Prediction of linear B-cell epitopes

A Vaccine Strategy Based on the Identification of an Annular Ganglioside Binding Motif in Monkeypox Virus Protein E8L

The recent outbreak of Monkeypox virus requires the development of a vaccine specifically directed against this virus as quickly as possible. We propose here a new strategy based on a two-step analysis combining (i) the search for binding domains of viral proteins to gangliosides present in lipid rafts of host cells, and (ii) B epitope predictions. Based on previous studies of HIV and SARS-CoV-2 proteins, we show that the Monkeypox virus cell surface-binding protein E8L possesses a ganglioside-binding motif consisting of several subsites forming a ring structure. The binding of the E8L protein to a cluster of gangliosides GM1 mimicking a lipid raft domain is driven by both shape and electrostatic surface potential complementarities. An induced-fit mechanism unmasks selected amino acid side chains of the motif without significantly affecting the secondary structure of the protein. The ganglioside-binding motif overlaps three potential linear B epitopes that are well exposed on the unbound E8L surface that faces the host cell membrane. This situation is ideal for generating neutralizing antibodies. We thus suggest using these three sequences derived from the E8L protein as immunogens in a vaccine formulation (recombinant protein, synthetic peptides or genetically based) specific for Monkeypox virus. This lipid raft/ganglioside-based strategy could be used for developing therapeutic and vaccine responses to future virus outbreaks, in parallel to existing solutions.

Computational approach for predicting the conserved B-cell epitopes of hemagglutinin H7 subtype influenza virus

An avian-origin influenza H7N9 virus epidemic occurred in China in 2013–2014, in which >422 infected people suffered from pneumonia, respiratory distress syndrome and septic shock. H7N9 viruses belong to the H7 subtype of avian-origin influenza viruses (AIV-H7). Hemagglutinin (HA) is a vital membrane protein of AIV that has an important role in host recognition and infection. The epitopes of HA are significant determinants of the regularity of epidemic and viral mutation and recombination mechanisms. The present study aimed to predict the conserved B-cell epitopes of AIV-H7 HA using a bioinformatics approach, including the three most effective epitope prediction softwares available online: Artificial Neural Network based B-cell Epitope Prediction (ABCpred), B-cell Epitope Prediction (BepiPred) and Linear B-cell Epitope Prediction (LBtope). A total of 24 strains of Euro-Asiatic AIV-H7 that had been associated with a serious poultry pandemic or had infected humans in the past 30 years were selected to identify the conserved regions of HA. Sequences were obtained from the National Center for Biotechnology Information and Global Initiative on Sharing Avian Influenza Data databases. Using a combination of software prediction and sequence comparisons, the conserved epitopes of AIV-H7 were predicted and clarified. A total of five conserved epitopes [amino acids (aa) 37–52, 131–142, 215–234, 465–484 and 487–505] with a suitable length, high antigenicity and minimal variation were predicted and confirmed. Each obtained a score of >0.80 in ABCpred, 60% in LBtope and a level of 0.35 in Bepipred. In addition, a representative amino acid change (glutamine²³⁵-to-leucine²³⁵) in the HA protein of the 2013 AIV-H7N9 was discovered. The strategy adopted in the present study may have profound implications on the rapid diagnosis and control of infectious disease caused by H7N9 viruses, as well as by other virulent viruses, such as the Ebola virus.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Classification epitopes in groups based on their protein family

Background

The humoral immune system response is based on the interaction between antibodies and antigens for the clearance of pathogens and foreign molecules. The interaction between these proteins occurs at specific positions known as antigenic determinants or B-cell epitopes. The experimental identification of epitopes is costly and time consuming. Therefore the use of in silico methods, to help discover new epitopes, is an appealing alternative due the importance of biomedical applications such as vaccine design, disease diagnostic, anti-venoms and immune-therapeutics. However, the performance of predictions is not optimal been around 70% of accuracy. Further research could increase our understanding of the biochemical and structural properties that characterize a B-cell epitope.

Results

We investigated the possibility of linear epitopes from the same protein family to share common properties. This hypothesis led us to analyze physico-chemical (PCP) and predicted secondary structure (PSS) features of a curated dataset of epitope sequences available in the literature belonging to two different groups of antigens (metalloproteinases and neurotoxins). We discovered statistically significant parameters with data mining techniques which allow us to distinguish neurotoxin from metalloproteinase and these two from random sequences. After a five cross fold validation we found that PCP based models obtained area under the curve values (AUC) and accuracy above 0.9 for regression, decision tree and support vector machine.

Conclusions

We demonstrated that antigen's family can be inferred from properties within a single group of linear epitopes (metalloproteinases or neurotoxins). Also we discovered the characteristics that represent these two epitope groups including their similarities and differences with random peptides and their respective amino acid sequence. These findings open new perspectives to improve epitope prediction by considering the specific antigen's protein family. We expect that these findings will help to improve current computational mapping methods based on physico-chemical due it's potential application during epitope discovery.

Design and Antigenic Epitopes Prediction of a New Trial Recombinant Multiepitopic Rotaviral Vaccine: In Silico Analyses

Rotavirus is the major etiologic factor of severe diarrheal disease. Natural infection provides protection against subsequent rotavirus infection and diarrhea. This research presents a new vaccine designed based on computational models. In this study, three types of epitopes are considered-linear, conformational, and combinational-in a proposed model protein. Several studies on rotavirus vaccines have shown that VP6 and VP4 proteins are good candidates for vaccine production. In the present study, a fusion protein was designed as a new generation of rotavirus vaccines by bioinformatics analyses. This model-based study using ABCpred, BCPREDS, Bcepred, and Ellipro web servers showed that the peptide presented in this article has the necessary properties to act as a vaccine. Prediction of linear B-cell epitopes of peptides is helpful to investigate whether these peptides are able to activate humoral immunity.

Chimerically fused antigen rich of overlapped epitopes from latent membrane protein 2 (LMP2) of Epstein-Barr virus as a potential vaccine and diagnostic agent

Epstein-Barr virus (EBV) is prevalent throughout the world and is associated with several malignant diseases in humans. Latent membrane protein 2 (LMP2) of EBV plays a crucial role in the pathogenesis of EBV-associated tumors; therefore, LMP2 has been considered to be a potential immunodiagnostic and immunotherapeutic target. A multi-epitope-based antigen is a promising option for therapeutic vaccines and diagnoses of such malignancies. In this study, we systematically screened cytotoxic T lymphocyte (CTL), helper T cell (Th) and B-cell epitopes within EBV-LMP2 using bioinformatics. Based on the screen, two peptides rich in overlapping epitopes of both T cells and B cells were selected to construct a plasmid containing the sequence for a chimeric multi-epitope protein referred to as EBV-LMP2m, which is composed of LMP2aa195∼232 and LMP2aa419∼436. The EBV-LMP2m protein was expressed in E. coli BL21 (DE3) after prokaryotic codon optimization. Inoculation of the purified chimeric antigen in BALB/c mice induced not only high levels of specific IgG in the serum and secretory IgA in the vaginal mucus but also a specific CTL response. By using purified EBV-LMP2m as an antigen, the presence of specific IgG in the serum specimens of 202 nasopharyngeal carcinoma (NPC) patients was effectively detected with 52.84% sensitivity and 95.40% specificity, which represents an improvement over the traditional detection method based on VCA-IgA (60.53% sensitivity and 76.86% specificity). The above results indicate that EBV-LMP2m may be used not only as a potential target antigen for EBV-associated tumors but also a diagnostic agent for NPC patients.

Prediction of Antibody Epitopes

Antibodies recognize their cognate antigens in a precise and effective way. In order to do so, they target regions of the antigenic molecules that have specific features such as large exposed areas, presence of charged or polar atoms, specific secondary structure elements, and lack of similarity to self-proteins. Given the sequence or the structure of a protein of interest, several methods exploit such features to predict the residues that are more likely to be recognized by an immunoglobulin. Here, we present two methods (BepiPred and DiscoTope) to predict linear and discontinuous antibody epitopes from the sequence and/or the three-dimensional structure of a target protein.

Hybrid methods for B-cell epitope prediction

Many computational approaches to B-cell epitope prediction have been published, including combinations of previously proposed methods, which complicates the tasks of further developing such computational approaches and of selecting those most appropriate for practical applications (e.g., the design of novel immunodiagnostics and vaccines). These tasks are considered together herein to clarify their close but often overlooked interrelationship, thereby providing a guide to their performance in mutual support of one another, with emphasis on key physicochemical and biological considerations that are relevant from an applications perspective. This aims to assist investigators in performing either or both tasks, with the overall goals of successfully applying computational tools towards practical ends and of generating informative new data towards iterative improvement of the tools, particularly as regards the design of peptide-based immunogens for eliciting the production of antipeptide antibodies that modulate biological activity of protein targets via functionally relevant cross-reactivity in relation to the phenomena of protein folding and protein disorder.

Databases and in silico tools for vaccine design

In vaccine design, databases and in silico tools play different but complementary roles. Databases collect experimentally verified vaccines and vaccine components, and in silico tools provide computational methods to predict and design new vaccines and vaccine components. Vaccine-related databases include databases of vaccines and vaccine components. In the USA, the Food and Drug Administration (FDA) maintains a database of licensed human vaccines, and the US Department of Agriculture keeps a database of licensed animal vaccines. Databases of vaccine clinical trials and vaccines in research also exist. The important vaccine components include vaccine antigens, vaccine adjuvants, vaccine vectors, and -vaccine preservatives. The vaccine antigens can be whole proteins or immune epitopes. Various in silico vaccine design tools are also available. The Vaccine Investigation and Online Information Network (VIOLIN; http://www.violinet.org ) is a comprehensive vaccine database and analysis system. The VIOLIN database includes various types of vaccines and vaccine components. VIOLIN also includes Vaxign, a Web-based in silico vaccine design program based on the reverse vaccinology strategy. Vaccine information and resources can be integrated with Vaccine Ontology (VO). This chapter introduces databases and in silico tools that facilitate vaccine design, especially those in the VIOLIN system.

B-cell epitope prediction through a graph model

BACKGROUND

Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist.

RESULTS

In this paper, we propose to divide an antigen surface graph into subgraphs by using a Markov Clustering algorithm, and then we construct a classifier to distinguish these subgraphs as epitope or non-epitope subgraphs. This classifier is then taken to predict epitopes for a test antigen. On a big data set comprising 92 antigen-antibody PDB complexes, our method significantly outperforms the state-of-the-art epitope prediction methods, achieving 24.7% higher averaged f-score than the best existing models. In particular, our method can successfully identify those epitopes with a non-planarity which is too small to be addressed by the other models. Our method can also detect multiple epitopes whenever they exist.

CONCLUSIONS

Various protrusive and planar patches at the surface of antigens can be distinguishable by using graphical models combined with unsupervised clustering and supervised learning ideas. The difficult problem of identifying multiple epitopes from an antigen can be made easied by using our subgraph approach. The outstanding residue combinations found in the supervised learning will be useful for us to form new hypothesis in future studies.

A guide to in silico vaccine discovery for eukaryotic pathogens

In this article, a framework for an in silico pipeline is presented as a guide to high-throughput vaccine candidate discovery for eukaryotic pathogens, such as helminths and protozoa. Eukaryotic pathogens are mostly parasitic and cause some of the most damaging and difficult to treat diseases in humans and livestock. Consequently, these parasitic pathogens have a significant impact on economy and human health. The pipeline is based on the principle of reverse vaccinology and is constructed from freely available bioinformatics programs. There are several successful applications of reverse vaccinology to the discovery of subunit vaccines against prokaryotic pathogens but not yet against eukaryotic pathogens. The overriding aim of the pipeline, which focuses on eukaryotic pathogens, is to generate through computational processes of elimination and evidence gathering a ranked list of proteins based on a scoring system. These proteins are either surface components of the target pathogen or are secreted by the pathogen and are of a type known to be antigenic. No perfect predictive method is yet available; therefore, the highest-scoring proteins from the list require laboratory validation.

B-Pred, a structure based B-cell epitopes prediction server

The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein's peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window's width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

Frequent associations between CTL and T-Helper epitopes in HIV-1 genomes and implications for multi-epitope vaccine designs

BACKGROUND

Epitope vaccines have been suggested as a strategy to counteract viral escape and development of drug resistance. Multiple studies have shown that Cytotoxic T-Lymphocyte (CTL) and T-Helper (Th) epitopes can generate strong immune responses in Human Immunodeficiency Virus (HIV-1). However, not much is known about the relationship among different types of HIV epitopes, particularly those epitopes that can be considered potential candidates for inclusion in the multi-epitope vaccines.

RESULTS

In this study we used association rule mining to examine relationship between different types of epitopes (CTL, Th and antibody epitopes) from nine protein-coding HIV-1 genes to identify strong associations as potent multi-epitope vaccine candidates. Our results revealed 137 association rules that were consistently present in the majority of reference and non-reference HIV-1 genomes and included epitopes of two different types (CTL and Th) from three different genes (Gag, Pol and Nef). These rules involved 14 non-overlapping epitope regions that frequently co-occurred despite high mutation and recombination rates, including in genomes of circulating recombinant forms. These epitope regions were also highly conserved at both the amino acid and nucleotide levels indicating strong purifying selection driven by functional and/or structural constraints and hence, the diminished likelihood of successful escape mutations.

CONCLUSIONS

Our results provide a comprehensive systematic survey of CTL, Th and Ab epitopes that are both highly conserved and co-occur together among all subtypes of HIV-1, including circulating recombinant forms. Several co-occurring epitope combinations were identified as potent candidates for inclusion in multi-epitope vaccines, including epitopes that are immuno-responsive to different arms of the host immune machinery and can enable stronger and more efficient immune responses, similar to responses achieved with adjuvant therapies. Signature of strong purifying selection acting at the nucleotide level of the associated epitopes indicates that these regions are functionally critical, although the exact reasons behind such sequence conservation remain to be elucidated.

Prospects for defined epitope vaccines for respiratory syncytial virus

The history of vaccines for respiratory syncytial virus (RSV) illustrates the complex immunity and immunopathology to this ubiquitous virus, starting from the failed formalin-inactivated vaccine trials performed in the 1960s. An attractive alternative to traditional live or killed virus vaccines is a defined vaccine composed of discrete antigenic epitopes for which immunological activities have been characterized as comprehensively as possible. Here we present cumulative data on murine and human CD4, CD8 and neutralization epitopes identified in RSV proteins along with information regarding their associated immune responses and host-dependent variability. Identification and characterization of RSV epitopes is a rapidly expanding topic of research with potential contributions to the tailored design of improved safe and effective vaccines.

Benchmarking B-cell epitope prediction for the design of peptide-based vaccines: problems and prospects

To better support the design of peptide-based vaccines, refinement of methods to predict B-cell epitopes necessitates meaningful benchmarking against empirical data on the cross-reactivity of polyclonal antipeptide antibodies with proteins, such that the positive data reflect functionally relevant cross-reactivity (which is consistent with antibody-mediated change in protein function) and the negative data reflect genuine absence of cross-reactivity (rather than apparent absence of cross-reactivity due to artifactual masking of B-cell epitopes in immunoassays). These data are heterogeneous in view of multiple factors that complicate B-cell epitope prediction, notably physicochemical factors that define key structural differences between immunizing peptides and their cognate proteins (e.g., unmatched electrical charges along the peptide-protein sequence alignments). If the data are partitioned with respect to these factors, iterative parallel benchmarking against the resulting subsets of data provides a basis for systematically identifying and addressing the limitations of methods for B-cell epitope prediction as applied to vaccine design.

Advances in the design and delivery of peptide subunit vaccines with a focus on toll-like receptor agonists

Considerable success has been made with many peptide antigen formulations, and peptide-based vaccines are emerging as the next generation of prophylactic and remedial immunotherapy. However, finding an optimal platform balancing all of the requirements for an effective, specific and safe immune response remains a major challenge for many infectious and chronic diseases. This review outlines how peptide immunogenicity is influenced by the way in which peptides are presented to the immune system, underscoring the need for multifunctional delivery systems that couple antigen and adjuvant into a single construct. Particular attention is given to the ability of Toll-like receptor agonists to act as adjuvants. A survey of recent approaches to developing peptide antigen delivery systems is given, many of which incorporate Toll-like receptor agonists into the design.

Removal of B cell epitopes as a practical approach for reducing the immunogenicity of foreign protein-based therapeutics

Immunogenicity of non-human proteins with useful therapeutic properties has prevented their development for use in the therapy of disease. However, this class of proteins could be very useful, if their immunogenicity could be markedly reduced so that many treatment cycles could be administered. One approach to reduce the immunogenicity of foreign proteins is to identify B cell epitopes on the protein and eliminate them by mutagenesis. In this article, theoretical aspects and experimental evidence for the feasibility of B cell epitope removal is reviewed. A special focus is given to our results with deimmunization of recombinant immunotoxins in which Fvs are fused to a 38kDa portion of the bacterial protein, Pseudomonas exotoxin A (PE38). Immunotoxins targeting CD22 and CD25 have produced complete remissions in many patients with drug resistant Hairy Cell Leukemia and are being evaluated in other malignancies. Experimental data summarized in this review indicates that removal of B cell epitopes is a practical approach for making less immunogenic protein therapeutics from non-human functional proteins. This approach requires grouping of the epitopes to identify targets for deimmunization followed by quantitative analysis of the decrease in affinity produced by the mutations in B cell epitopes.