In Silico Prediction of Linear B-Cell Epitopes on Proteins.
Ontology highlight
ABSTRACT:
SUBMITTER: El-Manzalawy Y
PROVIDER: S-EPMC8109234 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND: The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply. RESULTS: We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes. CONCLUSION: We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.
| S-EPMC3005920 | biostudies-literature
Project description:High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV.This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV.This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV.
| S-EPMC4682406 | biostudies-other
Project description: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.
| S-EPMC10246785 | biostudies-literature
Project description:SARS-CoV-2 continues to infect an ever-expanding number of people, resulting in an increase in the number of deaths globally. With the emergence of new variants, there is a corresponding decrease in the currently available vaccine efficacy, highlighting the need for greater insights into the viral epitope profile for both vaccine design and assessment. In this study, three immunodominant linear B cell epitopes in the SARS-CoV-2 spike receptor-binding domain (RBD) were identified by immunoinformatics prediction, and confirmed by ELISA with sera from Macaca fascicularis vaccinated with a SARS-CoV-2 RBD subunit vaccine. Further immunoinformatics analyses of these three epitopes gave rise to a method of linear B cell epitope prediction and selection. B cell epitopes in the spike (S), membrane (M), and envelope (E) proteins were subsequently predicted and confirmed using convalescent sera from COVID-19 infected patients. Immunodominant epitopes were identified in three regions of the S2 domain, one region at the S1/S2 cleavage site and one region at the C-terminus of the M protein. Epitope mapping revealed that most of the amino acid changes found in variants of concern are located within B cell epitopes in the NTD, RBD, and S1/S2 cleavage site. This work provides insights into B cell epitopes of SARS-CoV-2 as well as immunoinformatics methods for B cell epitope prediction, which will improve and enhance SARS-CoV-2 vaccine development against emergent variants.
| S-EPMC8516869 | biostudies-literature
Project description:Cysticercosis is a public health problem in several developing countries. The oncosphere protein TSOL18 is the most immunogenic and protective antigen ever reported against porcine cysticercosis, although no specific epitope has been identified to account for these properties. Recent evidence suggests that protection might be associated with conformational epitopes. Linear epitopes from TSOL18 were computationally predicted and evaluated for immunogenicity and protection against porcine cysticercosis. A synthetic peptide was designed based on predicted linear B cell and T cell epitopes that are exposed on the surface of the theoretically modeled structure of TSOL18. Three surface epitopes from TSOL18 were predicted as immunogenic. A peptide comprising a linear arrangement of these epitopes was chemically synthesized. The capacity of the synthetic peptide to protect pigs against an oral challenge with Taenia solium proglottids was tested in a vaccine trial. The synthetic peptide was able to produce IgG antibodies in pigs and was associated to a reduction of the number of cysts, although was not able to provide complete protection, defined as the complete absence of cysts in necropsy. This study demonstrated that B cell and T cell predicted epitopes from TSOL18 were not able to completely protect pigs against an oral challenge with Taenia solium proglottids. Therefore, other linear epitopes or eventually conformational epitopes may be responsible for the protection conferred by TSOL18.
| S-EPMC3124692 | biostudies-literature
Project description: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 .
| S-EPMC10828400 | biostudies-literature
Project description:In our previous study, a proteome-wide screen was conducted to identify linear epitopes in this parasite's proteome, resulting in the discovery of three immunodominant motifs. Here, we investigated whether such antigenic peptides were found in proteins that were already known as vaccine candidates and excretome/secretome proteins for Onchocerca volvulus This approach led to the identification of 71 immunoreactive stretches in 46 proteins. A deep-dive into the immunoreactivity profiles of eight vaccine candidates that were chosen as most promising candidates for further development (Ov-CPI-2, Ov-ALT-1, Ov-RAL-2, Ov-ASP-1, Ov-103, Ov-RBP-1, Ov-CHI-1, and Ov-B20), resulted in the identification of a poly-glutamine stretch in Ov-RAL-2 that has properties for use as a serodiagnostic marker for O. volvulus infection. A peptide ELISA was developed, and the performance of this assay was evaluated. Based on this assessment, it was found that this assay has a sensitivity of 75.0% [95% CI: 64.9%-83.5%] and a specificity of 98.5% [95% CI: 94.6%-99.8%]. Furthermore, 8.7% reactivity in Asian parasite-infected individuals (8 out of 92) was observed. Besides this identification of a linear epitope marker, the information on the presence of linear epitopes in vaccine candidate proteins might be useful in the study of vaccines for river blindness.
| S-EPMC6220944 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-04-06 | E-GEOD-44717 | biostudies-arrayexpress
Project description:BackgroundLeishmaniasis is caused by intracellular Leishmania parasites that induce a T-cell mediated response associated with recognition of CD4+ and CD8+ T cell Line 1Lineepitopes. Identification of CD8+ antigenic determinants is crucial for vaccine and therapy development. Herein, we developed an open-source software dedicated to search and compile data obtained from currently available on line prediction algorithms.Methodology/principal findingsWe developed a two-phase algorithm and implemented in an open source software called EPIBOT, that consolidates the results obtained with single prediction algorithms, generating a final output in which epitopes are ranked. EPIBOT was initially trained using a set of 831 known epitopes from 397 proteins from IEDB. We then screened 63 Leishmania braziliensis vaccine candidates with the EPIBOT trained tool to search for CD8+ T cell epitopes. A proof-of-concept experiment was conducted with the top eight CD8+ epitopes, elected by EPIBOT. To do this, the elected peptides were synthesized and validated for their in vivo cytotoxicity. Among the tested epitopes, three were able to induce lysis of pulsed-target cells.ConclusionOur results show that EPIBOT can successfully search across existing prediction tools, generating a compiled list of candidate CD8+ epitopes. This software is fast and a simple search engine that can be customized to search over different MHC alleles or HLA haplotypes.
| S-EPMC4407964 | biostudies-literature
Project description:Neutralizing, serotype-specific, and hemagglutination-inhibiting antibodies against infectious bronchitis virus (IBV) are induced by epitopes in the S1 protein. Most changes in the virus genome due to mutation and recombination during serial passaging in embryonated chicken eggs occur in the S1 gene. In the current study, we tried to predict the potential linear B-cell epitopes of the S1 subunit of two Iranian 793/B isolates and then we analyzed their changes at passage level 90 due to mutations at this passage level. To predict linear B-cell epitopes of the S1 protein belonging to two Iranian 793/B isolates, we used two online epitope prediction programs called BepiPred and ABCpred. Some of the most important features of proteins including antigenicity, physicochemical properties, and secondary structure composition were analyzed. The predicted epitopes were studied between wild viruses and their passage level 90 viruses. We identified 15 potential linear B-cell epitopes among which six epitopes had the highest scores of physicochemical properties and antigenicity. Due to amino acid substitutions, seven predicted epitopes had different amino acid sequences at passage level 90. Among eight epitopes with no amino acid substitution at passage level 90, three epitopes had the highest scores. These three conserved epitopes including NH2-NQLGSCPLTGMI-COOH, NH2-GNFSDGFYPFTNSSLVKD-COOH, and NH2-GPIQGGC-COOH might be strategic and potential candidates for use in designing epitope-based vaccine researches. In conclusion, based on scores of physicochemical properties and antigenicity, it seemed that the sequence of most epitopes in wild viruses might be more antigenic and immunogenic compared to their sequence in viruses of passage 90.
| S-EPMC7904132 | biostudies-literature