Discontinuous Epitope Prediction Research Articles

An In silico Study on B-cell Epitope Mapping of Acinetobacter baumannii Outer Membrane Protein K.

Acinetobacter baumannii is one of the main causes of nosocomial infections. No vaccine has yet been licensed for use in humans, and efforts are still ongoing. In the present study, we have predicted the B-cell epitopes of A. baumannii's outer membrane protein K (OMPK) by using epitope prediction algorithms as possible vaccine candidates for future studies. The linear B-cell epitopes were predicted by seven different prediction tools. The 3D structure of OMPK was modeled and used for discontinuous epitope prediction by ElliPro and DiscoTope 2.0 tools. The final linear epitopes and the discontinuous epitope segments were checked for potential allergenicity, toxicity, human similarity, and experimental records. The structure and physicochemical features of the final epitopic peptide were assessed by numerous bioinformatics tools. Many B-cell epitopes were detected that could be assessed for possible antigenicity and immunogenicity. Also, an epitopic 22-mer region (peptide) of OMPK was found that contained both linear and discontinuous B-cell epitopes. This epitopic peptide has been found to possess appropriate physicochemical and structural properties to be an A. baumannii vaccine candidate. Altogether, here, the high immunogenic B-cell epitopes of OMPK have been identified, and a high immunogenic 22-mer peptide as an A. baumannii vaccine candidate has been introduced. The in vitro/in vivo studies of this peptide are recommended to decide its real efficacy and efficiency.

627. Bioinformatic Approach to Design a Plasmodium falciparum PfRipr Multi-Epitope Vaccine Construct

Abstract Background Malaria infections remain an enormous contributor to global fatalities with an estimated death toll of 619,000 in 2021 and 77% of deaths being children aged under 5 years. Transmitted via mosquito, Plasmodium falciparum is the most lethal parasite of its genus but has evaded many treatment and vaccine efforts due to its complex life cycle and redundant invasion mechanisms. Epitope-based vaccines hold significant promise for malaria vaccine development due to their ease of development and ability to target dominant regions in antigenically variable pathogens. The recently characterized protein P. falciparum merozoite Rh5 interacting protein (PfRipr) is nonredundant, highly conserved, and essential for erythrocyte invasion, making it an ideal target for a bloodstage malaria vaccine. Merozoite Invasion Model of binding and insertion of PRCR complex to erythrocyte basigin in merozoite invasion. Adapted from Scally et al. 2022. Methods Using P. falciparum sequences collected from Burkina Faso and Uganda, we assessed the immunogenic potential of PfRipr epitopes with regard to T-cell receptor binding and B-cell recognition. T-cell receptor binding was predicted using NetMHCpan searching against MHC I and II alleles with high regional frequencies. Using an in-silico 3D model of PfRipr predicted via AlphaFold, tertiary structures of all PfRipr sample sequences were predicted via SWISS-MODEL then analyzed by ElliPro to identify linear and discontinuous B-cell epitopes. Putative epitopes were filtered using allele coverage, conservation, antigenicity, and allergenicity. B-Cell Epitope Prediction Protocol for B-cell epitope prediction using AlphaFold for template modeling, Swiss-Model for homology-based modeling of sample sequences, and ElliPro for continuous and discontinuous epitope prediction.​ Epitope Sorting and Filtering Protocol for sorting T-cell epitopes using Average Allele Coverage (percentage of alleles in which the protein sequence was identified as an epitope) and B-cell epitopes using ElliPro Score (Protrusion Index representing surface accessibility of the epitope).​ Results Between the two datasets, there were 19 matching epitopes with 7 MHC I, 9 MHC II, 1 linear, and 2 discontinuous. These epitopes were used to design a multi-epitope-based bloodstage vaccine construct against P. falciparum. MHC Epitopes Partial list of MHC epitopes found in Burkina Faso and Uganda datasets. B-Cell Epitopes B-cell epitopes found in Burkina Faso and Uganda datasets. PfRipr 3D Structure with B-cell Epitopes 3D model of PfRipr predicted via AlphaFold with labeled B-cell epitopes Conclusion To validate their predicted immunogenicity, epitopes can be further investigated using in silico protein stabilization and docking simulations, in vitro methods such as HLA stabilization or T-cell activation assays, and in vivo methods using transgenic mouse models. The pipeline of immunoinformatic analyses formulated in this project can be further applied to P. falciparum sequence datasets collected from other malaria endemic regions to develop vaccines effective against circulating strains. Multi-Epitope Vaccine Construct Schematic representation of hypothetical multi-epitope vaccine construct. FliC is added to the N-terminus to stimulate and amplify immune response. The EAAAK linker is used to separate the bifunctional fusion protein domains. AAY linkers are used to amplify MHC I and II epitopes and prevent junctional epitope binding. GPGPG linkers between B-cell epitopes function to minimize junctional epitopes and retain conformational-dependent immunogenicity. A six amino acid Histidine (6H) chain was added to the C-terminus to aid during lab purification processes. Disclosures All Authors: No reported disclosures

In Silico Prediction of Plasmodium falciparum PfRipr Epitopes as Vaccine Candidates

Abstract Background The global malarial death toll in 2020 was ~627,000, 77% of which occurred in children <5 years of age. Plasmodium falciparum is the most lethal parasite of its genus but has evaded vaccine development due to its complex life cycle and redundant invasion mechanisms. Epitope-based vaccines hold significant promise due to their ability to target dominant and/or conserved regions in highly antigenically variable pathogens, including malaria. The recently characterized P. falciparum merozoite Rh5 interacting protein (PfRipr) is nonredundant, highly conserved, and essential for erythrocyte invasion, making it an ideal target for a bloodstage malaria vaccine. (Figure 1) We sought to analyze P. falciparum sequences from highly endemic areas using bioinformatic approaches that identify candidate PfRipr epitope-based bloodstage vaccine antigens for inclusion in a multiantigen vaccine. Figure 1: Model of binding and insertion of PRCR complex to erythrocyte basigin in merozoite invasion. Adapted from Scally et al. 2022. (https://doi.org/10.1038/s41564-022-01261-2 , https://creativecommons.org/licenses/by/4.0/) Methods Using two P. falciparum sequence datasets from Burkina Faso and Uganda, we assessed the immunogenic potential of PfRipr epitopes for T-cell receptor binding and B-cell recognition. T-cell receptor binding was predicted using NetMHCpan and NetMHCIIpan searching against MHC I and II alleles with high regional frequencies. Using an in-silico 3D model of PfRipr predicted via AlphaFold, tertiary structures of all PfRipr sample sequences were predicted via SWISS-MODEL then analyzed by ElliPro to identify linear and discontinuous B-cell epitopes. (Figure 2) Putative epitopes were filtered using binding thresholds, allele coverage, conservation, antigenicity, and allergenicity. (Figure 3) Figure 2: Protocol for B-cell epitope prediction using AlphaFold for template modeling, Swiss-Model for homology-based modeling of sample sequences, and ElliPro for continuous and discontinuous epitope prediction. Figure 3: Protocol for sorting T-cell epitopes using Average Allele Coverage (percentage of alleles in which the protein sequence was identified as an epitope) and B-cell epitopes using ElliPro Score (Protrusion Index representing surface accessibility of the epitope). Results The Burkina Faso PfRipr dataset predicted 1729 unique T-cell and 51 unique B-cell epitopes, and 28 T-cell and 4 B-cell epitopes remained post-filtration. Epitope prediction using the Ugandan dataset predicted 576 unique T-cell and 53 unique B-cell epitopes, and 57 T-cell and 7 B-cell epitopes remained post-filtration. Between the two datasets, there were 19 matching epitopes with 7 MHC I, 9 MHC II, 1 linear, and 2 discontinuous. (Tables 1-2, Figure 4) These epitopes were used to design a multi-epitope-based vaccine construct with a Flagellin adjuvant and peptide linkers to separate functional regions for immune processing. (Figure 5) Figure 4: 3D model of PfRipr with labeled B-cell epitopes. Predicted tertiary structure adapted from AlphaFold database. (https://doi.org/10.1038/s41586-021-03819-2, https://doi.org/10.1093/nar/gkab1061, https://creativecommons.org/licenses/by/4.0/) Figure 5: Schematic representation of hypothetical multi-epitope vaccine construct. FliC is added to the N-terminus to stimulate and amplify immune response. The EAAAK linker is used to separate the bifunctional fusion protein domains. AAY linkers are used to amplify MHC I and II epitopes and prevent junctional epitope binding. GPGPG linkers between B-cell epitopes function to minimize junctional epitopes and retain conformational-dependent immunogenicity. A six amino acid Histidine (6H) chain was added to the C-terminus to aid during lab purification processes. Conclusions Immunoinformatic tools employed in sequence identified candidate PfRipr epitopes used to design a bloodstage vaccine construct against P. falciparum. To validate their predicted immunogenicity, epitopes can be further investigated using in silico protein stabilization and docking simulations, in vitro methods such as HLA stabilization or T-cell activation assays, and in vivo methods using transgenic mouse models. The pipeline of immunoinformatic analyses formulated in this project can be further applied to a larger database of diverse P. falciparum sequences collected from other malaria endemic regions to develop vaccines effective against circulating strains.

B-Cell Epitope Predictions Using Computational Methods.

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

EPCES and EPSVR: Prediction of B-Cell Antigenic Epitopes on Protein Surfaces with Conformational Information.

Accurate prediction of discontinuous antigenic epitopes is important for immunologic research and medical applications, but it is not an easy problem. Currently, there are only a few prediction servers available, though discontinuous epitopes constitute the majority of all B-cell antigenic epitopes. In this chapter, we describe two online servers, EPCES and EPSVR, for discontinuous epitope prediction. All methods were benchmarked by a curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The servers and all datasets are available at http://sysbio.unl.edu/EPCES/ and http://sysbio.unl.edu/EPSVR/ .

Conformational B-Cell Epitope Prediction on Antigen Protein Structures: A Review of Current Algorithms and Comparison with Common Binding Site Prediction Methods

Accurate prediction of B-cell antigenic epitopes is important for immunologic research and medical applications, but compared with other bioinformatic problems, antigenic epitope prediction is more challenging because of the extreme variability of antigenic epitopes, where the paratope on the antibody binds specifically to a given epitope with high precision. In spite of the continuing efforts in the past decade, the problem remains unsolved and therefore still attracts a lot of attention from bioinformaticists. Recently, several discontinuous epitope prediction servers became available, and it is intriguing to review all existing methods and evaluate their performances on the same benchmark. In addition, these methods are also compared against common binding site prediction algorithms, since they have been frequently used as substitutes in the absence of good epitope prediction methods.

EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results

BackgroundAccurate prediction of antigenic epitopes is important for immunologic research and medical applications, but it is still an open problem in bioinformatics. The case for discontinuous epitopes is even worse - currently there are only a few discontinuous epitope prediction servers available, though discontinuous peptides constitute the majority of all B-cell antigenic epitopes. The small number of structures for antigen-antibody complexes limits the development of reliable discontinuous epitope prediction methods and an unbiased benchmark to evaluate developed methods.ResultsIn this work, we present two novel server applications for discontinuous epitope prediction: EPSVR and EPMeta, where EPMeta is a meta server. EPSVR, EPMeta, and datasets are available at http://sysbio.unl.edu/services.ConclusionThe server application for discontinuous epitope prediction, EPSVR, uses a Support Vector Regression (SVR) method to integrate six scoring terms. Furthermore, we combined EPSVR with five existing epitope prediction servers to construct EPMeta. All methods were benchmarked by our curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The area under the receiver operating characteristic curve (AUC) of EPSVR was 0.597, higher than that of any other existing single server, and EPMeta had a better performance than any single server - with an AUC of 0.638, significantly higher than PEPITO and Disctope (p-value < 0.05).

PEPOP: Computational design of immunogenic peptides

BackgroundMost methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.ResultsPEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated.ConclusionPEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at .

Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.

Discontinuous epitope prediction based on mimotope analysis

Phage display is a widespread technique used to obtain peptide mimotopes selected by binding to a given monoclonal antibody in a similar way as the native epitope. However, the localization of the interaction site mimicked by the mimotopes on the surface of the antigen is not always a straightforward task. MIMOP is a computational tool developed with the aim of helping experimentalists to analyze a set of mimotope sequences and guide them in the identification of the mimicked region. To predict potential epitopic regions, MIMOP integrates two different approaches combining two- and three-dimensional analyses: MimAlign starts from degenerated alignment analyses, and MimCons is based on consensus identification. The relevance and usefulness of the tool are illustrated by four use cases corresponding to real-life situations.