Recent advances in B-cell epitope prediction methods

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

A computational approach to design a multiepitope vaccine against H5N1 virus

Since 1997, highly pathogenic avian influenza viruses, such as H5N1, have been recognized as a possible pandemic hazard to men and the poultry business. The rapid rate of mutation of H5N1 viruses makes the whole process of designing vaccines extremely challenging. Here, we used an in silico approach to design a multi-epitope vaccine against H5N1 influenza A virus using hemagglutinin (HA) and neuraminidase (NA) antigens. B-cell epitopes, Cytotoxic T lymphocyte (CTL) and Helper T lymphocyte (HTL) were predicted via IEDB, NetMHC-4 and NetMHCII-2.3 respectively. Two adjuvants consisting of Human β-defensin-3 (HβD-3) along with pan HLA DR-binding epitope (PADRE) have been chosen to induce more immune response. Linkers including KK, AAY, HEYGAEALERAG, GPGPGPG and double EAAAK were utilized to link epitopes and adjuvants. This construct encodes a protein having 350 amino acids and 38.46 kDa molecular weight. Antigenicity of ~ 1, the allergenicity of non-allergen, toxicity of negative and solubility of appropriate were confirmed through Vaxigen, AllerTOP, ToxDL and DeepSoluE, respectively. The 3D structure of H5N1 was refined and validated with a Z-Score of - 0.87 and an overall Ramachandran of 99.7%. Docking analysis showed H5N1 could interact with TLR7 (docking score of - 374.08 and by 4 hydrogen bonds) and TLR8 (docking score of - 414.39 and by 3 hydrogen bonds). Molecular dynamics simulations results showed RMSD and RMSF of 0.25 nm and 0.2 for H5N1-TLR7 as well as RMSD and RMSF of 0.45 nm and 0.4 for H5N1-TLR8 complexes, respectively. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) confirmed stability and continuity of interaction between H5N1-TLR7 with the total binding energy of - 29.97 kJ/mol and H5N1-TLR8 with the total binding energy of - 23.9 kJ/mol. Investigating immune response simulation predicted evidence of the ability to stimulate T and B cells of the immunity system that shows the merits of this H5N1 vaccine proposed candidate for clinical trials.

Reverse vaccinology and immunoinformatics approach to design a chimeric epitope vaccine against Orientia tsutsugamushi

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Immunoinformatics analysis of Brucella melitensis to approach a suitable vaccine against brucellosis

BACKGROUND

Brucellosis caused by B. melitensis is one of the most important common diseases between humans and livestock. Currently, live attenuated vaccines are used for this disease, which causes many problems, and unfortunately, there is no effective vaccine for human brucellosis. The aim of our research was to design a recombinant vaccine containing potential immunogenic epitopes against B. melitensis.

METHODS

In this study, using immunoinformatics approaches, 3 antigens Omp31, Omp25, and Omp28 were identified and the amino acid sequence of the selected antigens was determined in NCBI. Signal peptides were predicted by SignaIP-5.0 server. To predict B-cell epitopes from ABCpred and Bcepred servers, to predict MHC-I epitopes from RANKPEP and SYFPEITHI servers, to predict MHC-II epitopes from RANKPEP and MHCPred servers, and to predict CTL epitopes were used from the CTLPred server. Potentially immunogenic final epitopes were joined by flexible linkers. Finally, allergenicity (AllerTOP 2.0 server), antigenicity (Vaxijen server), physicochemical properties (ProtParam server), solubility (Protein-sol server), secondary (PSIPRED and GRO4 servers) and tertiary structure (I-TASSER server), refinement (GalaxyWEB server), validation (ProSA-web, Molprobity, and ERRAT servers), and optimization of the codon sequence (JCat server) of the structure of the multi-epitope vaccine were analyzed.

RESULTS

The analysis of immunoinformatics tools showed that the designed vaccine has high quality, acceptable physicochemical properties, and can induce humoral and cellular immune responses against B. melitensis bacteria. In addition, the high expression level of recombinant antigens in the E. coli host was observed through in silico simulation.

CONCLUSION

According to the results in silico, the designed vaccine can be a suitable candidate to fight brucellosis and in vitro and in vivo studies are needed to evaluate the research of this study.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm

Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.

DL-TCNN: Deep Learning-based Temporal Convolutional Neural Network for prediction of conformational B-cell epitopes

Prediction of conformational B-cell epitopes (CBCE) is an essential phase for vaccine design, drug invention, and accurate disease diagnosis. Many laboratorial and computational approaches have been developed to predict CBCE. However, laboratorial experiments are costly and time consuming, leading to the popularity of Machine Learning (ML)-based computational methods. Although ML methods have succeeded in many domains, achieving higher accuracy in CBCE prediction remains a challenge. To overcome this drawback and consider the limitations of ML methods, this paper proposes a novel DL-based framework for CBCE prediction, leveraging the capabilities of deep learning in the medical domain. The proposed model is named Deep Learning-based Temporal Convolutional Neural Network (DL-TCNN), which hybridizes empirical hyper-tuned 1D-CNN and TCN. TCN is an architecture that employs causal convolutions and dilations, adapting well to sequential input with extensive receptive fields. To train the proposed model, physicochemical features are firstly extracted from antigen sequences. Next, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. Finally, the proposed DL-TCNN is employed for the prediction of CBCE. The model's performance is evaluated and validated on a benchmark antigen-antibody dataset. The DL-TCNN achieves 94.44% accuracy, and 0.989 AUC score for the training dataset, 78.53% accuracy, and 0.661 AUC score for the validation dataset; and 85.10% accuracy, 0.855 AUC score for the testing dataset. The proposed model outperforms all the existing CBCE methods.

Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.

Molecular insights into recognition of GUCY2C by T-cell engaging bispecific antibody anti-GUCY2CxCD3

The intestinal epithelial receptor Guanylyl Cyclase C (GUCY2C) is a tumor-associated cell surface antigen expressed across gastrointestinal malignancies that can serve as an efficacious target for colorectal cancer immunotherapy. Here, we describe a yeast surface-display approach combined with an orthogonal peptide-based mapping strategy to identify the GUCY2C binding epitope of a novel anti-GUCY2CxCD3 bispecific antibody (BsAb) that recently advanced into the clinic for the treatment of cancer. The target epitope was localized to the N-terminal helix H2 of human GUCY2C, which enabled the determination of the crystal structure of the minimal GUCY2C epitope in complex with the anti-GUCY2C antibody domain. To understand if this minimal epitope covers the entire antibody binding region and to investigate the impact of epitope position on the antibody's activity, we further determined the structure of this interaction in the context of the full-length extracellular domain (ECD) of GUCY2C. We found that this epitope is positioned on the protruding membrane-distal helical region of GUCY2C and that its specific location on the surface of GUCY2C dictates the close spatial proximity of the two antigen arms in a diabody arrangement essential to the tumor killing activity of GUCY2CxCD3 BsAb.

A systematic review of peptide-based serological tests for the diagnosis of leishmaniasis

Serological methods should meet the needs of leishmaniasis diagnosis due to their high sensitivity and specificity, economical and adaptable rapid diagnostic test format, and ease of use. Currently, the performances of serological diagnostic tests, despite improvements with recombinant proteins, vary greatly depending on the clinical form of leishmaniasis and the endemic area. Peptide-based serological tests are promising as they could compensate for antigenic variability and improve performance, independently of Leishmania species and subspecies circulating in the endemic areas. The objective of this systematic review was to inventory all studies published from 2002 to 2022 that evaluate synthetic peptides for serological diagnosis of human leishmaniases and also to highlight the performance (e.g., sensitivity and specificity) of each peptide reported in these studies. All clinical forms of leishmaniasis, visceral and tegumentary, and all Leishmania species responsible for these diseases were considered. Following PRISMA statement recommendations, 1,405 studies were identified but only 22 articles met the selection criteria and were included in this systematic review. These original research articles described 77 different peptides, of which several have promising performance for visceral or tegumentary leishmaniasis diagnosis. This review highlights the importance of and growing interest in synthetic peptides used for serological diagnosis of leishmaniases, and their performances compared to some widely used tests with recombinant proteins.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.

DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes

The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consists of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers at: http://www.biolscience.cn/DeepLBCEPred/.

Bioinformatics Designing and Molecular Modelling of a Universal mRNA Vaccine for SARS-CoV-2 Infection

At this present stage of COVID-19 re-emergence, designing an effective candidate vaccine for different variants of SARS-CoV-2 is a study worthy of consideration. This research used bioinformatics tools to design an mRNA vaccine that captures all the circulating variants and lineages of the virus in its construct. Sequences of these viruses were retrieved across the six continents and analyzed using different tools to screen for the preferable CD8⁺ T lymphocytes (CTL), CD4⁺ T lymphocytes (HTL), and B-cell epitopes. These epitopes were used to design the vaccine. In addition, several other co-translational residues were added to the construct of an mRNA vaccine whose molecular weight is 285.29686 kDa with an estimated pI of 9.2 and has no cross affinity with the human genome with an estimated over 68% to cover the world population. It is relatively stable, with minimal deformability in its interaction with the human innate immune receptor, which includes TLR 3 and TLR 9. The overall result has proven that the designed candidate vaccine is capable of modulating cell-mediated immune responses by activating the actions of CD4⁺ T cells, natural killer cells, and macrophages, and displayed an increased memory T cell and B cell activities, which may further be validated via in vivo and in vitro techniques.

A Method for Screening Proteases That Can Specifically Hydrolyze the Epitope AA83-105 of αs1-Casein Allergen

Milk protein hydrolysates are common in infant formula, but some of them retain allergenicity due to incomplete hydrolysis of the epitopes for milk allergens. This study explored a method for screening proteases that could specifically hydrolyze the epitope of αs1-casein allergen. Firstly, the αs1-casein epitope AA83-105 was synthesized by the solid-phase synthesis method. Then, after purification and identification, the complete antigen was prepared through coupling with bovine serum albumin (BSA) and was used to raise monoclonal antibodies (mAb) in BALB/c mice. Additionally, an indirect competitive-enzyme-linked immunosorbent assay (icELISA) was established. The mAb raised against αs1-casein protein was used as a control. The results showed that the purity of the synthetic epitope was >90%, and the coupling rate with BSA was 6.31. The mAb subtype is IgG1, with a titer of 1:320,000. The mAb reacted specifically with αs1-casein but did not cross-react with soybean protein. The linear regression equation of the competitive inhibition curve was y = −9.22x + 100.78 (R2 = 0.9891). The detection limit of icELISA method was more sensitive, and the method showed good accuracy and repeatability. The amounts of antigen residues in papain protease hydrolysates were relatively small, and the epitope fragment was detected in papain hydrolysate via mass spectrometry. This study provides ideas and methods for screening proteases that specifically hydrolyze the epitopes of milk allergens and also provides a superior foundation for the development of an advanced hypoallergenic formula.

Relationship between B-cell epitope structural properties and the immunogenicity of blood group antigens: Outlier properties of the Kell K1 antigen

BACKGROUND

The immunogenicities of polypeptide blood group antigens vary, despite most being created by single amino acid (AA) substitutions. To study the basis of these differences, we employed an immunoinformatics approach to determine whether AA substitution sites of blood group antigens have structural features typical of B-cell epitopes and whether the extent of B-cell epitope properties is positively related to immunogenicity.

STUDY DESIGN AND METHODS

Fifteen structural property prediction programs were used to determine the likelihood of β-turns, surface accessibility, flexibility, hydrophilicity, particular AA composition and AA pairs, and other B-cell epitope properties at AA substitution sites of polypeptide blood group antigens.

RESULTS

AA substitution sites of Lu^a , Jk^a , E, c, M, Fy^a , C, and S were each located in regions with at least two structural features typical of B-cell epitopes. The substitution site of K, the most immunogenic non-ABO/D antigen, scored the lowest for most B-cell epitope properties and was the only one not predicted to be part of a linear B-cell epitope. The most immunogenic antigens studied (K, Jk^a , Lu^a , E) had B-cell epitope structural properties determined by the fewest programs; the least immunogenic antigens (e.g., Fy^a , S, C, c) had B-cell epitope properties according to the most programs.

DISCUSSION

Counter to prediction, the immunogenicity of polypeptide blood group antigens was not positively related to B-cell epitope structural features present at their AA-substitution sites. Instead, it tended to be negatively related. The AA-substitution site of the most immunogenic non-ABO/D antigen, K, had the least B-cell epitope features.

Immunopotentiation by linking Hsp70 T-cell epitopes to Gag-Pol-Env-Nef-Rev multiepitope construct and increased IFN-gamma secretion in infected lymphocytes

Therapeutic human immunodeficiency virus (HIV) vaccines can boost the anti-HIV host immunity to control viral replication and eliminate viral reservoirs in the absence of anti-retroviral therapy. In this study, two computationally designed multiepitope Gag-Pol-Env-Nef-Rev and Hsp70-Gag-Pol-Env-Nef-Rev constructs harboring immunogenic and highly conserved HIV T cell epitopes were generated in E. coli as polypeptide vaccine candidates. Furthermore, the multiepitope gag-pol-env-nef-rev and hsp70-gag-pol-env-nef-rev DNA vaccine constructs were prepared and complexed with MPG cell-penetrating peptide. The immunogenicity of the multiepitope constructs were evaluated using the homologous and heterologous prime/boost strategies in mice. Moreover, the secretion of IFN-γ was assessed in infected lymphocytes in vitro. Our data showed that the homologous polypeptide regimens could significantly induce a mixture of IgG1 and IgG2a antibody responses, activate T cells to secret IFN-γ, IL-5, IL-10, and generate Granzyme B. Moreover, IFN-γ secretion was significantly enhanced in single-cycle replicable (SCR) HIV-1 virions-infected splenocytes in these groups compared to uninfected splenocytes. The linkage of heat shock protein 70 (Hsp70) epitopes to Gag-Pol-Env-Nef-Rev polypeptide in the homologous regimen increased significantly cytokines and Granzyme B levels, and IFN-γ secretion in virions-infected splenocytes. Briefly, both designed constructs in the homologous regimens can be used as a promising vaccine candidate against HIV infection.

In silico epitope-based vaccine design against influenza a neuraminidase protein: Computational analysis established on B- and T-cell epitope predictions

Objective

Influenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.

Methods

The present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.

Results

Computational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.

Conclusions

We propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop these vaccines and effectively monitor these diseases, it is necessary to use accurate diagnostic methods capable of identifying highly immunogenic regions within a given pathogenic protein. Existing experimental methods are expensive, time-consuming, and require arduous laboratory work, as they require the screening of a large number of potential candidate epitopes, making the methods extremely laborious, especially for application to larger microorganisms. In the last decades, researchers have developed in silico prediction methods, based on machine learning, to identify these markers, to drastically reduce the list of potential candidate epitopes for experimental tests, and, consequently, to reduce the laborious task associated with their mapping. Despite these efforts, the tools and methods still have low accuracy, slow diagnosis, and offline training. Thus, we develop a method to predict B-cell linear epitopes which are based on a Fuzzy-ARTMAP neural network architecture, called BepFAMN (B Epitope Prediction Fuzzy ARTMAP Artificial Neural Network). This was trained using a linear averaging scheme on 15 properties that include an amino acid ratio scale and a set of 14 physicochemical scales. The database used was obtained from the IEDB website, from which the amino acid sequences with the annotations of their positive and negative epitopes were taken. To train and validate the knowledge models, five-fold cross-validation and competition techniques were used. The BepiPred-2.0 database, an independent database, was used for the tests. In our experiment, the validation dataset reached sensitivity = 91.50%, specificity = 91.49%, accuracy = 91.49%, MCC = 0.83, and an area under the curve (AUC) ROC of approximately 0.9289. The result in the testing dataset achieves a significant improvement, with sensitivity = 81.87%, specificity = 74.75%, accuracy = 78.27%, MCC = 0.56, and AOC = 0.7831. These achieved values demonstrate that BepFAMN outperforms all other linear B-cell epitope prediction tools currently used. In addition, the architecture provides mechanisms for online training, which allow the user to find a new B-cell linear epitope, and to improve the model without need to re-train itself with the whole dataset. This fact contributes to a considerable reduction in the number of potential linear epitopes to be experimentally validated, reducing laboratory time and accelerating the development of diagnostic tests, vaccines, and immunotherapeutic approaches.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

An immunoinformatics-based designed multi-epitope candidate vaccine (mpme-VAC/STV-1) against Mycoplasma pneumoniae

Pneumonia is a serious global health problem that accounts for over one million deaths annually. Among the main microorganisms causing pneumonia, Mycoplasma pneumoniae is one of the most common ones for which a vaccine is immediately required. In this context, a multi-epitope vaccine against this pathogen could be the best option that can induce effective immune response avoiding any serious adverse reactions. In this study, using an immunoinformatics approach we have designed a multi-epitope vaccine (mpme-VAC/STV-1) against M. pneumoniae. Our designed mpme-VAC/STV-1 is constructed using CTL (cytotoxic T lymphocyte), HTL (Helper T lymphocyte), and B-cell epitopes. These epitopes are selected from the core proteins of 88 M. pneumoniae genomes that were previously identified through reverse vaccinology approaches. The epitopes were filtered according to their immunogenicity, population coverage, and several other criteria. Sixteen CTL/B- and thirteen HTL/B- epitopes that belong to 5 core proteins were combined together through peptide linkers to develop the mpme-VAC/STV-1. The heat-labile enterotoxin from E. coli was used as an adjuvant. The designed mpme-VAC/STV-1 is predicted to be stable, non-toxic, non-allergenic, non-host homologous, and with required antigenic and immunogenic properties. Docking and molecular dynamic simulation of mpme-VAC/STV-1 shows that it can stimulate TLR2 pathway mediated immunogenic reactions. In silico cloning of mpme-VAC/STV-1 in an expression vector also shows positive results. Finally, the mpme-VAC/STV-1 also shows promising efficacy in immune simulation tests. Therefore, our constructed mpme-VAC/STV-1 could be a safe and effective multi-epitope vaccine for immunization against pneumonia. However, it requires further experimental and clinical validations.

Development of Multi-epitope Subunit Vaccine Against Pseudomonas aeruginosa Using OprF/OprI and PopB Proteins

Background: The emerging problem of antibiotic resistance in Pseudomonas aeruginosa is a global health concern; hence, revealing innovative therapeutic approaches (such as designing an immunogenic vaccine candidate) is needed. There is no evidence of the availability of an effective vaccine that can combat the infection caused by this microorganism. Objectives: This research was conducted to develop a potential chimeric vaccine against P. aeruginosa using reverse vaccinology approaches. Methods: The present vaccine candidate comprised outer membrane protein F and I (OprF/OprI) and PopB with appropriate linkers. After applying meticulous immune-informatics investigation, the multi-epitope vaccine was created, including helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL), interferon gamma (IFN-γ), and interleukin 4 (IL-4) epitopes. Then, the physicochemical characteristics, allergenicity, toxicity, and antigenicity were analyzed. After investigating the secondary structure, the tertiary structure (3D) model was generated, refined, and validated via computational methods. Besides, the strong protein-ligand interaction and stability between the vaccine candidate and toll-like receptor 4 (TLR4) were determined via molecular docking and dynamics analyses. Moreover, in silico cloning accompanied by pET-22b (+) was used to achieve high translation efficiency. Results: Our results presumed that the chimeric-designed vaccine was thermostable and contained optimal physicochemical properties. This vaccine candidate was nontoxic and highly soluble and had stable protein and TLR4 interaction, adequately overexpressed in Escherichia coli. Overall, it could induce immune responses and repress this microorganism. Conclusions: Therefore, to inhibit Pseudomonas infections experimentally, the efficacy and safety of the vaccine design need to be validated.

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.

Different Aspects Concerning Viral Infection and the Role of MHC Molecules in Viral Prevention

Major Histocompatibility Complex (MHC) molecules play a crucial role in inducing an adaptive immune response. T-cell epitopes require compatible MHC molecules to form MHC-peptide Complexes (pMHC) that activate the T-cell Receptors (TCR) of T-lymphocyte clones. MHCs are polymorphic molecules with wide varieties of gene alleles. There are two classes of MHC molecules, class I and II. Both classes have three classical loci HLA-A, -B, and –C are present in class I and HLA-DP, -DQ, and -DR in class II. To induce a compatible T-lymphocyte clone, the T-cell epitope requires the association of the compatible MHC molecule to form pMHC. Each MHC variant possesses a different groove that is capable of binding a different range of antigenic epitopes. Without the compatible MHC molecule, a T cell clone cannot be activated by a particular viral epitope. With the aim of preventing viral transmission, the efficiency of a viral vaccine is related to the existence of specific MHC alleles in the individual. This article proposes the roles of the MHC molecule to prevent viral infection. In addition, the association of the viral receptor molecule with the viral infection will also be discussed.

In silico prediction of B and T cell epitopes based on NDV fusion protein for vaccine development against Newcastle disease virus

Newcastle disease (ND) is known as the most common diseases of economic importance worldwide. Vaccination against virulent strains of Newcastle disease virus (NDV) has failed during some outbreaks. Here, we aimed to assess the epitopes of NDV fusion protein as targets for a peptide-based vaccine. To explore the most antigenic epitopes on the F protein, we retrieved virulent strains of genotype VII from National Center for Biotechnology Information (NCBI). Linear and conformational B-cell epitopes were identified. Moreover, T-cell epitopes with high and moderate binding affinities to human major histocompatibility complex (MHC) class I and class II alleles were predicted using bioinformatics tools. Subsequently, the overlapped epitopes of B-cell and MHC class I and MHC class II were determined. To validate our predictions, the best epitopes were docked, to chicken MHC class I (B-F) alleles using the HADDOCK flexible docking server. Seven ‘high ranked epitopes’ were identified. Among them, ‘LYCTRIVTF’ and ‘MRATYLETL’ showed the highest scores. The other five epitopes including LSGEFDATY, LTTPPYMALK, LYLTELTTV, DCIKITQQV and SIAATNEAV obtained very encouraging results as well. SIAATNEAV had been recognized as a neutralizing epitope of F protein using monoclonal antibodies before. Taken together, our results demonstrated that the identified epitopes needed to be tested by in vitro and in vivo experiments.

Conformational epitope matching and prediction based on protein surface spiral features

Background

A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.

Results

We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.

Conclusions

The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells

BACKGROUND

Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods.

OBJECTIVE

We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells.

METHODS

In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay.

RESULTS

New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells.

CONCLUSION

In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

A potential peptide inhibitor of SARS-CoV-2 S and human ACE2 complex

The disease COVID-19 has caused heavy socio-economic burden and there is immediate need to control it. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The viral entry into human cell depends on the attachment of spike (S) protein via its receptor binding domain (RBD) to human cell receptor angiotensin-converting enzyme 2 (hACE2). Thus, blocking the virus attachment to hACE2 could serve as potential therapeutics for viral infection. We have designed a peptide inhibitor (ΔABP-α2) targeting the RBD of S protein using in-silico approach. Docking studies and computed affinities suggested that peptide inhibitor binds at the RBD with ∼95-fold higher affinity than hACE2. Molecular dynamics (MD) simulation confirms the stable binding of inhibitor to hACE2. Immunoinformatics studies suggest non-immunogenic and non-toxic nature of peptide. Thus, the proposed peptide could serve as potential blocker for viral attachment.

Communicated by Ramaswamy H. Sarma

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2

BACKGROUND

In order to obtain antibodies that recognize natural proteins, it is possible to predict the antigenic determinants of natural proteins, which are eventually embodied as polypeptides. The polypeptides can be coupled with corresponding vectors to stimulate the immune system to produce corresponding antibodies, which is also a simple and effective vaccine development method. The discovery of epitopes is helpful to the development of SARS-CoV-2 vaccine.

METHODS

The analyses were related to epitopes on 3 proteins, including spike (S), envelope (E) and membrane (M) proteins, which are located on the lipid envelope of the SARS-CoV-2. Based on the NCBI Reference Sequence: NC_045512.2, the conformational and linear B cell epitopes of the surface protein were predicted separately by various prediction methods. Furthermore, the conservation of the epitopes, the adaptability and other evolutionary characteristics were also analyzed, the sequences of the whole genome of SARS-CoV-2 were obtained from the GISAID.

RESULTS

7 epitopes were predicted, including 6 linear epitopes and 1 conformational epitope. One of the linear and one of the conformational consist of identical sequence, but represent different forms of epitopes. It is worth mentioning that all 6 identified epitopes were conserved in nearly 3500 SARS-CoV-2 genomes, showing that it is helpful to obtain stable and long-acting epitopes under the condition of high frequency of amino acid mutation, which deserved further study at the experiment level.

CONCLUSION

The findings would facilitate the vaccine development, had the potential to be directly applied on the prevention in this disease, but also have the potential to prevent the possible threats caused by other types of coronavirus.

Coronavirus epitope prediction from highly conserved region of spike protein

Purpose

The aim of this research was to predict the epitope for coronavirus family spike protein. Coronavirus family is highly evolved viruses which cause several outbreaks in the past decades. Therefore, it is crucial to design a global vaccine candidate to prevent the coronavirus outbreak in the future.

Materials and Methods

The spike protein amino acid sequences from nine coronavirus family were searched in the Uniprot database. The spike protein sequences were aligned using Clustal method. The highly conservatives amino acids were analyzed its B cell linear and continuous epitopes and T cell epitopes.

Results

From the alignment results it was found that there is a highly conserved region in the extracellular domain of spike protein. With prediction methods from this highly conserved region, B cell and T cell epitopes from spike protein were derived.

Conclusion

From several different prediction results, B cell epitope and T cell epitope were identified in the highly conserved region thus it is promising to be developed as a coronavirus vaccine candidate.

Structure-based drug designing and immunoinformatics approach for SARS-CoV-2

The prevalence of respiratory illness caused by the novel SARS-CoV-2 virus associated with multiple organ failures is spreading rapidly because of its contagious human-to-human transmission and inadequate globalhealth care systems. Pharmaceutical repurposing, an effective drug development technique using existing drugs, could shorten development time and reduce costs compared to those of de novo drug discovery. We carried out virtual screening of antiviral compounds targeting the spike glycoprotein (S), main protease (Mpro), and the SARS-CoV-2 receptor binding domain (RBD)-angiotensin-converting enzyme 2 (ACE2) complex of SARS-CoV-2. PC786, an antiviral polymerase inhibitor, showed enhanced binding affinity to all the targets. Furthermore, the postfusion conformation of the trimeric S protein RBD with ACE2 revealed conformational changes associated with PC786 drug binding. Exploiting immunoinformatics to identify T cell and B cell epitopes could guide future experimental studies with a higher probability of discovering appropriate vaccine candidates with fewer experiments and higher reliability.

Chimeric Protein Designed by Genome-Scale Immunoinformatics Enhances Serodiagnosis of Bovine Neosporosis

Neosporosis has become a concern since it is associated with abortion in cattle. Currently, in situ diagnosis is determined through anamnesis, evaluation of the history, and perception of the clinical signs of the herd. There is no practical and noninvasive test adapted to a large number of samples, which represents a gap for the use of new approaches that provide information about infections and the risks of herds. Here, we performed a search in the Neospora caninum genome by linear B-cell epitopes using immunoinformatic tools aiming to develop a chimeric protein with high potential to bind specifically to antibodies from infected cattle samples. An enzyme-linked immunosorbent assay with the new chimeric antigen was developed and tested with sera from natural field N. caninum-infected bovines. The cross-reactivity of the new antigen was also evaluated using sera from bovines infected by other abortive pathogens, including Trypanosoma vivax, Leptospira sp., Mycobacterium bovis, and Brucella abortus, and enzootic bovine leucosis caused by bovine leukemia virus, as well as with samples of animals infected with Toxoplasma gondii The assay using the chimeric protein showed 96.6% ± 3.4% of sensitivity in comparison to healthy animal sera. Meanwhile, in relation to false-positive results provided by cross-reactivity with others pathogens, the specificity value was 97.0% ± 2.9%. In conclusion, immunoinformatic tools provide an efficient platform to build an accurate protein to diagnose bovine neosporosis based on serum samples.

Immunogenicity, antibody responses and vaccine efficacy of recombinant annexin B30 against Schistosoma mansoni

AIMS

Schistosomes infect approximately 250 million people worldwide. To date, there is no effective vaccine available for the prevention of schistosome infection in endemic regions. There remains a need to develop means to confer long-term protection of individuals against reinfection. In this study, an annexin, namely annexin B30, which is highly expressed in the tegument of Schistosoma mansoni was selected to evaluate its immunogenicity and protective efficacy in a mouse model.

METHODS AND RESULTS

Bioinformatics analysis showed that there were three potential linear B-cell epitopes and four conformational B-cell epitopes predicted from annexin B30, respectively. Full-length annexin B30 was cloned and expressed in Escherichia coli BL21(DE3). In the presence of adjuvants, the soluble recombinant protein was evaluated for its protective efficacy in two independent vaccine trials. Immunization of CBA mice with recombinant annexin B30 formulated either in alum only or alum/CpG induced a mixed Th1/Th2 cytokine profile but no significant protection against schistosome infection was detected.

CONCLUSION

Recombinant annexin B30 did not confer significant protection against the parasite. The molecule may not be suitable for vaccine development. However, it could be an ideal biomarker recommended for immunodiagnostics development.

Designing and evaluation of an antibody-targeted chimeric recombinant vaccine encoding Shigella flexneri outer membrane antigens

Shigellosis is one of the most common diseases found in the developing countries, especially those countries that are prone flood. The causative agent for this disease is the Shigella species. This organism is one of the third most common enteropathogens responsible for childhood diarrhea. Since Shigella can survive gastric acidity and is an intracellular pathogen, it becomes difficult to treat. Also, uncontrolled use of antibiotics has led to development of resistant strains which poses a threat to public health. Therefore, there is a need for long term control of Shigella infection which can be achieved by designing a proper and effective vaccine. In this study, emphasis was made on designing a candidate that could elicit both B-cell and T-cell immune response. Hence B- and T-cell epitopes of outer membrane channel protein (OM) and putative lipoprotein (PL) from S. flexneri 2a were computationally predicted using immunoinformatics approach and a chimeric construct (chimeric-OP) containing the immunogenic epitopes selected from OM and PL was designed, cloned and expressed in E. coli system. The immunogenicity of the recombinant chimeric-OP was assessed using Shigella antigen infected rabbit antibody. The result showed that the chimeric-OP was a synthetic peptide candidate suitable for the development of vaccine and immunodiagnostics against Shigella infection.

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

Background

Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances.

Results

Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method.

Conclusion

The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, “optimized” methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

Physicochemical determinants of antibody-protein interactions

Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.

PEPOP 2.0: new approaches to mimic non-continuous epitopes

BACKGROUND

Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody.

RESULTS

We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.

CONCLUSION

Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.

Second update of the International Registry of HLA Epitopes. I. The HLA-ABC Epitope Database

The International Registry of HLA Epitopes (http://www.epregistry.com.br) is a website-based resource for HLA epitopes important in transplant rejection and platelet transfusion refractoriness. Its primary goal is to document epitopes that are verified experimentally with specific antibodies. Such epitopes can be defined by single eplets and by eplets paired with certain polymorphic residues within a 15-Å radius, the dimension of the corresponding structural epitope. This report is an update of the HLA-ABC repertoire including descriptions of 72 antibody-verifications of epitopes defined by eplets and/or eplet pairs. The newly updated version 2.0 EpRegistry shows also the polymorphic residue compositions of structural epitopes corresponding to eplets shared between groups of alleles. At present, 151 eplets have not been antibody-verified, and we ranked them with a so-called ElliPro score as a potential predictor of immunogenicity. Sixty eplets with low ElliPro scores might be considered non-epitopes incapable of inducing specific antibodies.

H1N1 influenza virus epitopes classified by monoclonal antibodies

Epitopes serve an important role in influenza infection. It may be useful to screen universal influenza virus vaccines, analyzing the epitopes of multiple subtypes of the hemagglutinin (HA) protein. A total of 40 monoclonal antibodies (mAbs) previously obtained from flu virus HA antigens (development and characterization of 40 mAbs generated using H1N1 influenza virus split vaccines were previously published) were used to detect and classify mAbs into distinct flu virus sub-categories using the ELISA method. Following this, the common continuous amino acid sequences were identified by multiple sequence alignment analysis with the GenBank database and DNAMAN software, for use in predicting the epitopes of the HA protein. Synthesized peptides of these common sequences were prepared, and used to verify and determine the predicted linear epitopes through localization and distribution analyses. With these methods, nine HA linear epitopes distributed among different strains of influenza virus were identified, which included three from influenza A, four from 2009 H1N1 and seasonal influenza, and two from H1. The present study showed that considering a combination of the antigen-antibody reaction specificity, variation in the influenza virus HA protein and linear epitopes may present a useful approach for designing effective multi-epitope vaccines. Furthermore, the study aimed to clarify the cause and pathogenic mechanism of influenza virus HA-induced flu, and presents a novel idea for identifying the epitopes of other pathogenic microorganisms.