Linear B-cell Epitope Prediction Research Articles

Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings

Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT.

The rise of taxon-specific epitope predictors.

Computational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment, we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.

In-silico identification of linear B-cell epitopes in specific proteins of Bartonella bacilliformis for the serological diagnosis of Carrion's disease.

Carrion´s disease is caused by Bartonella bacilliformis, it is a Gram-negative pleomorphic bacterium. B. bacilliformis is transmitted by Lutzomyia verrucarum in endemic areas of the Peruvian Inter-Andean valleys. Additionally, the pathogenicity of B. bacilliformis involves an initial infection of erythrocytes and the further infection of endothelial cells, which mainly affects children and expectant women from extreme poverty rural areas. Therefore, the implementation of serological diagnostic methods and the development of candidate vaccines for the control of CD could be facilitated by the prediction of linear b-cell epitopes in specific proteins of B. bacilliformis by bioinformatics analysis. In this study, We used an in-silico analysis employing six web servers for the identification of epitopes in proteins of B. bacilliformis. The selection of B. bacilliformis-specific proteins and their analysis to identify epitopes allowed the selection of seven protein candidates that are expected to have high antigenic activity.

Cloning and Molecular Characterization of the Recombinant CVB4E2 Immunogenic Viral Protein (rVP1), as a Potential Subunit Protein for Vaccine and Immunodiagnostic Reagent Candidate.

The aim of the present study was, first, to clone the VP1 gene of the human coxsackievirus B4 strain E2 (CVB4E2) in the prokaryotic pUC19 plasmid expression vector then to compare it with the structural capsid proteins of the same strain using bioinformatic tools. PCR colony amplification followed through a restriction digestion analysis and sequencing process which affirmed the success of the cloning process. SDS-PAGE and Western Blotting were used to characterize the purified recombinant viral protein expressed in bacteria cells. The BLASTN tool revealed that the nucleotide sequence of the recombinant VP1 (rVP1) expressed by pUC19 highly matched the target nucleotide sequence of the diabetogenic CVB4E2 strain. Secondary structure and three-dimension structure prediction suggested that rVP1, such as wild-type VP1, is chiefly composed of random coils and a high percentage of exposed amino acids. Linear B-cell epitope prediction showed that several antigenic epitopes are likely present in rVP1 and CVB4E2 VP1 capsid protein. Additionally, phosphorylation site prediction revealed that both proteins may affect the signal transduction of host cells and can be involved in virus virulence. The present work highlights the usefulness of cloning and bioinformatics characterizations for gene investigation. Furthermore, the collected data are helpful for future experimental research related to the development of immunodiagnostic reagents and subunit vaccines based on the expression of immunogenic viral capsid proteins.

B-Cell Epitope Mapping of the Plasmodium falciparum Malaria Vaccine Candidate GMZ2.6c in a Naturally Exposed Population of the Brazilian Amazon.

The GMZ2.6c malaria vaccine candidate is a multi-stage P. falciparum chimeric protein that contains a fragment of the sexual-stage Pfs48/45-6C protein genetically fused to GMZ2, an asexual-stage vaccine construction consisting of the N-terminal region of the glutamate-rich protein (GLURP) and the C-terminal region of the merozoite surface protein-3 (MSP-3). Previous studies showed that GMZ2.6c is widely recognized by antibodies from Brazilian exposed individuals and that its components are immunogenic in natural infection by P. falciparum. In addition, anti-GMZ2.6c antibodies increase with exposure to infection and may contribute to parasite immunity. Therefore, identifying epitopes of proteins recognized by antibodies may be an important tool for understanding protective immunity. Herein, we identify and validate the B-cell epitopes of GMZ2.6c as immunogenic and immunodominant in individuals exposed to malaria living in endemic areas of the Brazilian Amazon. Specific IgG antibodies and subclasses against MSP-3, GLURP, and Pfs48/45 epitopes were detected by ELISA using synthetic peptides corresponding to B-cell epitopes previously described for MSP-3 and GLURP or identified by BepiPred for Pfs48/45. The results showed that the immunodominant epitopes were P11 from GLURP and MSP-3c and DG210 from MSP-3. The IgG1 and IgG3 subclasses were preferentially induced against these epitopes, supporting previous studies that these proteins are targets for cytophilic antibodies, important for the acquisition of protective immunity. Most individuals presented detectable IgG antibodies against Pfs48/45a and/or Pfs48/45b, validating the prediction of linear B-cell epitopes. The higher frequency and antibody levels against different epitopes from GLURP, MSP-3, and Pfs48/45 provide additional information that may suggest the relevance of GMZ2.6c as a multi-stage malaria vaccine candidate.

Comparative Molecular Analysis and Antigenicity Prediction of an Outer Membrane Protein (ompC) of Non-typhoidal Salmonella Serovars Isolated from Different Food Animals in Lagos, Nigeria.

Non-typhoidal Salmonella (NTS) infections occur globally with high morbidity and mortality. The public health challenge caused is exacerbated by increasing rate of antibiotic resistance and absence of NTS vaccine. In this study, we characterized the outer membrane protein C (OmpC) serovars isolated from different food animals and predicted antigenicity. ompC of 27 NTS serovars were amplified by polymerase chain reaction (PCR) and sequenced. Sequence data were analysed and B-cell epitope prediction was done by BepiPred tool. T-cell epitope prediction was done by determining peptide-binding affinities of major histocompatibility complex (MHC) classes I and II using NetMHC pan 2.8 and NetMHC-II pan 3.2, respectively. ompC sequence analysis revealed conserved region among ompCs of Salmonella Serovars. A total of 66.7% of ompCs were stable with instability index value < 40 and molecular weight that ranged from 27 745.47 to 32 714.32 kDa. All ompCs were thermostable and hydrophilic with the exception of S. Pomona (14p) isolate that had ompC with GRAVY value of 0.028 making it hydrophobic. Linear B-cell epitope prediction revealed ability of ompC to elicit humoral immunity. Multiple B-cell epitopes that were exposed and buried were observed on several positions on the ompC sequences. T-cell epitope prediction revealed epitopes with strong binding affinity to MHC-I and -II. Strong binding to human leukocyte antigen (HLA-A) ligands, including HLA-A03:1, HLA-A24:02 and HLA-A26:01 in the case of MHC-I were observed. While binding affinity to H-2 IAs, H-2 IAq and H-2 IAu (H-2 mouse molecules) were strongest in the case of MHC-II. ompCs of NTS serovars isolated from different food animal sources indicated ability to elicit humoral and cell-mediated immunity. Hence, ompCs of NTS serovars are potential candidate for production of NTS vaccines.

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/ .

NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes

Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.

Construction of Multi-Epitopes Vaccine Candidate against SARS-CoV-2 D614G Variant

COVID-19 caused by the SARS-CoV-2 virus has become a real threat due to the emergence of new variants which are more deadly with higher infectivity. Vaccine constructs that target specific SARS-CoV-2 variants are needed for stemming COVID-19 fatality. The spike (S) glycoprotein is the major antigenic component that triggers the host immune response. Reverse vaccinology strategy was applied to the S protein of COVID-19 variant D614G to identify highly ranked antigenic proteins. In this study, a multi-epitope synthetic gene was designed using computational strategies for the COVID-19 D614G variant. The SARS-CoV-2 D614G variant protein sequence was retrieved from the NCBI database. The prediction of linear B-cell epitopes was carried out using Artificial Neural Network (ANN)-based ABCpred and BepiPred 2.0 software. The top 15 highly antigenic epitopes sequences were then selected. Propred 1 and Propred servers were used to identify major histocompatibility complex (MHC) class I and class II binding epitopes within pre-determined B-cell epitopes to predict T-cell epitopes. The top 5 MHC class I and class II were selected. Further in-silico testing for its solubility, allergenicity, antigenicity, and other physiochemical properties was analyzed using Bpred. The constructed gene was subjected to assembly PCR and the gene product was confirmed by Sanger sequencing. The findings from this study suggested that a highly antigenic specific region of the SARS-CoV-2 D614G variant can be predicted in-silico and amplified using the assembly PCR method. The designed synthetic gene was shown to elicit specific humoral and cell-mediated immune responses towards the SARS-CoV-2 variants.

In silico analysis of the conserved surface-exposed epitopes to design novel multiepitope peptide vaccine for all variants of the SARS-CoV-2

Recently the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pervasive threat to generic health. The SARS-CoV-2 spike (S) glycoprotein plays a fundamental role in binds and fusion to the angiotensin-converting enzyme 2 (ACE2). The multi-epitope peptide vaccines would be able to elicit both long-lasting humoral and cellular immune responses, resulting the eliminating SARS-CoV-2 infections as asymptomatic patients are in large numbers. Recently, the omicron variant of the SARS-CoV-2 became a variant of concern that contained just 15-point mutations in the receptor-binding domain of the spike protein. In order to eliminate new evidence on coronavirus variants of concern detected through epidemic intelligence, the conserved epitopes of the receptor-binding domain (RBD) and spike cleavage site is the most probable target for vaccine development to inducing binds and fusion inhibitors neutralizing antibodies respectively. In this study, we utilized bioinformatics tools for identifying and analyzing the spike (S) glycoprotein sequence, e.g. the prediction of the potential linear B-cell epitopes, B-cell multi‑epitope design, secondary and tertiary structures, physicochemical properties, solubility, antigenicity, allergenicity, the molecular docking and molecular dynamics simulation for the promising vaccine candidate against all variant of concern of SARS-CoV-2. Among the epitopes of the RBD region are surface-exposed epitopes SVYAWNRKRISNCV and ATRFASVYAWNRKR as the conserved sequences in all variants of concern can be a good candidate to induce an immune response. Communicated by Ramaswamy H. Sarma

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes.

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop these vaccines and effectively monitor these diseases, it is necessary to use accurate diagnostic methods capable of identifying highly immunogenic regions within a given pathogenic protein. Existing experimental methods are expensive, time-consuming, and require arduous laboratory work, as they require the screening of a large number of potential candidate epitopes, making the methods extremely laborious, especially for application to larger microorganisms. In the last decades, researchers have developed in silico prediction methods, based on machine learning, to identify these markers, to drastically reduce the list of potential candidate epitopes for experimental tests, and, consequently, to reduce the laborious task associated with their mapping. Despite these efforts, the tools and methods still have low accuracy, slow diagnosis, and offline training. Thus, we develop a method to predict B-cell linear epitopes which are based on a Fuzzy-ARTMAP neural network architecture, called BepFAMN (B Epitope Prediction Fuzzy ARTMAP Artificial Neural Network). This was trained using a linear averaging scheme on 15 properties that include an amino acid ratio scale and a set of 14 physicochemical scales. The database used was obtained from the IEDB website, from which the amino acid sequences with the annotations of their positive and negative epitopes were taken. To train and validate the knowledge models, five-fold cross-validation and competition techniques were used. The BepiPred-2.0 database, an independent database, was used for the tests. In our experiment, the validation dataset reached sensitivity = 91.50%, specificity = 91.49%, accuracy = 91.49%, MCC = 0.83, and an area under the curve (AUC) ROC of approximately 0.9289. The result in the testing dataset achieves a significant improvement, with sensitivity = 81.87%, specificity = 74.75%, accuracy = 78.27%, MCC = 0.56, and AOC = 0.7831. These achieved values demonstrate that BepFAMN outperforms all other linear B-cell epitope prediction tools currently used. In addition, the architecture provides mechanisms for online training, which allow the user to find a new B-cell linear epitope, and to improve the model without need to re-train itself with the whole dataset. This fact contributes to a considerable reduction in the number of potential linear epitopes to be experimentally validated, reducing laboratory time and accelerating the development of diagnostic tests, vaccines, and immunotherapeutic approaches.

EpiBuilder: A Tool for Assembling, Searching, and Classifying B-Cell Epitopes.

Epitopes are portions of a protein that are recognized by antibodies. These small amino acid sequences represent a significant breakthrough in a branch of bioinformatics called immunoinformatics. Various software are available for linear B-cell epitope (BCE) prediction such as ABCPred, SVMTrip, EpiDope, and EpitopeVec; a well-known BCE predictor is BepiPred-2.0. However, despite the prediction, there are several essential steps, such as epitope assembly, evaluation, and searching for epitopes in other proteomes. Here, we present EpiBuilder (https://epibuilder.sourceforge.io), a user friendly software that assists in epitope assembly, classifying and searching using input results of BepiPred-2.0. EpiBuilder generates several output results from these data and supports a proteome-wide processing approach. In addition, this software provides the following features: Chou & Fasman beta-turn prediction, Emini surface accessibility prediction, Karplus and Schulz flexibility prediction, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity prediction, N-glycosylation domains, and hydropathy. These information generate a unique topology for each epitope, visually demonstrating its characteristics. The software can search the entire epitope sequence in various FASTA files, and it allows to use BLASTP to identify epitopes that eventually have sequence variations. As an EpiBuilder application, we developed a epitope dataset from the protozoan Trypanosoma brucei gambiense, the gram-positive bacterium Clostridioides difficile, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

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.ResultsThis 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 implementationThe 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.

Structural and immunological characterization of an epitope within the PAN motif of ectodomain I in Babesia bovis apical membrane antigen 1 for vaccine development.

BackgroundBovine babesiosis caused by Babesia bovis (B. bovis) has had a significant effect on the mobility and mortality rates of the cattle industry worldwide. Live-attenuated vaccines are currently being used in many endemic countries, but their wide use has been limited for a number of reasons. Although recombinant vaccines have been proposed as an alternative to live vaccines, such vaccines are not commercially available to date. Apical membrane antigen-1 (AMA-1) is one of the leading candidates in the development of a vaccine against diseases caused by apicomplexan parasite species. In Plasmodium falciparum (P. falciparum) AMA-1 (PfAMA-1), several antibodies against epitopes in the plasminogen, apple, and nematode (PAN) motif of PfAMA-1 domain I significantly inhibited parasite growth. Therefore, the purpose of this study was to predict an epitope from the PAN motif of domain I in the B. bovis AMA-1 (BbAMA-1) using a combination of linear and conformational B-cell epitope prediction software. The selected epitope was then bioinformatically analyzed, synthesized as a peptide (sBbAMA-1), and then used to immunize a rabbit. Subsequently, in vitro growth- and the invasion-inhibitory effects of the rabbit antiserum were immunologically characterized.ResultsOur results demonstrated that the predicted BbAMA-1 epitope was located on the surface-exposed α-helix of the PAN motif in domain I at the apex area between residues 181 and 230 with six polymorphic sites. Subsequently, sBbAMA-1 elicited antibodies capable of recognizing the native BbAMA-1 in immunoassays. Furthermore, anti-serum against sBbAMA-1 was immunologically evaluated for its growth- and invasion-inhibitory effects on B. bovis merozoites in vitro. Our results demonstrated that the rabbit anti-sBbAMA-1 serum at a dilution of 1:5 significantly inhibited (p < 0.05) the growth of B. bovis merozoites by approximately 50–70% on days 3 and 4 of cultivation, along with the invasion of merozoites by approximately 60% within 4 h of incubation when compared to the control groups.ConclusionOur results indicate that the epitope predicted from the PAN motif of BbAMA-1 domain I is neutralization-sensitive and may serve as a target antigen for vaccine development against bovine babesiosis caused by B. bovis.

In-silico Studies on Virulence Factors of Cryptococcus Species: Phylogenetic Analysis and B-cell Epitope Prediction

Virulence proteins ensure the survival of Cryptococcus in its host. The epitopes present in these virulence factors can modulate the host's immune system and contribute tocryptococcosis's pathobiology significantly. The amino acid sequences of virulence factors (glucuronoxylomannan (GXM), superoxide dismutase (SOD), mannoprotein (MP), urease, CAP binding protein, galactoxylomannan (GalXM), phospholipase-B, and laccase) of C. neoformans, C. n. grubii, and C. gattii were retrieved from NCBI. Analyses of the phylogenetic relationship between virulence factors were performed by using PhyML software and JMP 13.1 software. Further, ABCpred, BCPred, BcePred web servers were employed for the prediction of linear B-cell epitopes in amino acid sequences of said virulence factors. In all the three Cryptococcus species, laccase, CAP binding protein, and mannoprotein were highly conserved compared to GalXM, GXM, and SOD virulence factors. Superoxide dismutase (SOD) with the lowest gamma distribution value is considered to be highly adaptable. Further, the maximum number of B-cell epitopes was observed on the urease of C. n. grubii. In due course of time, Cu, Zn Superoxide dismutase (SOD) might play the main role in Cryptococcus species' pathogenicity due to its highly variable nature. Additionally, urease could be used to design epitope-based anti-cryptococcal drugs. Nonetheless, the results of this in-silico study need wet lab validation.

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.

Study on immunogenicity and antigenicity of a novel brucella multiepitope recombined protein

Currently, brucellosis is a reemerged zoonotic infectious disease with an increased incidence in recent years. A simple, rapid and sensitive method for diagnosing brucellosis can help to reduce medical burden and economic loss. Previously, a multiple epitope recombinant protein was constructed based on linear B-cell epitope prediction tools. In this study, the recombinant protein was used as an antigen to study the immune response produced by immunized mice, and goat serum was used to verify its diagnostic accuracy. The production of antibodies was successfully induced in the vaccinated mice. Flow cytometric analysis revealed that the percentage of CD4+, CD8+ and the CD4+/CD8+ ratios were increased by T cell subsets in mouse splenocytes, indicating that the recombinant protein induced a strong immune response had strong immunoreactivity. Using indirect ELISA, the recombinant protein correctly diagnosed positive and negative brucellosis samples. Compared with the whole bacterial antigen, the recombinant protein had a weaker sensitivity but a stronger specificity. Animal experiments showed that the recombinant protein had good antigenicity, and indirect ELISA indicates that it can be used as an antigen to diagnose brucellosis. Therefore, the recombinant protein is a potential candidate antigen for brucellosis vaccine development and serological diagnosis.

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells.

Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods. We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells. In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay. New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells. In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

Sequence identification and expression profile of seven Dermacentor marginatus glutathione S-transferase genes

Dermacentor marginatus is a widespread tick species and a vector of many pathogens in Eurasia. Due to the medical importance of D. marginatus, control measures are needed for this tick species. Currently tick control approaches rely mostly on acaricide application, whereas wrong and irrational acaricide use may result in drug resistance and residue problems. Vaccination as an alternative approach for tick control has been proven to be effective towards some tick species. However, immunization against D. marginatus has not yet reached satisfactory protection. The effort of in silico based analysis could predict antigenicity and identify candidates for anti-tick vaccine development. We carried out an in silico analysis of D. marginatus glutathione S-transferases (DmGSTs) in order to identify blood-feeding induced GSTs as antigens that can be used in anti-tick vaccine development. Phylogenetic analysis, linear B-cell epitope prediction, homology modeling, and conformational B-cell epitope mapping on the GST models were performed to identify highly antigenic DmGSTs. Relative gene expressions of the seven GSTs were profiled through real-time quantitative PCR (RT-qPCR) to outline GSTs up-regulated during blood feeding. The phylogenetic analysis indicated that the seven GSTs belonged to four classes of GST, including one in epsilon-class, one in zeta-class, one in omega-class, and four in mu-class. Linear B-cell epitope prediction revealed mu-class GSTs share similar conserved antigenic regions. The conformational B-cell epitope mapped on the homology model of the GSTs displayed that GSTs of mu-class showed stronger antigenicity than that of other classes. RT-qPCR revealed DmGSTM1 and DmGSTM2 were positively related to blood feeding. In sum, the data suggest that DmGSTM1 and DmGSTM2 could be tested for potential anti-tick vaccine trials.Electronic supplementary materialThe online version of this article (10.1007/s10493-020-00546-7) contains supplementary material, which is available to authorized users.