Discontinuous B-cell Epitopes Research Articles

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

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

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

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

In Silico Prediction of Plasmodium falciparum PfRipr Epitopes as Vaccine Candidates

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

Probing the proteome of mpox virus for in silico design of a multiepitope vaccine

Aim: This work aims to contribute toward development of preventive measures for the control of monkeypox (mpox) virus disease through computational design of a multiepitope vaccine. Methods: To accomplish this, we employed a robust immunoinformatics approach to design a putative chimeric vaccine candidate from 18 viral transmembrane proteins. Results: The resulting chimeric vaccine candidate is a 76.4 kDa protein containing 687 amino acids with an estimated isoelectric point of 9.39. In addition, it was predicted to adopt a stable 3D conformation that harbors discontinuous B-cell epitopes and strongly interacts with key immune receptors. Conclusion: The designed hypothetical antigen is a valuable addition to the collection of prospective vaccine candidates for future development and trials against the re-emerging mpox disease.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections.

COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

An in silico approach for prediction of B cell and T cell epitope candidates against Chikungunya virus

Several outbreaks of Chikungunya virus (CHIKV) had been reported since 1952 when mankind had his first encounter against the virus in Tanzania. Although these reports designate the CHIKV to be rarely fatal, cases of outbreaks in the last decade accompanied by severe complications and death poses a challenge to the development of effective treatment methods. Several attempts to vaccine development against CHIKV still remains unsuccessful. In this study, we aimed at the prediction of B-cell and T cell epitopes against CHIKV by using immunoinformatics. This, in turn, can contribute to development of an epitope based vaccine against CHIKV. Both linear and discontinuous B-cell epitopes, as well as Cytotoxic T-lymphocyte epitopes, were predicted for the CHIKV Envelope (E1 and E2) glycoproteins and (NS2). The antigenic CTL epitopes with highest binding affinities with type-1 MHC were selected and the peptides were docked to them. Docking followed by molecular dynamics simulations were performed to assess the stability of the docked complexes.

In silico design of a polypeptide as a vaccine candidate against ascariasis

Ascariasis is the most prevalent zoonotic helminthic disease worldwide, and is responsible for nutritional deficiencies, particularly hindering the physical and neurological development of children. The appearance of anthelmintic resistance in Ascaris is a risk for the target of eliminating ascariasis as a public health problem by 2030 set by the World Health Organisation. The development of a vaccine could be key to achieving this target. Here we have applied an in silico approach to design a multi-epitope polypeptide that contains T-cell and B-cell epitopes of reported novel potential vaccination targets, alongside epitopes from established vaccination candidates. An artificial toll-like receptor-4 (TLR4) adjuvant (RS09) was added to improve immunogenicity. The constructed peptide was found to be non-allergic, non-toxic, with adequate antigenic and physicochemical characteristics, such as solubility and potential expression in Escherichia coli. A tertiary structure of the polypeptide was used to predict the presence of discontinuous B-cell epitopes and to confirm the molecular binding stability with TLR2 and TLR4 molecules. Immune simulations predicted an increase in B-cell and T-cell immune response after injection. This polypeptide can now be validated experimentally and compared to other vaccine candidates to assess its possible impact in human health.

Immunoinformatic-Based Multi-Epitope Vaccine Design for Co-Infection of Mycobacterium tuberculosis and SARS-CoV-2.

(1) Background: Many co-infections of Mycobacterium tuberculosis (MTB) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have emerged since the occurrence of the SARS-CoV-2 pandemic. This study aims to design an effective preventive multi-epitope vaccine against the co-infection of MTB and SARS-CoV-2. (2) Methods: The three selected proteins (spike protein, diacylglycerol acyltransferase, and low molecular weight T-cell antigen TB8.4) were predicted using bioinformatics, and 16 epitopes with the highest ranks (10 helper T lymphocyte epitopes, 2 CD8+ T lymphocytes epitopes, and 4 B-cell epitopes) were selected and assembled into the candidate vaccine referred to as S7D5L4. The toxicity, sensitization, stability, solubility, antigenicity, and immunogenicity of the S7D5L4 vaccine were evaluated using bioinformatics tools. Subsequently, toll-like receptor 4 docking simulation and discontinuous B-cell epitope prediction were performed. Immune simulation and codon optimization were carried out using immunoinformatics and molecular biology tools. (3) Results: The S7D5L4 vaccine showed good physical properties, such as solubility, stability, non-sensitization, and non-toxicity. This vaccine had excellent antigenicity and immunogenicity and could successfully simulate immune responses in silico. Furthermore, the normal mode analysis of the S7D5L4 vaccine and toll-like receptor 4 docking simulation demonstrated that the vaccine had docking potential and a stable reaction. (4) Conclusions: The S7D5L4 vaccine designed to fight against the co-infection of MTB and SARS-CoV-2 may be safe and effective. The protective efficacy of this promising vaccine should be further verified using in vitro and in vivo experiments.

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications.

Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.

Molecular characterization and genetic diversity of Babesia bovis and Babesia bigemina of cattle in Thailand.

Babesia bovis and B. bigemina are the most common tick-borne parasites that cause bovine babesiosis which effects livestock production, leading to economic losses in tropical and subtropical areas of the world. The aims of this study were to determine the molecular detection, genetic diversity and antigenicity prediction of B. bovis based on spherical body protein 2 (sbp-2) gene and B. bigemina based on rhoptry-associated protein 1a (rap-1a) gene in cattle in Thailand. By PCR assay, the molecular detection of B. bovis and B. bigemina infection revealed levels of 2.58% (4/155) and 5.80% (9/155), respectively. The phylograms showed that B. bovis sbp-2 and B. bigemina rap-1a sequences displayed 5 and 3 clades with similarity ranging between 85.53 to 100% and 98.28 to 100%, respectively, when compared within Thailand strain. Diversity analysis of sbp-2 and rap-1a sequences showed 18 and 4 haplotypes, respectively. The entropy analysis illustrated 104 and 7 polymorphic sites of sbp-2 and rap-1a nucleic acid sequences, respectively, while those of sbp-2 and rap-1a amino acid sequences showed 46 and 4 high entropy peaks, respectively. Motifs analysis exhibited the distribution and conservation among sbp-2 and rap-1a sequences. The continuous and discontinuous B-cell epitopes have also been evaluated in this work. Therefore, our findings may be used to ameliorate the understanding inputs of molecular phylogeny, genetic diversity and antigenicity of B. bovis and B. bigemina Thailand stains.

Screening of B-cell epitopes of Der-p1 and Der-p2 major aeroallergens by computational approach for designing immunotherapeutics

Introduction and Aim: Allergic diseases are IgE-mediated hypersensitivity reactions affecting approximately 30% of the general population globally. Dermatophagoides pteronyssinus (Der-p) is the most prevalent house dust mite (HDM) species consisting of 23 mite allergen groups. Among these, group 1 and 2 are major allergenic proteins, which causes allergic asthma in 80% of sensitized individuals, with elevated IgE titres in the serum. This study involves in silico analysis of potential B-cell epitopes of group 1 and group 2 of Der-p, which can be utilized in designing immunotherapeutic vaccines. Materials and Methods: Allergen sequences obtained from the database- International Union of Immunological Societies (IUIS), for predicting of B-cell epitopes. The physiochemical properties and secondary structures of the obtained sequence were evaluated. The sequences were further subjected to determining antigenicity, surface accessibility, and prediction of linear and discontinuous B-cell epitope by utilizing IEDB tools. Results: The linear and discontinuous B-cell epitopes of Der-p1 and Der-p2 aeroallergen were predicted. Further, Der-p1 and Der-p2 showed 6 linear epitopes each respectively. Conformational epitopes predicted were 123 of Der-p1 and 72 of Der-p2 respectively, by the ElliPro tool. Based on the structure, antigenicity, and surface accessibility, only 10% of Der-p1 and Der-p2 which binds to B-cell epitopes are linear and the majority are discontinuous. Conclusion: The linear and conformational epitopes of Der-p1 and Der-p2 are predicted using in silico tools. These identified epitopes might be useful for developing epitope-based immunotherapeutics for HDM allergy.

B-Cell Epitope Predictions Using Computational Methods.

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

Prediction of promiscuous epitopes in ORF2 of Hepatitis E virus: an In-Silico approach.

Vaccine development against emerging infections is essentially important for saving people from increasing viral infections. In developing countries, Hepatitis E (HEV) is a common infection affecting millions of people worldwide. Based on In-silico analysis, different approaches have been targeted. Rationale of this study is to design an epitope-based vaccine candidates with the help of immunoinformatics that can predict promiscuous B-cell and T-cell epitopes of the most antigenic HEV-ORF2 capsid protein. This study suggests potential T-cell and B-cell epitopes of the highly antigenic HEV ORF2 capsid protein while using various In-silico tools such as NCBI-BLAST, Expassy, CLC workbench, Ellipro and Discotope. Potential antigenic and immunogenic CD8+ T-cell epitopes were predicted from the global consensus sequence of ORF2-HEV. Furthermore, twenty-two linear B-cell epitopes were predicted. Among these, "SLGAGPV" at position 587-593 and "LEFRNLTPGNTNTRVSRYSS" at position 306-325 were most antigenic with antigenicity score 1.4206 and 1.3600 respectively. Discontinuous B-cell epitopes were found by three-dimensional capsid protein structure. Epitopes predicted in this study reveal high antigenicity and promiscuity for HLA classes. Collectively, our data suggests promiscuous epitopes that can potentially acts as new candidates for the design of HEV peptide vaccine.

Computational analysis of immunogenic epitopes in the p30 and p54 proteins of African swine fever virus

African swine fever (ASF) is a highly infectious viral disease of pigs, which causes acute fatal haemorrhage and is a severe concern to the global pork industry. The present study followed computational approaches to identify B- and T-cell epitopes for the p30 and p54 proteins of the African swine fever virus (ASFV) by interacting with the swine leukocyte antigen (SLA) alleles. The amino acid sequences of p30 and p54 were analysed for variability and relative solvent accessibility, and their three-dimensional structures were predicted and validated. Molecular dynamics simulation was performed to study the structural and dynamic properties of the protein. Six and five linear B-cell epitopes have been predicted for p30 and p54, respectively. Four and three discontinuous B-cell epitopes have been predicted for p30 and p54, respectively. Further, the top five T-cell epitopes for SLA-1, 2, and 3 have been listed for both proteins. These results can help us to understand the immunodominant regions in the p30 and p54 proteins of ASFV and potentially assist in designing peptide-based diagnostics and vaccines. Also, the identified T-cell epitopes may be considered for peptide-based vaccine design against ASFV. Communicated by Ramaswamy H. Sarma

Identification and Characterization of Novel Mutants of Nsp13 Protein among Indian SARS-CoV-2 Isolates

Background: SARS-CoV-2, the causative agent of COVID-19, has mutated rapidly, enabling it to adapt and evade the immune system of the host. Emerging SARS-CoV-2 variants with crucial mutations pose a global challenge in the context of therapeutic drugs and vaccines developing globally. There are currently no specific therapeutics or vaccines available to combat SARS-CoV-2 devastation. Concerning this, the current study aimed to identify and characterize the mutations found in the Nsp13 of SARS-CoV-2 in Indian isolates. Methods: In the present study, the Clustal omega tool was used for mutational analysis. The impact of mutations on protein stability, flexibility, and function was predicted using the DynaMut and PROVEAN tools. Furthermore, B-cell epitopes contributed by Nsp13 were identified using various predictive immunoinformatic tools. Results: Non-structural protein Nsp13 sequences from Indian isolates were analyzed by comparing them with the firstly reported Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) protein sequence in Wuhan, China. Out of 825 Nsp13 protein sequences, a total of 38 mutations were observed among Indian isolates. Our data showed that mutations in Nsp13 at various positions (H164Y, A237T, T214I, C309Y, S236I, P419S, V305E, G54S, H290Y, P53S, A308Y, and A308Y) have a significant impact on the protein's stability and flexibility. Moreover, the impact of Nsp13 mutations on protein function was predicted based on the PROVEAN score that indicated 15 mutants as neutral and 23 mutants as deleterious effects. Immunological parameters of Nsp13, such as antigenicity, allergenicity, and toxicity, were evaluated to predict the potential B-cell epitopes. The predicted peptide sequences were correlated with the observed mutants. Our predicted data showed that there are seven high-rank linear epitopes as well as 18 discontinuous B-cell epitopes based on immunoinformatic tools. Moreover, it was observed that out of the total 38 identified mutations among Indian SARS-CoV-2 Nsp13 protein, four mutant residues at positions 142 (E142), 245 (H245), 247 (V247), and 419 (P419) were localised in the predicted B cell epitopic region. Conclusion: Altogether, the results of the present in silico study might help to understand the impact of the identified mutations in Nsp13 protein on its stability, flexibility, and function.

Germin-like Protein 9-3, a Potential Allergen from a Rice Variety derived from Radiation-Mutation Breeding: An In Silico Study

New varieties of crops with a history of safe and widespread use derived from radiation-mutation breeding are generally regarded to be unlikely to produce food safety concerns compared with genetically modified (GM) crops. However, more recent molecular analysis revealed that there might be a greater likelihood that the former may bring about more unintended effects than genetic modification by foreign gene introduction. Thus, in this paper, an allergenicity prediction analysis was carried out on the microarray data of a stable rice mutant derived from γ irradiation-mutation breeding. For the first time, it was revealed that germin-like protein (GLP) 9-3, the transcript with the most significant fold increase in expression in a transcriptome data analyzed, may be a potential allergen in radiation-mutant rice. An immunoinformatic analysis of GLP 9-3 disclosed several linear and conformational (discontinuous) B-cell epitopes and MHCII-binders. These epitopes were located primarily on the random coil and surface-exposed regions based on inspection of the protein’s secondary structures. Additionally, the GLP 9-3 epitopes were predicted to be reactive in more than 50% of the population in top rice-producing and -consuming countries based on human leukocyte antigen (HLA) haplotypes. Since this is the first report on the up-regulation of GLP 9-3 allergen in mutant rice, it is recommended to investigate this molecule for further biochemical and clinical analyses.

Predicted 3D model of the M protein of Porcine Epidemic Diarrhea Virus and analysis of its immunogenic potential.

The membrane protein M of the Porcine Epidemic Diarrhea Virus (PEDV) is the most abundant component of the viral envelope. The M protein plays a central role in the morphogenesis and assembly of the virus through protein interactions of the M-M, M-Spike (S) and M-nucleocapsid (N) type. The M protein is known to induce protective antibodies in pigs and to participate in the antagonistic response of the cellular antiviral system coordinated by the type I and type III interferon pathways. The 3D structure of the PEDV M protein is still unknown. The present work exposes a predicted 3D model of the M protein generated using the Robetta protocol. The M protein model is organized into a transmembrane and a globular region. The obtained 3D model of the PEDV M protein was compared with 3D models of the SARS-CoV-2 M protein created using neural networks and with initial machine learning-based models created using trRosetta. The 3D model of the present study predicted four linear B-cell epitopes (RSVNASSGTG and KHGDYSAVSNPSALT peptides are noteworthy), six discontinuous B-cell epitopes, forty weak binding and fourteen strong binding T-cell epitopes in the CV777 M protein. A high degree of conservation of the epitopes predicted in the PEDV M protein was observed among different PEDV strains isolated in different countries. The data suggest that the M protein could be a potential candidate for the development of new treatments or strategies that activate protective cellular mechanisms against viral diseases.

Bioinformatic Techniques for Vaccine Development: Epitope Prediction and Structural Vaccinology.

Structural vaccinology involves characterizing the interactions between an antigen and antibodies or host immune receptors. Central to this is the task of epitope prediction, which involves describing the binding affinity and interactions of a given peptide typically to the major histocompatibility complex in the case of T-cells or to the antibodies in the case of B-cells. Several computational models exist for this purpose which we will review here. Generally, epitope predictions for MHC-I and MHC-II are substantially different tasks as well as epitope prediction for continuous versus discontinuous B-cell epitopes. Overall, these models suffer from overprediction of epitopes although general themes support both the use of neural networks as well as the incorporation of more abundant and more varied experimental annotation into model training as valuable in improving predictive performance.

Construction of a multiepitope vaccine candidate against Fasciola hepatica: an in silico design using various immunogenic excretory/secretory antigens

ABSTRACT Background Fasciola hepatica is an important pathogen that causes liver fluke disease in definitive hosts such as livestock animals and humans. Various excretory/secretory products have been used in serological diagnosis and vaccination studies targeting fasciolosis. There are no commercial vaccines against fasciolosis yet. Bioinformatic analysis based on computational methods have lower cost and provide faster output compared to conventional vaccine antigen discovery techniques. The aim of this study was to predict B- and T-cell specific epitopes of four excretory/secretory antigens (Kunitz-type serine protease inhibitor, cathepsin L1, helminth defense molecule, and glutathione S-transferase) of Fasciola hepatica and to construct a multiepitope vaccine candidate against fasciolosis. Methods and Results Initially, nonallergic and the highest antigenic B- and T- cell epitopes were selected and then, physico-chemical parameters, secondary and tertiary structures of designed multiepitope vaccine candidate were predicted. Tertiary structure was refined and validated using online bioinformatic tools. Linear and discontinuous B-cell epitopes and disulfide bonds were determined. Finally, molecular docking analysis for MHC-I and MHC-II receptors was performed. Conclusion This multi-epitope vaccine candidate antigen, with high immunological properties, can be considered as a promising vaccine candidate for animal experiments and wet lab studies.

Combination of human papillomaviruses L1 and L2 multiepitope constructs protects mice against tumor cells

Different types of cancer including cervical (>90%), anal (~88%), vaginal (~40%), and penile (~40%) cancers are associated with human papillomaviruse (HPV) infections. Three prophylactic vaccines (Cervarix, Gardasil, and Gardasil-9) were approved to provide immuno-protection against certain types of HPVs. Currently, next-generation HPV vaccines such as L1/L2-based vaccines are being developed to provide broad-type HPV protection. In this study, we introduced a comprehensive framework for design of L1/L2 polyepitope-based HPV vaccine candidate. This framework started with protein sequence retrieval and followed by conservancy analysis between high-risk HPVs, MHC-I and MHC-II epitope mapping, and B-cell and T-cell epitope mapping. Subsequently, we performed Tap transport and proteasomal cleavage, population coverage, antigenicity, allergenicity and cross-reactivity. After that, peptide-MHCI/II flexible docking and comprehensive conservancy analysis against all HPV types were carried out. The next steps were prediction of interferon-gamma and interleukin-10 inducing epitopes, epitope selection and construct design, tertiary structure prediction, refinement and validation, discontinuous B-cell epitope prediction, vaccine-TLR4 molecular docking, and codon optimization. Our data showed that two designed vaccine constructs harboring 8 L1 peptides or 7 L2 peptides, individually were highly conserved between all well-known HPV types. In addition, the combination of in silico/in vivo approaches indicated the potential ability of L1 and L2 polyepitope constructs for development of next generation prophylactic/therapeutic HPV vaccine.