tuberculosisResuscitation-promoting factor E (RPFE_MYCTU), which is an agonist of TLR4, was then added to the N-terminal of the vaccine candidate as a built-in adjuvant. Immune simulation analyses predicted strong B-cell and IFN-γ based immune responses which are necessary for protection againstO. volvulusinfection. Protein-protein docking and molecular dynamic simulation predicted stable interactions between the 3D structure of the vaccine candidate and human TLR4. These results show that the designed vaccine candidate has the potential to stimulate both humoral and cellular immune responses and should therefore be subject to further laboratory investigation.

Computational analysis of immunogenic epitopes in the p30 and p54 proteins of African swine fever virus

African swine fever (ASF) is a highly infectious viral disease of pigs, which causes acute fatal haemorrhage and is a severe concern to the global pork industry. The present study followed computational approaches to identify B- and T-cell epitopes for the p30 and p54 proteins of the African swine fever virus (ASFV) by interacting with the swine leukocyte antigen (SLA) alleles. The amino acid sequences of p30 and p54 were analysed for variability and relative solvent accessibility, and their three-dimensional structures were predicted and validated. Molecular dynamics simulation was performed to study the structural and dynamic properties of the protein. Six and five linear B-cell epitopes have been predicted for p30 and p54, respectively. Four and three discontinuous B-cell epitopes have been predicted for p30 and p54, respectively. Further, the top five T-cell epitopes for SLA-1, 2, and 3 have been listed for both proteins. These results can help us to understand the immunodominant regions in the p30 and p54 proteins of ASFV and potentially assist in designing peptide-based diagnostics and vaccines. Also, the identified T-cell epitopes may be considered for peptide-based vaccine design against ASFV. Communicated by Ramaswamy H. Sarma

SILVI, an open-source pipeline for T-cell epitope selection.

High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.

Identification and characterization of promiscuous T-cell epitopes of hypothetical proteins for design of Plasmodium falciparum peptide-based malaria vaccine

Identification and characterization of promiscuous T-cell epitopes of hypothetical proteins for design of Plasmodium falciparum peptide-based malaria vaccine

DFBP: a comprehensive database of food-derived bioactive peptides for peptidomics research.

Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence-structure-activity relationship of FBPs in a large dataset. Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence-structure-activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. Supplementary data are available at Bioinformatics online.

Review of Immunoinformatic design of multi epitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target

Review of Immunoinformatic design of multi epitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target

Structural patterns in class 1 major histocompatibility complex-restricted nonamer peptide binding to T-cell receptors.

The startling diversity in αβ T-cell receptor (TCR) sequences and structures complicates molecular-level analyses of the specificity and sensitivity determining T-cell immunogenicity. A number of three-dimensional (3D) structures are now available of ternary complexes between TCRs and peptides: major histocompatibility complexes (pMHC). Here, to glean molecular-level insights we analyze structures of TCRs bound to human class I nonamer peptide-MHC complexes. Residues at peptide positions 4-8 are found to be particularly important for TCR binding. About 90% of the TCRs hydrogen bond with one or both of the peptide residues at positions 4 and 8 presented by MHC allele HLA-A2, and this number is still ~79% for peptides presented by other MHC alleles. Residue 8, which lies outside the previously-identified central peptide region, is crucial for TCR recognition of class I MHC-presented nonamer peptides. The statistics of the interactions also sheds light on the MHC residues important for TCR binding. The present analysis will aid in the structural modeling of TCR:pMHC complexes and has implications for the rational design of peptide-based vaccines and T-cell-based immunotherapies.

Prediction of Variable-Length B-Cell Epitopes for Antipeptide Paratopes Using the Program HAPTIC.

B-cell epitope prediction for antipeptide antibody responses enables peptide-based vaccine design and related translational applications. This entails estimating epitopeparatope binding free-energy changes from antigen sequence; but attempts to do so assuming uniform epitope length (e.g., of hexapeptide sequences, each spanning a typical paratope diameter when fully extended) have neglected empirically established variation in epitope length. This work aimed to develop a sequence-based physicochemical approach to variablelength B-cell epitope prediction for antipeptide paratopes recognizing flexibly disordered targets. Said approach was developed by analogy between epitope-paratope binding and protein folding modeled as polymer collapse, treating paratope structure implicitly. Epitope-paratope binding was thus conceptually resolved into processes of epitope compaction, collapse and contact, with epitope collapse presenting the main entropic barrier limiting epitope length among nonpolyproline sequences. The resulting algorithm was implemented as a computer program, namely the Heuristic Affinity Prediction Tool for Immune Complexes (HAPTIC), which is freely accessible via an online interface (http://badong.freeshell.org/haptic.htm). This was used in conjunction with published data on representative known peptide immunogens. HAPTIC predicted immunodominant epitope sequences with lengths limited by penalties for both compaction and collapse, consistent with known paratope-bound structures of flexibly disordered epitopes. In most cases, the predicted association constant was greater than its experimentally determined counterpart but below the predicted upper bound for affinity maturation in vivo. HAPTIC provides a physicochemically plausible means for estimating the affinity of antipeptide paratopes for sterically accessible and flexibly disordered peptidic antigen sequences by explicitly considering candidate B-cell epitopes of variable length.

Scoping review of the applications of peptide microarrays on the fight against human infections.

IntroductionThis scoping review explores the use of peptide microarrays in the fight against infectious diseases. The research domains explored included the use of peptide microarrays in the mapping of linear B-cell and T cell epitopes, antimicrobial peptide discovery, immunosignature characterisation and disease immunodiagnostics. This review also provides a short overview of peptide microarray synthesis.MethodsElectronic databases were systematically searched to identify relevant studies. The review was conducted using the Joanna Briggs Institute methodology for scoping reviews and data charting was performed using a predefined form. The results were reported by narrative synthesis in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsNinety-five articles from 103 studies were included in the final data charting process. The majority (92. 0%) of the articles were published during 2010–2020 and were mostly from Europe (44.2%) and North America (34.7%). The findings were from the investigation of viral (45.6%), bacterial (32. 0%), parasitic (23.3%) and fungal (2. 0%) infections. Out of the serological studies, IgG was the most reported antibody type followed by IgM. The largest portion of the studies (77.7%) were related to mapping B-cell linear epitopes, 5.8% were on diagnostics, 5.8% reported on immunosignature characterisation and 8.7% reported on viral and bacterial cell binding assays. Two studies reported on T-cell epitope profiling.ConclusionThe most important application of peptide microarrays was found to be B-cell epitope mapping or antibody profiling to identify diagnostic and vaccine targets. Immunosignatures identified by random peptide microarrays were found to be applied in the diagnosis of infections and interrogation of vaccine responses. The analysis of the interactions of random peptide microarrays with bacterial and viral cells using binding assays enabled the identification of antimicrobial peptides. Peptide microarray arrays were also used for T-cell linear epitope mapping which may provide more information for the design of peptide-based vaccines and for the development of diagnostic reagents.

Diversity of the BoLA-DRB3 gene in cattle breeds from tropical and subtropical regions of Argentina.

Bovine leukocyte antigens (BoLA) have been widely studied because of their primary function in the recognition of pathogens by the immune system. To date, however, the characterization of the BoLA-DRB3 gene in Latin American Zebu and mixed zebuine breeds is scarce. By a sequence-based typing method, here we sequenced exon 2 of BoLA class II DRB3 gene in 264 animals from the five most commonly used breeds in northern Argentina (Creole, Brahman, Braford, Brangus, and Nellore).The Bos taurus, Bos indicus, and mixed breeds analyzed here contained 61 previously reported alleles. Genetic diversity was high at both allelic and nucleotide sequence levels, particularly in the mixed breeds Braford and Brangus. In contrast to previous reports on DRB3 diversity, no evidence of balancing selection was found in our data. Differentiation among breeds was highly significant, as shown by FST (FST = 0.052, P < 0.001) and cluster analyses. In accordance with historical origin of the breeds, UPGMA trees and metric multidimensional scaling (MDS) analyses showed that Creole is distantly related to the other zebuine breeds. Among them, Brahman, Braford, and Brangus exhibited the closest affiliations. Despite the overall differentiation of the breeds, analysis of the peptide binding regions at the aminoacid level revealed that the key aminoacids involved in peptide recognition are greatly conserved suggesting little influence of domestication and breeding in functional MHC variability. In sum, this is the first report of BoLA-DRB3 diversity in pure and mixed Bos indicus cattle breeds from Argentina. Knowledge of BoLA-DRB3 variability in breeds adapted to tropical and subtropical environments contributes not only to the characterization of MHC diversity but also to the design of peptide-based vaccines.

"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.

BCEPS: A Web Server to Predict Linear B Cell Epitopes with Enhanced Immunogenicity and Cross-Reactivity.

Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody–antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies.