Conformational B-cell Epitopes Research Articles

Phylogenetic analysis and antigenic epitope prediction for E6 and E7 of Alpha-papillomavirus 9 in Taizhou, China.

Alpha-papillomavirus 9 (α-9) is a member of the human papillomavirus (HPV) α genus, causing 75% invasive cervical cancers worldwide. The purpose of this study was to provide data for effective treatment of HPV-induced cervical lesions in Taizhou by analysing the genetic variation and antigenic epitopes of α-9 HPV E6 and E7. Cervical exfoliated cells were collected for HPV genotyping. Positive samples of the α-9 HPV single type were selected for E6 and E7 gene sequencing. The obtained nucleotide sequences were translated into amino acid sequences (protein primary structure) using MEGA X, and positive selection sites of the amino acid sequences were evaluated using PAML. The secondary and tertiary structures of the E6 and E7 proteins were predicted using PSIPred, SWISS-MODEL, and PyMol. Potential T/B-cell epitopes were predicted by Industrial Engineering Database (IEDB). From 2012 to 2023, α-9 HPV accounted for 75.0% (7815/10423) of high-risk HPV-positive samples in Taizhou, both alone and in combination with other types. Among these, single-type-positive samples of α-9 HPV were selected, and the entire E6 and E7 genes were sequenced, including 298 HPV16, 149 HPV31, 185 HPV33, 123 HPV35, 325 HPV52, and 199 HPV58 samples. Compared with reference sequences, 34, 12, 10, 2, 17, and 17 nonsynonymous nucleotide mutations were detected in HPV16, 31, 33, 35, 52, and 58, respectively. Among all nonsynonymous nucleotide mutations, 19 positive selection sites were selected, which may have evolutionary significance in rendering α-9 HPV adaptive to its environment. Immunoinformatics predicted 57 potential linear and 59 conformational B-cell epitopes, many of which are also predicted as CTL epitopes. The present study provides almost comprehensive data on the genetic variations, phylogenetics, positive selection sites, and antigenic epitopes of α-9 HPV E6 and E7 in Taizhou, China, which will be helpful for local HPV therapeutic vaccine development.

SEMA 2.0: web-platform for B-cell conformational epitopes prediction using artificial intelligence.

Prediction of conformational B-cell epitopes is a crucial task in vaccine design and development. In this work, we have developed SEMA 2.0, a user-friendly web platform that enables the research community to tackle the B-cell epitopes prediction problem using state-of-the-art protein language models. SEMA 2.0 offers comprehensive research tools for sequence- and structure-based conformational B-cell epitopes prediction, accurate identification of N-glycosylation sites, and a distinctive module for comparing the structures of antigen B-cell epitopes enhancing our ability to analyze and understand its immunogenic properties. SEMA 2.0 website https://sema.airi.net is free and open to all users and there is no login requirement. Source code is available at https://github.com/AIRI-Institute/SEMAi.

Designing and Developing Serological Test for the Diagnosis of Human Fascioliasis Using a New Recombinant Multi-epitope.

Fascioliasis is a common parasitic disease in humans and herbivores which is caused by Fasciola hepatica and Fasciola gigantica and has a worldwide distribution. Serological tests such as the enzyme-linked immunosorbent assay (ELISA) technique play a prominent role in the fast diagnosis of the disease. However, there are diagnostic limitations, including cross-reactivity with other worms, which decline the specificity of the results. This study aimed to evaluate the structure of a recombinant multi-epitope antigen produced from linear and conformational B-cell epitopes of three parasitic proteins with sera of individuals with fasciolosis, healthy controls, and those with other diseases to gain accurate sensitivity and specificity. After designing the multi-epitope structure of cathepsin L1, FhTP16.5, and SAP-2 antigens and then synthesizing, cloning, and expressing, the extracted purified protein was evaluated by indirect ELISA to detect IgG antibodies against Fasciola hepatica parasite among the sera of 39 serum samples of Fasciola hepatica, 35 healthy individual samples, and 20 samples of other types of parasitic diseases. The synthesized multi-epitope produced from cathepsin L1, FhTP16.5, and SAP-2 antigens was evaluated using the indirect ELISA. The analysis of the samples mentioned for IgG antibody diagnosis against Fasciola hepatica showed 97.43% (95% confidence interval, 94.23-100%) sensitivity and 100% (95% confidence interval, 97-100%) specificity. The recombinant B-cell multi-epitope with high antigenic potency may increase the specificity of epitopic peptides and ultimately help improve and develop indirect ELISA commercial kits for the diagnosis of fascioliasis in humans.

Designing a conjugate vaccine targeting Klebsiella pneumoniae ST258 and ST11

Klebsiella pneumoniae (K. pneumoniae) is a common bacterium that can cause iatrogenic infection. Recently, the rise of antibiotic resistance among K. pneumoniae strains is one key factor associated with antibiotic treatment failure. Hencefore, there is an urgent need for effective K. pneumoniae vaccines. This study aimed to design a multi-epitope vaccine (MEV) candidate against K. pneumonia by utilizing an immunoinformatics method. In this study, we obtained 15 cytotoxic T lymphocyte epitopes, 10 helper T lymphocyte epitopes, 6 linear B-cell epitopes, and 2 conformational B-cell epitopes for further research. Then, we designed a multi-epitope vaccine composed of a total of 743 amino acids, containing the epitopes linked by GPGPG flexible links and an EAAAK linker to the Cholera Toxin Subunit B coadjuvant.The observed properties of the MEV, including non-allergenicity, high antigenicity, and hydrophilicity, are noteworthy. The improvements in the tertiary structure through structural refinement and disulfide bonding, coupled with promising molecular interactions revealed by molecular dynamics simulations with TLR4, position the MEV as a strong candidate for further investigation against K. pneumoniae.

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named “CLBTope” has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).

Improved detection sensitivity of anti-PRV variant antibodies through preparation of anti-gB and anti-gE monoclonal antibodies and development of blocking ELISAs

Since 2011, PRV has resurged in China and is characterized by a mutated strain with significant alterations in antigenicity and virulence. Therefore, we hypothesized that antibody detection kits based on classic PRV strains may have limitations in detecting PRV variants. For more sensitive antibody detection of PRV variants, two MABs targeting the gB and gE proteins were developed. IFA revealed that these MABs exhibited strong reactivity toward both classic and variant PRV strains. MAB-gE recognizes a novel conserved linear B-cell epitope (41PSAEVWD47), while MAB-gB recognizes a conformational B-cell epitope. The binding of both MABs was effectively inhibited in the PRV-positive pig blood samples. Accordingly, we established blocking-ELISAs to detect anti-PRV gB and gE antibodies, which achieved higher sensitivity than commercial kits. Moreover, the clinical serum samples results of our method and that of IFA were in high agreement, and our test results had a higher coincidence rate than that of a commercial kit. Assessing antibody levels by our methods at various times following immunization and challenge accurately reflected the trend of antibody-level changes and revealed the conversion to positive antibody status before the commercial kit. Our method is crucial for monitoring PRV infections, assessing immune responses, and controlling disease.

In silico immunoinformatics based prediction and designing of multi-epitope construct against human rhinovirus C

Human rhinovirus C (HRV-C) is an RNA virus infecting human respiratory tract. It is associated with complexities like asthma, chronic obstructive pulmonary disease, and respiratory damage. HRV-C has many serotypes. Till date there is no vaccine. Despite some limitations, corticosteroids, bronchodilators, and common cold medicines are used to treat HRV-C infections. Here, we have used immunoinformatics approach to predict suitable cytotoxic T-cell, helper T-cell and linear B-cell epitopes from the most antigenic protein. VP2 protein of Rhinovirus C53 strain USA/CO/2014-20993 was found to be most antigenic. The multi-epitope construct was designed using the best CTL, HTL and linear B-cell epitopes and attaching them with adjuvant and linkers. Interferon-gamma inducing epitopes and conformational B-cell epitopes were also predicted from the construct. Physicochemical and structural properties of the construct were satisfactory. Binding pockets were identified that could be the targets for designing effective inhibitors. Molecular docking revealed strong binding affinity of the construct with human Toll-like receptors 2 and 4. Normal mode analysis divulged stability of the docked complex. Codon optimization, in silico cloning and immune simulation analysis demonstrated suitability of the construct. These findings are likely to aid in vitro studies for developing vaccine against HRV-C.

Multi-perspectives and challenges in identifying B -cell epitopes.

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

Immunoinformatic Execution and Design of an Anti-Epstein-Barr Virus Vaccine with Multiple Epitopes Triggering Innate and Adaptive Immune Responses.

One of the most important breakthroughs in healthcare is the development of vaccines. The life cycle and its gene expression in the numerous virus-associated disorders must be considered when choosing the target vaccine antigen for Epstein-Barr virus (EBV). The vaccine candidate used in the current study will also be effective against all other herpesvirus strains, based on the conservancy study, which verified that the protein is present in all herpesviruses. From the screening, two B-cell epitopes, four MHC-I, and five MHC-II restricted epitopes were chosen for further study. The refined epitopes indicated 70.59% coverage of the population in Malaysia and 93.98% worldwide. After removing the one toxin (PADRE) from the original vaccine design, it was projected that the new vaccine would not be similar to the human host and would instead be antigenic, immunogenic, non-allergenic, and non-toxic. The vaccine construct was stable, thermostable, soluble, and hydrophilic. The immunological simulation projected that the vaccine candidate would be subject to a long-lasting active adaptive response and a short-lived active innate response. With IgM concentrations of up to 450 cells per mm3 and active B-cell concentrations of up to 400 cells per mm3, the B-cells remain active for a considerable time. The construct also discovered other conformational epitopes, improving its ability to stimulate an immune response. This suggests that, upon injection, the epitope will target the B-cell surface receptors and elicit a potent immune response. Furthermore, the discotope analysis confirmed that our conformational B-cell epitope was not displaced during the design. Lastly, the docking complex was stable and exhibited little deformability under heat pressure. These computational results are very encouraging for future testing of our proposed vaccine, which may potentially help in the management and prevention of EBV infections worldwide.

Designing a multi-epitope vaccine to control porcine epidemic diarrhea virus infection using immunoinformatics approaches.

Porcine epidemic diarrhea virus (PEDV), a continuously evolving pathogen, causes severe diarrhea in piglets with high mortality rates. However, current vaccines cannot provide complete protection against PEDV, so vaccine development is still necessary and urgent. Here, with the help of immunoinformatics approaches, we attempted to design a multi-epitope vaccine named rPMEV to prevent and control PEDV infection. The epitopes of rPMEV were constructed by 9 cytotoxic T lymphocyte epitopes (CTLs), 11 helper T lymphocyte epitopes (HTLs), 6 linear B cell epitopes (LBEs), and 4 conformational B cell epitopes (CBEs) based on the S proteins from the four representative PEDV G2 strains. To enhance immunogenicity, porcine β-defensin-2 (PBD-2) was adjoined to the N-terminal of the vaccine as an adjuvant. All of the epitopes and PBD-2 were joined by corresponding linkers and recombined into the multivalent vaccine, which is stable, antigenic, and non-allergenic. Furthermore, we adopted molecular docking and molecular dynamics simulation methods to analyze the interaction of rPMEV with the Toll-like receptor 4 (TLR4): a stable interaction between them created by 13 hydrogen bonds. In addition, the results of the immune simulation showed that rPMEV could stimulate both cellular and humoral immune responses. Finally, to raise the expression efficiency, the sequence of the vaccine protein was cloned into the pET28a (+) vector after the codon optimization. These studies indicate that the designed multi-epitope vaccine has a potential protective effect, providing a theoretical basis for further confirmation of its protective effect against PEDV infection in vitro and in vivo studies.

Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach

The merger of COVID-19 and seasonal influenza infections is considered a potentially serious threat to public health. These two viral agents can cause extensive and severe lower and upper respiratory tract infections with lung damage with host factors. Today, the development of vaccination has been shown to reduce the risk of hospitalization and mortality from the COVID-19 virus and influenza epidemics. Therefore, this study contributes to an immunoinformatics approach to producing a vaccine that can elicit strong and specific immune responses against COVID-19 and influenza A and B viruses. The NCBI, GISAID, and Uniprot databases were used to retrieve sequences. Linear B cell, Cytotoxic T lymphocyte, and Helper T lymphocyte epitopes were predicted using the online servers. Population coverage of MHC I epitopes worldwide for SARS-CoV-2, Influenza virus H3N2, H3N2, and Yamagata/Victoria were 99.93%, 68.67%, 68.38%, and 85.45%, respectively. Candidate epitopes were linked by GGGGS, GPGPG, and KK linkers. Different epitopes were permutated several times to form different peptides and then screened for antigenicity, allergenicity, and toxicity. The vaccine construct was analyzed for physicochemical properties, conformational B-cell epitopes, interaction with Toll-like receptors, and IFN-gamma-induced. Immune stimulation response of final construct was evaluated using C-IMMSIM. Eventually, the final construct sequence was codon-optimized for Escherichia coli K12 and Homo sapiens to design a multi-epitope vaccine and mRNA vaccine. In conclusion, due to the variable nature of SARS-CoV-2 and influenza proteins, the design of a multi-epitope vaccine can protect against all their standard variants, but laboratory validation is required. Communicated by Ramaswamy H. Sarma

Comparison of Anti-Trop2 Extracellular Domain Antibodies Generated Against Peptide and Protein Immunogens for Targeting Trop2-Positive Tumour Cells.

Trophoblast antigen 2 (Trop2) is a transmembrane glycoprotein upregulated in multiple solid tumours. Trop2-based passive immunotherapies are in clinical trials, while Trop2 targeting CAR-T cell-based therapies are also reported. Information about its T- and B-cell epitopes is needed for it to be pursued as an active immunotherapeutic target. This study focused on identification of immunodominant epitopes in the Trop2 extracellular domain (ECD) that can mount an efficient anti-Trop2 antibody response. In silico analysis using various B-cell epitope prediction tools was carried out to identify linear and conformational B-cell epitopes in the ECD of Trop2. Three linear peptide immunogens were shortlisted and synthesized. Along with linear peptides, truncated Trop2 ECD that possesses combination of linear and conformational epitopes was also selected. Recombinant protein immunogen was produced in 293-F suspension culture system and affinity purified. Antisera against different immunogens were characterized by ELISA and Western blotting. Two anti-peptide antisera detected recombinant and ectopically expressed Trop2 protein; however, they were unable to recognize the endogenous Trop2 protein expressed by cancer cells. Antibodies against truncated Trop2 ECD could bind to the endogenous Trop2 expressed on the surface of cancer cells. In addition to their high avidity, these polyclonal anti-sera against truncated Trop2 protein also mediated antibody-dependent cell-mediated cytotoxicity (ADCC). In summary, our comparative analysis demonstrated the utility of truncated Trop2 ECD as a promising candidate to be pursued as an active immunotherapeutic molecule against Trop2-positive cancer cells.

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

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

Defeating a superbug: A breakthrough in vaccine design against multidrug-resistant Pseudomonas aeruginosa using reverse vaccinology.

Multidrug-resistant Pseudomonas aeruginosa has become a major cause of severe infections. Due to the lack of approved vaccines, this study has presented putative vaccine candidates against it. P. aeruginosa 24Pae112 as a reference strain was retrieved from GenBank database. The surface-exposed, antigenic, non-allergenic, and non-homologous human proteins were selected. The conserved domains of selected proteins were evaluated, and the prevalence of proteins was assessed among 395 genomes. Next, linear and conformational B-cell epitopes, and human MHC II binding sites were determined. Finally, five conserved and highly antigenic B-cell epitopes from OMPs were implanted on the three platforms as multi-epitope vaccines, including FliC, the bacteriophage T7 tail, and the cell wall-associated transporter proteins. The immunoreactivity was investigated using molecular docking and immune simulation. Furthermore, molecular dynamics simulation was done to refine the chimeric cell-wall-associated transporter-TLR4 complex as the best interaction. Among 6494 total proteins of P. aeruginosa 24Pae112, 16 proteins (seven OMPs and nine secreted) were ideal according to the defined criteria. These proteins had a molecular weight of 110 kDa and were prevalent in ≥ 75% of P. aeruginosa genomes. Among the presented multi-epitope vaccines, the chimeric cell-wall-associated transporter had the strongest interaction with TLR4. Moreover, the immune simulation response revealed that the bacteriophage T7 tail chimeric protein had the strongest ability to stimulate the immune system. In addition, molecular docking and molecular dynamic simulation indicated the proper and stable interactions between the chimeric cell-wall-associated transporter and TLR4. This study proposed 16 shortlisted proteins as promising immunogenic targets. Two novel platforms (e.g. cell-wall-associated transporter and bacteriophage T7 tail proteins) for designing of multi-epitope vaccines (MEVs), showed the better performance compared to FliC. In our future studies, these two MEVs will receive more scrutiny to evaluate their immunoreactivity.

Development of a candidate multi-epitope vaccine against Sphingobacterium spiritivorum : Reverse vaccinology and immunoinformatics approach.

To develop a candidate vaccine aginst the Sphingobacterium spiritivorum. Since there is currently no vaccine against this pathogen, we employed in-silico methods to extensively explore the outer membrane toxin-producing proteins found specifically in S. spiritivorum to forecast a multi-epitope chimeric vaccine design. This computational study was conducted in Saudi Arabia in 2022 (study design: computational; ethical approval not applicable). TThe vaccine peptide comprises multiple linear and conformational B-cell epitopes, which have the potential to elicit humoral immunity. Projected B-cell- derived T-cell epitopes for outer membrane proteins are present in the produced protein. The docking and molecular dynamic simulation results indicating that the chimeric vaccine had adequate binding stability with TLR-4. Following the immunological simulation, significant levels of immune cell expression were observed as immunoglobulin (Ig) M and IgG, IgM, IgM1, and IgM2, and independently IgG1 and IgG2. The developed vaccine candidate is suitable for further testing and can assist experimental vaccinologists in developing an effective vaccine against S. spiritivorum.

Reducing the Immunogenicity of Pulchellin A-Chain, Ribosome-Inactivating Protein Type 2, by Computational Protein Engineering for Potential New Immunotoxins

Pulchellin is a plant biotoxin categorized as a type 2 ribosome-inactivating protein (RIPs) which potentially kills cells at very low concentrations. Biotoxins serve as targeting immunotoxins (IT), consisting of antibodies conjugated to toxins. ITs have two independent protein components, a human antibody and a toxin with a bacterial or plant source; therefore, they pose unique setbacks in immunogenicity. To overcome this issue, the engineering of epitopes is one of the beneficial methods to elicit an immunological response. Here, we predicted the tertiary structure of the pulchellin A-chain (PAC) using five common powerful servers and adopted the best model after refining. Then, predicted structure using four distinct computational approaches identified conformational B-cell epitopes. This approach identified some amino acids as a potential for lowering immunogenicity by point mutation. All mutations were then applied to generate a model of pulchellin containing all mutations (so-called PAM). Mutants’ immunogenicity was assessed and compared to the wild type as well as other mutant characteristics, including stability and compactness, were computationally examined in addition to immunogenicity. The findings revealed a reduction in immunogenicity in all mutants and significantly in N146V and R149A. Furthermore, all mutants demonstrated remarkable stability and validity in Molecular Dynamic (MD) simulations. During docking and simulations, the most homologous toxin to pulchellin, Abrin-A was applied as a control. In addition, the toxin candidate containing all mutations (PAM) disclosed a high level of stability, making it a potential model for experimental deployment. In conclusion, by eliminating B-cell epitopes, our computational approach provides a potential less immunogenic IT based on PAC.

Prediction of Conformational and Linear B-Cell Epitopes on Envelop Protein of Zika Virus Using Immunoinformatics Approach.

The current spread of Zika virus infection in India has become a public health issue due to the virus's possible link to birth abnormalities and neurological disorders. There is a need for enhanced vaccines or drugs as a result of its epidemic outbreak and the lack of potential medication. B-cell mediated adaptive immunity is capable of developing pathogen-specific memory that confers immunological protection. Therefore, in this study, the envelope protein of the Zika virus was retrieved from the NCBI protein database. The ABCpred and BepiPred software were used to discover linear B-cell epitopes on envelope protein. Conformational B-cell epitopes on envelope protein were identified using SEPPA 3.0 and Ellipro tools. Predicted B-cell epitopes were evaluated for allergenicity, toxicity, and antigenicity. Two consensus linear B-cell epitopes, envelope165-180 (AKVEITPNSPRAEATL) and envelope224-238 (PWHAGADTGTPHWNN) were identified using ABCpred and BepiPredtools. SEPPA 3.0 and Elliprotools predicted consensus conformational envelope98-110 (DRGWGNGCGLFGK) and envelope248-251 (AHAK) epitopes and one residue (75PRO) within envelope protein as a component of B-cell epitopes. These predicted linear and conformational B-cell epitopes will help in designing peptide vaccines that will activate the humoral response. However, in-vitro and in-vivo laboratory experimental confirmations are still needed to prove the application's feasibility.

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.

Identification of Novel Nontoxic Mutants of Diphtheria Toxin Unable to ADP-ribosylate EF2 Using Molecular Dynamics Simulations and Free Energy Calculations

Introduction: The Diphtheria vaccine is currently manufactured by chemical inactivation of Diphtheria toxin (DT) following large-scale cultivation of the pathogenic Corynebacterium diphtheriae. The development of non-toxic variants of DT in non-pathogenic recombinant hosts improves the process safety aspects considerably and is therefore desirable. The cytotoxicity of DT was exerted through ADPribosylation of elongation factor 2 (EF2) by the catalytic (C) domain of DT using NAD as the ADP donor. Aim: We, therefore, aim to design DT variants unable to perform ADP-ribosylation of EF2. Methods: We used molecular dynamics simulations (MDS) for docking of the C-domain of DT onto EF2 to identify interacting amino acid fluctuations using the root mean square fluctuation (RMSF). Amino acids of the C domain possessing low fluctuations and hence low flexibility were then specified and used for the mutant design. The C domain and the mutants were docked with NAD and ADENYLYL-3'-5'- PHOSPHO-URIDINE3'-MONOPHOSPHATE (APU) as a C domain inhibitor. G52E showing negligible NAD binding was also included in our study. Free binding energies of the complexes were calculated and used to select the desired mutants. Moreover, contact maps of the C domain and the selected mutants were compared to elucidate their structural differences. The SCRATCH tool was used to estimate their solubility upon recombinant expression in E. coli. ElliPro was further used to determine their B-cell epitopes. Results: Our results indicated that amino acids Y20, V80, V81, V83, and Y149 within the C domain showed low flexibility. The constructed mutants, including Y20E and Y80E were able to bind APU, whereas Y149E was not able to bind it. Both Y20E and Y149E were unable to bind NAD. Thus, these mutants did not have ADP-ribosylation activity and were nontoxic. These were structurally different from the C domain. However, their solubility in E. coli and their conformational B-cell epitopes were similar to those of the C domain. Conclusion: Therefore, Y20E and Y149E are applicable for the vaccine design with DT using a much safer process compared to the commercial process.

Design of a polytopic construct of LACK, TSA and GP63 proteins for the diagnosis of cutaneous leishmaniasis: An in silico strategy

• The synthesized multi-epitope was successfully expressed into Escherichia coli. • rGLT increased specificity of antigen detection tests by identifying B-cell epitopes. • Our novel multi-epitope antigens represent a potential target for CL diagnostic. Cutaneous leishmaniasis (CL), which is caused by different species of the genus Leishmania, including L. major , is a significant parasitic disease worldwide. Commercial leishmaniasis diagnostic kits frequently target the entire parasite antigen, although this approach can reduce the specificity of the tests. The present study sought to increase the specificity of antigen detection tests by identifying the B-cell epitopes on the surface of the protozoa and developing an antigenic multiepitope construct to react with the system of B-cell epitopes unique to L. major . More specifically, the linear and conformational B-cell epitopes for three L. major proteins, namely, GP63, LACK, and TSA, were predicted using the ABCprd, IEDB, LBtope, CBTOPE, and DiscoTope servers. The three-dimensional structures of these proteins, as well as of the complete multiepitope, were predicted using the I-TASSER server. The multiepitope structure models were validated by means of Ramachandran plots and the Prosa and Verify3D servers. The multiepitope construct was evaluated using the ProtParam and SOLpro servers. The synthesized multiepitope was then successfully expressed into a pET28a plasmid. The expression was confirmed using SDS-PAGE and dot blot techniques. Moreover, the expressed protein was evaluated by means of Western blotting. A band of approximately 39 kDa was observed by SDS-PAGE. The rGLT-pET-28a showed a high response with the 1:1000 dilution of the anti-His-tag monoclonal antibody by Western blot. The results of this study indicated that the integrated GP63, LACK, and TSA multiepitope antigens of L. major represent a potential target for a novel diagnostic kit for CL.