Sequence-based B-cell epitope detection software tools | Immune system data analysis
For over 30 years, computational methods have been developed for facilitating epitope recognition. In the past, the majority of the in silico methods were focused on linear epitopes. Most of these approaches are sequence-based and use amino acid-based propensity scales, such as hydrophilicity, solvent accessibility, secondary structure and flexibility; a score derived from the propensity scales is assigned to each residue, and the whole sequence is examined for high-scoring window fragments, which are then predicted as epitopes.
Predicts the location of linear B-cell epitopes using a combination of a hidden Markov model (HMM) and a propensity scale method. BepiPred is a web server constructed from a large set of structurally defined B cell epitopes. The interface can aid researchers with limited computational knowledge to use and understand the results to their full extent. Additionally, the advanced option allows more experienced researchers to further interpret the output based on additionally predicted structural features.
A method to identify the epitope region on the antigen, given the structures of the antibody and the antigen. EpiPred combines conformational matching of the antibody-antigen structures and a specific antibody-antigen score.
Allows the user to easily, quickly, and accurately increase the immunogenicity of peptide epitopes through selective inclusion of endogenous B-cell epitopes that exist within the target protein sequence near the epitope of interest. EpIC identifies the optimal iteration of your target peptide antigen through sequential single amino acid-based expansion of your peptide sequence and analysis of the resulting antigen using the BepiPred program.
The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. BCPREDS is a method for predicting linear B-cell epitopes using the subsequence kernel.
Allows users to determine the clonal-family-specific substitution profile for any single input sequence. SPURF consists of a penalized tensor regression framework that integrates multiple sources of information for performing clonal family (CF)-specific amino acid frequency profile prediction. During the test, this tool was used to show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction.
One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell's response, also called B-cell epitopes. LBtope has been developed for predicting and designing B-cell epitopes.
A web server developed for predicting different types of B-cell epitopes that can induce different class of Antibodies like IgG, IgE and IgA. In past large number of methods have been developed for predicting B-cell epitopes but no method have been developed for predicting antibody-specific epitopes. One of the major features of this server is that it assists users in designing B-cell epitopes using rational technique of mutation.
A method to predict antigenic epitope with lastest sequence input from IEDB database. Support Vector Machine (SVM) has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP) in order to achieve the better prediction performance.
Provides support vector machine (SVM) classifier models. The BROracle classifier utilizes BEOracle scores as features and therefore predicts using the information from local features only. The BEOracle was trained for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Furthermore, BROracle was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.26-63.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database.
Uses a voting algorithm for combining the predictions of two classifiers, a Gaussian Naïve Bayes and a Random Forest classifier. SEPIa is a conformational epitope prediction method that requires only the amino acid sequence as input and is based on commonly used features, but also on new ones. It utilizes a metalearning approach, which combines the predictions obtained with two different classifiers through a voting procedure and yields a single prediction with improved accuracy.
A perl based open source tool for the prediction of Linear B-cell epitopes. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics.
Prediction of linear B-cell epitopes, using physico-chemical properties. Bcepred is able to predict epitopes with 58.7% accuracy using Flexibility, Hydrophobicity, Polarity, and Surface properties combined at a threshold of 2.38.
A web-server predicting antibody-specific epitopes, utilizing the sequence of the antibody. The predictions are provided both at the residue level and as patches on the antigen structure. The tradeoff between recall and precision can be tuned by the user, by changing the default parameters. The results are provided as text and HTML files as well as a graph, and can be viewed on the antigen 3D structure.
Provides users a support vector machines (SVM) prediction model using Bayes Feature Extraction (BFE) to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). Bayesb is based on the report that linear B-cell epitopes and non-epitope sequences have distinctive residue composition and position-specific propensity patterns which could be used for epitope discrimination in silico. It aims to discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins.
Predicts epitopes from full protein chains using an approach based on averaging selected scores generated from 20-mers by a support vector machine (SVM)-based predictor. BEST uses a sliding window to represent the input antigen chain as a set of 20-mers. It then combines predictions from the SVM using a custom-designed scheme that outputs the propensity of each amino acid (AA) to form of a B-cell epitope. This tool outperforms several modern sequence-based B-cell epitope predictors.
Determines linear B-cell epitopes in hepatitis C virus (HCV). Bcell-HCV is a web server that can deal with the amino acid sequences of HCV antigens. It returns the identified B-cell epitopes with location information and their prediction scores. This application is implemented using a carrier-based vector machine (SVM) with 34 informative physico-chemical properties which enables accurate identification of epitopes of HCV B cells to assist in peptide-based vaccine development.