Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

An immuno-informatics approach for annotation of hypothetical proteins and multi-epitope vaccine designed against the Mpox virus

A worrying new outbreak of Monkeypox (Mpox) in humans is caused by the Mpox virus (MpoxV). The pathogen has roughly 28 hypothetical proteins of unknown structure, function, and pathogenicity. Using reliable bioinformatics tools, we attempted to analyze the MpoxV genome, identify the role of hypothetical proteins (HPs), and design a potential candidate vaccine. Out of 28, we identified seven hypothetical proteins using multi-server validation with high confidence for the occurrence of conserved domains. Their physical, chemical, and functional characterizations, including molecular weight, theoretical isoelectric point, 3D structures, GRAVY value, subcellular localization, functional motifs, antigenicity, and virulence factors, were performed. We predicted possible cytotoxic T cell (CTL), helper T cell (HTL) and linear and conformational B cell epitopes, which were combined in a 219 amino acid multiepitope vaccine with human β defensin as a linker. This multi-epitopic vaccine was structurally modelled and docked with toll-like receptor-3 (TLR-3). The dynamical stability of the vaccine-TLR-3 docked complexes exhibited stable interactions based on RMSD and RMSF tests. Additionally, the modelled vaccine was cloned in-silico in an E. coli host to check the appropriate expression of the final vaccine built. Our results might conform to an immunogenic and safe vaccine, which would require further experimental validation.Communicated by Ramaswamy H. Sarma.

Marc van Regenmortel, personal recollections on a forward-thinking editor

Marc van Regenmortel was the Editor-in-Chief of the Journal of Molecular Recognition for the last 25 years. Without attempting to summarize Marc's exceptional career and achievements, we would like to tell the story of the tortuous and contingent path to the unravelling of a key molecular recognition process in antigenicity. Life is indeed full of contingencies and scientific life, full of meetings and random encounters, is prone to contingencies, a key element in discovery and innovation.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

Epitope Mapping of Lysozyme Using the Chinese Egg-Allergic Sera at Both Pooled and Individual Levels

To accurately map the B-cell linear epitopes of lysozyme (LYS) in eggs, five bioinformatics tools were first used to obtain the mimotopes. Afterward, based on the Chinese egg-allergic sera samples screened by the indirect enzyme-linked immunosorbent, the epitopes possessing the capability of binding to IgG/IgE were mapped at both pooled and individual levels by using overlapping peptides covering the complete amino acid sequence of LYS. Six B-cell linear epitopes and two dominant B-cell linear epitopes that could bind to LYS-sIgG were mapped for the first time. Seven IgE-binding epitopes and three dominant IgE-binding epitopes were also obtained. Furthermore, AA31-34 and AA88-91 were the shared dominant epitopes of LYS-sIgG and LYS-sIgE at pooled and individual levels. Overall, the mapped B-cell linear epitopes filled in the gaps in the study of LYS epitopes, and the results may provide theoretical support for the following immunotherapy of egg allergy.

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

Critical review of conformational B-cell epitope prediction methods

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Prediction of B cell epitopes in proteins using a novel sequence similarity-based method

Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast.

Refining the DC-targeting vaccination for preventing emerging infectious diseases

The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.

Characterization, Epitope Identification, and Cross-reactivity Analysis of Tropomyosin: An Important Allergen of Crassostrea angulata

Oyster is a common shellfish product in China, which is associated with food allergy. However, there is still lack of research on allergens in oysters. In this study, tropomyosin (TM), an important allergen of Crassostrea angulata, was purified and identified by mass spectrometry. Subsequently, TM was cloned and expressed, with a sequence of size 852 bp, encoding 284 amino acid residues. The results of circular dichroism, digestion assay, inhibition enzyme-linked immunosorbent assay, and basophil activation test showed that recombinant TM had similar physicochemical properties and immunological properties to native TM. Furthermore, two conformational mimotopes were obtained and 10 IgE linear epitopes were verified. Meanwhile, different degrees of cross-reactivity were observed between C. angulata TM and the other 8 shellfish TMs using antibodies and serological analysis, which may relate to the 3 conserved epitope regions. These findings are expected to provide a theoretical basis for the molecular diagnosis of oyster allergy and cross-reactivity among shellfish.

Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae

Background: Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs). Methodology: A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs. Principal findings: We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined. Conclusion: Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.

C3Mab-3: A Monoclonal Antibody for Mouse CC Chemokine Receptor 3 for Flow Cytometry

CC chemokine receptor 3 (CCR3) belongs to the G protein-coupled receptor family and is highly expressed in eosinophils and basophils. CCR3 is essential for recruiting eosinophils into the lung. Moreover, CCR3 was found in the serum of colorectal cancer patients higher than in the control group. Therefore, CCR3 will be a useful target for asthma and colorectal cancer diagnosis. This study developed a specific and sensitive monoclonal antibody (mAb) for mouse CCR3 (mCCR3), which is useful for flow cytometry using the Cell-Based Immunization and Screening method. The established anti-mCCR3 mAb, C₃Mab-3 (rat IgG_2a, kappa), reacted with mCCR3-overexpressed Chinese hamster ovary-K1 (CHO/mCCR3) cells through flow cytometry. C₃Mab-3 also reacted with P388 (mouse lymphoid neoplasma) and J774-1 (mouse macrophage-like) cells, which express mCCR3 endogenously. Kinetic analyses using flow cytometry indicated that K_Ds of C₃Mab-3 for CHO/mCCR3, P388, and J774-1 cells were 4.3 × 10^-8 M, 2.6 × 10^-7 M, and 2.4 × 10^-7 M, respectively. C₃Mab-3 could be a valuable tool for elucidating mCCR3-related biological response using flow cytometry.

LBCEPred: a machine learning model to predict linear B-cell epitopes

B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

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.

Organism-specific training improves performance of linear B-cell epitope prediction

MOTIVATION

In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predictive tools were developed under a generalist approach, training models with heterogeneous datasets to develop predictors that can be deployed for a wide variety of pathogens. However, continuous advances in processing power and the increasing amount of epitope data for a broad range of pathogens indicate that training organism or taxon-specific models may become a feasible alternative, with unexplored potential gains in predictive performance.

RESULTS

This article shows how organism-specific training of epitope prediction models can yield substantial performance gains across several quality metrics when compared to models trained with heterogeneous and hybrid data, and with a variety of widely used predictors from the literature. These results suggest a promising alternative for the development of custom-tailored predictive models with high predictive power, which can be easily implemented and deployed for the investigation of specific pathogens.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article, as well as the full reproducibility scripts, are available at https://github.com/fcampelo/OrgSpec-paper. The R package that implements the organism-specific pipeline functions is available at https://github.com/fcampelo/epitopes.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Emerging genetic diversity of SARS-CoV-2 RNA dependent RNA polymerase (RdRp) alters its B-cell epitopes

The RNA dependent RNA polymerase (RdRp) plays crucial role in virus life cycle by replicating the viral genome. The SARS-CoV-2 is an RNA virus that rapidly spread worldwide and acquired mutations. This study was carried out to identify mutations in RdRp as the SARS-CoV-2 spread in India. We compared 50217 RdRp sequences reported from India with the first reported RdRp sequence from Wuhan, China to identify 223 mutations acquired among Indian isolates. Our protein modelling study revealed that several mutants can potentially alter stability and flexibility of RdRp. We predicted the potential B cell epitopes contributed by RdRp and identified thirty-six linear continuous and twenty-five discontinuous epitopes. Among 223 RdRp mutants, 44% of them localises in the B cell epitopes region. Altogether, this study highlights the need to identify and characterize the variations in RdRp to understand the impact of these mutations on SARS-CoV-2.

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.

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize ‘peptidome’ representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Immunoinformatics aided design of peptide-based vaccines against ebolaviruses

Ebolaviruses are at the forefront of emerging viruses and present a very perceptible threat to global peace and harmony. In the last decade, Ebola virus disease has claimed more than 90% of total lives since its inception in 1976. Owing to multiple host immune evasion methods employed by the virus and the limitations of traditional vaccine development approaches, finding a globally effective and reliable counter measure against Ebola virus remains a challenge. Highly conserved peptide fragments belonging to critical viral proteins and containing multiple epitopes which have the capacity to interact with a wide array of HLA molecules present a viable solution. Immunoinformatics or computational immunology enables rapid screening and shortlisting of plausible epitopes with a high immunogenic potential, thus, supporting expeditious elucidation of efficacious vaccine candidates. In light of above facts, we describe a computational methodology in this chapter for identification of potent peptide vaccine candidates against human infecting viruses. By applying this stringent methodology, we were able to identify multiple, immunogenic ebolavirus peptide fragments which, after verification in animal models, might be considered as part of future synthetic Ebola vaccine.

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.

Comparative proteomic analysis to annotate the structural and functional association of the hypothetical proteins of S. maltophilia k279a and predict potential T and B cell targets for vaccination

Stenotrophomonas maltophilia is a multidrug-resistant bacterium with no precise clinical treatment. This bacterium can be a vital cause for death and different organ failures in immune-compromised, immune-competent, and long-time hospitalized patients. Extensive quorum sensing capability has become a challenge to develop new drugs against this pathogen. Moreover, the organism possesses about 789 proteins which function, structure, and pathogenesis remain obscured. In this piece of work, we tried to enlighten the aforementioned sectors using highly reliable bioinformatics tools validated by the scientific community. At first, the whole proteome sequence of the organism was retrieved and stored. Then we separated the hypothetical proteins and searched for the conserved domain with a high confidence level and multi-server validation, which resulted in 24 such proteins. Furthermore, all of their physical and chemical characterizations were performed, such as theoretical isoelectric point, molecular weight, GRAVY value, and many more. Besides, the subcellular localization, protein-protein interactions, functional motifs, 3D structures, antigenicity, and virulence factors were also evaluated. As an extension of this work, 'RTFAMSSER' and 'PAAPQPSAS' were predicted as potential T and B cell epitopes, respectively. We hope our findings will help in better understating the pathogenesis and smoothen the way to the cure.

Development of Anti-Mouse CC Chemokine Receptor 3 Monoclonal Antibodies for Flow Cytometry

CC chemokine receptor 3 (CCR3), also known as CD193, belongs to class A of G protein-coupled receptors and is present in high levels in eosinophils, basophils, and airway epithelial cells. CCR3 is considered the therapeutic target for human immunodeficiency virus (HIV) infections and allergic diseases; therefore, the development of sensitive monoclonal antibodies (mAbs) for CCR3 has been desired. This study aimed to establish a specific and sensitive mAb against mouse CCR3 (mCCR3) useful for flow cytometry analysis by employing the Cell-Based Immunization and Screening (CBIS) method. The generated anti-mCCR3 mAb, C₃Mab-2 (rat IgG_2b, kappa), was found to react with mCCR3-overexpressed Chinese hamster ovary-K1 (CHO/mCCR3) cells, according to flow cytometric analysis. Also, it reacted with P388 (mouse lymphoid neoplasm) or J774-1 (mouse macrophage-like) cells, which express endogenous mCCR3. Taken together, C₃Mab-2, generated by the CBIS method, can be a valuable tool for detecting mCCR3 on the surface of mouse cells.

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 compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10⁴ motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

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/ .

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.

The Immune Epitope Database and Analysis Resource Program 2003-2018: reflections and outlook

The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.

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.

Novel peptides screened by phage display peptide library can mimic epitopes of the FnBPA-A protein and induce protective immunity against Staphylococcus aureus in mice

Fibronectin‐binding protein A (FnBPA) is a key adhesin of Staphylococcus aureus, and the protein binding to fibrinogen and elastin is mediated by its N‐terminal A domain. Thus, FnBPA‐A has been considered a potential vaccine candidate, but the relevant epitopes are not fully understood. Here, purified rabbit anti‐FnBPA‐A antibodies were produced and used to screen for peptides corresponding to or mimicking the epitope of native FnBPA‐A protein by using a phage random 12‐mer peptide library. After four rounds of panning, 25 randomly selected phage clones were detected by phage‐ELISA and competition‐inhibition ELISA. Then, eight anti‐rFnBPA‐A antibody‐binding phage clones were selected for sequencing, and six different 12‐mer peptides were displayed by these phages. Although these displayed peptides shared no more than three consecutive amino acid residues identical to the sequence of FnBPA‐A, they could be recognized by the FnBPA‐A‐specific antibodies in vitro and could induce specific antibodies against FnBPA‐A in vivo, suggesting that these displayed peptides were mimotopes of FnBPA‐A. Finally, the protective efficiencies of these mimotopes were investigated by mouse vaccination and challenge experiments. Compared with that of control group mice, the relative percent survival of mice immunized with phage clones displaying a mimotope was 13.33% (C2 or C15), 0% (C8), 6.67% (C10), 26.67% (C19 or 1:2 mixture of C23 and C19), 53.33% (C23), 33.33% (1:1 mixture of C23 and C19), and 66.67% (2:1 mixture of C23 and C19). Overall, five peptides mimicking FnBPA‐A protein epitopes were obtained, and a partially protective immunity against S. aureus infection could be stimulated by these mimotope peptides in mice.

Novel overlapping subgraph clustering for the detection of antigen epitopes

Motivation

Antigens that contain overlapping epitopes have been occasionally reported. As current algorithms mainly take a one-antigen-one-epitope approach to the prediction of epitopes, they are not capable of detecting these multiple and overlapping epitopes accurately, or even those multiple and separated epitopes existing in some other antigens.

Results

We introduce a novel subgraph clustering algorithm for more accurate detection of epitopes. This algorithm takes graph partitions as seeds, and expands the seeds to merge overlapping subgraphs based on the term frequency-inverse document frequency (TF-IDF) featured similarity. Then, the merged subgraphs are each classified as an epitope or non-epitope. Tests of our algorithm were conducted on three newly collected datasets of antigens. In the first dataset, each antigen contains only a single epitope; in the second, each antigen contains only multiple and separated epitopes; and in the third, each antigen contains overlapping epitopes. The prediction performance of our algorithm is significantly better than the state-of-art methods. The lifts of the averaged f-scores on top of the best existing methods are 60, 75 and 22% for the single epitope detection, the multiple and separated epitopes detection, and the overlapping epitopes detection, respectively.

Availability and implementation

The source code is available at github.com/lzhlab/glep/.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family

Background

The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily

Results

The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily

Conclusions

Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.

Advances in In-silico B-cell Epitope Prediction

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

Purification of Plant ESCRT Proteins for Polyclonal Antibody Production

Most endosomal sorting complex required for transport (ESCRT)-III proteins are not fully functional when expressed as fusion of fluorescent or epitope tags, frequently making the use of specific antibodies the only available method for their detection. Heterologous expression of ESCRT-III proteins in bacteria often results in the formation of insoluble aggregates or inclusion bodies that interfere with their purification. However, inclusion bodies are usually pure protein aggregates with high antigenicity. In addition, since proteins within inclusion bodies are presented in a range of folding states, immunization with inclusion bodies can potentially result in antibodies with specificity for different folding states of the protein under study. We describe here a protocol to isolate bacterial inclusion bodies of plant ESCRT-III proteins for production of polyclonal antibodies. We also describe a nitrocellulose-based immunoaffinity purification method that allows the immobilization of ESCRT-III proteins and the subsequent isolation of specific antibodies from a crude serum.

Requirements for Empirical Immunogenicity Trials, Rather than Structure-Based Design, for Developing an Effective HIV Vaccine

The claim that it is possible to rationally design a structure-based HIV-1 vaccine is based on misconceptions regarding the nature of protein epitopes and of immunological specificity. Attempts to use reverse vaccinology to generate an HIV-1 vaccine on the basis of the structure of viral epitopes bound to monoclonal neutralizing antibodies have failed so far because it was not possible to extrapolate from an observed antigenic structure to the immunogenic structure required in a vaccine. Vaccine immunogenicity depends on numerous extrinsic factors such as the host immunoglobulin gene repertoire, the presence of various cellular and regulatory mechanisms in the immunized host and the process of antibody affinity maturation. All these factors played a role in the appearance of the neutralizing antibody used to select the epitope to be investigated as potential vaccine immunogen, but they cannot be expected to be present in identical form in the host to be vaccinated. It is possible to rationally design and optimize an epitope to fit one particular antibody molecule or to improve the paratope binding efficacy of a monoclonal antibody intended for passive immunotherapy. What is not possible is to rationally design an HIV-1 vaccine immunogen that will elicit a protective polyclonal antibody response of predetermined efficacy. An effective vaccine immunogen can only be discovered by investigating experimentally the immunogenicity of a candidate molecule and demonstrating its ability to induce a protective immune response. It cannot be discovered by determining which epitopes of an engineered antigen molecule are recognized by a neutralizing monoclonal antibody. This means that empirical immunogenicity trials rather than structural analyses of antigens offer the best hope of discovering an HIV-1 vaccine.

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.

Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I

Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.

Screening and identification of B-cell epitopes within envelope protein of tembusu virus

Background

Tembusu virus is a newly emerging flavivirus that caused egg-drop syndrome in ducks in China. TMUV envelope protein is a major structural protein locates at the surface of tembusu virus particle. During tembusu virus infection, envelope protein plays a pivotal role in induction of neutralizing antibody. However, B cell epitopes within envelope protein have not been well studied.

Method

A series of 13 peptides derived from E protein of tembusu virus were synthesized and screened by Dot blot with tembusu virus-positive duck serum. Potential B-cell epitopes were respectively fused with GST tag and expressed in E. coli. The immunogenicity and protective efficiency of epitopes were assessed in ducks.

Results

Dot blot assay identified the peptides P21 (amino acids 301–329), P23 (amino acids 369–387), P27 (amino acids 464–471) and P28 (amino acids 482–496) as potential B-cell epitopes within the envelope protein of tembusu virus. Immunization of prokaryotically expressed epitopes elicited specific antibodies in ducks and the specific antibody elicited by P21, P27 and P28 could neutralized tembusu virus. In addition, protective test suggested that P21 and P27 could completely protect immunized ducks from TMUV challenge.

Conclusion

Four potential B cell epiotpes within tembusu virus envelope protein were identified and analyzed in vitro and in vivo. It was demonstrated that two of them (P21 and P27) could elicit neutralizing antibodies in ducks and offer complete protection against tembusu virus challenge. This findings will contribute to the development of epitope vaccine for tembusu virus prevention.

Food Allergen Epitope Mapping

With the increased global awareness and rise in food allergies, a multifold interest in food allergens is evident. The presence of undeclared food allergens results in expensive food recalls and increased risks of anaphylaxis for the sensitive individuals. Regardless of the allergenic food, the immunogen needs to be identified and detected before making any efforts to inactivate/eliminate it. In type I food allergies, protein immunogen cross-links immunoglobulin E, leading to basophil/mast cell degranulation, resulting in the symptoms that range from mild irritation to anaphylaxis. A portion/part of the protein, known as the epitope, can interact with either antibodies to elicit allergic reactions or T-cell receptors to initiate allergic sensitization. Antibody-recognized epitopes can be either a linear sequence of amino acids (linear epitope) or a three-dimensional motif (conformational epitope), while T-cell-receptor-recognized epitopes are exclusively linear peptides. Identifying and characterizing human-allergy-relevant epitopes are important for allergy diagnosis/prognosis, immunotherapy, and developing food processing methods that can reduce/eliminate immunogencity/immunoreactivity of the allergen.

Challenges related to the immunogenicity of parenteral recombinant proteins: Underlying mechanisms and new approaches to overcome it

Immune response elicited by therapeutic proteins is an important safety and efficacy issue for regulatory agencies, drug manufacturers, clinicians, and patients. Administration of therapeutic proteins can potentially induce the production of anti-drug antibodies or cell-mediated immune responses. At first, it was speculated that the immunogenicity is related to the non-human origin of these proteins. Later on, it was confirmed that the human proteins may also show immunogenicity. In this review article, we will focus on a number of factors, which play crucial roles in the human protein immunogenicity. These factors are related to the patient's status (or intrinsic properties) and molecular characteristics of the therapeutic protein's (or extrinsic properties). Furthermore, we will discuss available in silico, in vitro, and in vivo methods for the prediction of sequences, which may generate an immune response following parenteral administration of these proteins. In summary, nowadays, it is possible for drug manufacturers to evaluate the risk of immunogenicity of therapeutic proteins and implement a management plan to overcome the problems prior to proceeding to human clinical trials.

Establishment of the cross-clade antigen detection system for H5 subtype influenza viruses using peptide monoclonal antibodies specific for influenza virus H5 hemagglutinin

The H5 subtype of highly pathogenic avian influenza (H5 HPAI) viruses is a threat to both animal and human public health and has the potential to cause a serious future pandemic in humans. Thus, specific and rapid detection of H5 HPAI viruses is required for infection control in humans. To develop a simple and rapid diagnostic system to detect H5 HPAI viruses with high specificity and sensitivity, we attempted to prepare monoclonal antibodies (mAbs) that specifically recognize linear epitopes in hemagglutinin (HA) of H5 subtype viruses. Nine mAb clones were obtained from mice immunized with a synthetic partial peptide of H5 HA molecules conserved among various H5 HPAI viruses. The antigen-capture enzyme-linked immunosorbent assay using the most suitable combination of these mAbs, which bound specifically to lysed H5 HA under an optimized detergent condition, was specific for H5 viruses and could broadly detect H5 viruses in multiple different clades. Taken together, these peptide mAbs, which recognize linear epitopes in a highly conserved region of H5 HA, may be useful for specific and highly sensitive detection of H5 HPAI viruses and can help in the rapid diagnosis of human, avian, and animal H5 virus infections.

Decreasing the immunogenicity of arginine deiminase enzyme via structure-based computational analysis

The clinical applications of therapeutic enzymes are often limited due to their immunogenicity. B-cell epitope removal is an effective approach to solve this obstacle. The identification of hot spot epitopic residues is a critical step in the removal of protein B-cell epitope. Hereof, computational approaches are a suitable alternative to costly and labor-intensive experimental approaches. Arginine deiminase, a Mycoplasma arginine-catabolizing enzyme, is in the clinical trial for treating arginine auxotrophic cancers, especially hepatocellular carcinomas and melanomas through depleting plasma arginine and causing cell starvation. In this study, arginine deiminase from Mycoplasma hominis (MhADI) was computationally analyzed for recognizing and locating its immune-reactive regions. The 3D structure of the bioactive form of MhADI was modeled. The B-cell epitope mapping of protein was performed using various servers with different algorithms. Six segments: 31-40, 48-55, 131-140, 196-206, 294-314, and 331-344 were predicted to be the consensus immunogenic regions. The modification of epitopic hot spot residue was performed to reduce immune-reactiveness. The hot spot residue was selected considering a high B-cell epitope score, convexity index, surface accessibility, flexibility, and hydrophilicity. The structure stability of native and mutant proteins was evaluated through molecular dynamics simulation. The E304L mutein was suggested as a lower antigenic and stable enzyme derivative.

An Introduction to Epitope Mapping

Antibodies are protein molecules used routinely for therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and for proteinaceous antigens the epitope can be of complex nature. Information about the binding epitope of an antibody can provide important mechanistic insights and indicate for what applications an antibody might be useful. Therefore, a variety of epitope mapping techniques have been developed to localize such regions. Although the real picture is even more complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping of the two different classes of epitopes, using high-throughput or high-resolution methods, have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Early approaches utilizing sets of truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high-throughput screening in conjunction with bioinformatic analyses of large datasets.

Designing of CD8+ and CD8+-overlapped CD4+ epitope vaccine by targeting late and early proteins of human papillomavirus

BACKGROUND AND AIM

Human papillomavirus (HPV) is an oncogenic agent that causes over 90% of cases of cervical cancer in the world. Currently available prophylactic vaccines are type specific and have less therapeutic efficiency. Therefore, we aimed to predict the potential species-specific and therapeutic epitopes from the protein sequences of HPV45 by using different immunoinformatics tools.

METHODS

Initially, we determined the antigenic potential of late (L1 and L2) and early (E1, E2, E4, E5, E6, and E7) proteins. Then, major histocompatibility complex class I-restricted CD8⁺ T-cell epitopes were selected based on their immunogenicity. In addition, epitope conservancy, population coverage (PC), and target receptor-binding affinity of the immunogenic epitopes were determined. Moreover, we predicted the possible CD8⁺, nested interferon gamma (IFN-γ)-producing CD4⁺, and linear B-cell epitopes. Further, antigenicity, allergenicity, immunogenicity, and system biology-based virtual pathway associated with cervical cancer were predicted to confirm the therapeutic efficiency of overlapped epitopes.

RESULTS

Twenty-seven immunogenic epitopes were found to exhibit cross-protection (≥55%) against the 15 high-risk HPV strains (16, 18, 31, 33, 35, 39, 51, 52, 56, 58, 59, 68, 69, 73, and 82). The highest PC was observed in Europe (96.30%), North America (93.98%), West Indies (90.34%), North Africa (90.14%), and East Asia (89.47%). Binding affinities of 79 docked complexes observed as global energy ranged from -10.80 to -86.71 kcal/mol. In addition, CD8⁺ epitope-overlapped segments in CD4⁺ and B-cell epitopes demonstrated that immunogenicity and IFN-γ-producing efficiency ranged from 0.0483 to 0.5941 and 0.046 to 18, respectively. Further, time core simulation revealed the overlapped epitopes involved in pRb, p53, COX-2, NF-X1, and HPV45 infection signaling pathways.

CONCLUSION

Even though the results of this study need to be confirmed by further experimental peptide sensitization studies, the findings on immunogenic and IFN-γ-producing CD8⁺ and overlapped epitopes provide new insights into HPV vaccine development.