:: DESCRIPTION
EpiPred is a software of B-cell epitope prediction and antibody-antigen docking
::DEVELOPER
Oxford Protein Informatics Group (OPIG)
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Improving B-cell epitope prediction and its application to global antibody-antigen docking.
Krawczyk K, Liu X, Baker T, Shi J, Deane CM.
Bioinformatics. 2014 Aug 15;30(16):2288-94. doi: 10.1093/bioinformatics/btu190.
:: DESCRIPTION
CTLPred is a direct method for prediction of CTL epitopes crucial in subunit vaccine design.In direct methods the information or patterns of T cell epitopes instead of MHC binders were used for the development o f methods. The methods is based on elegant machine learning techniques like a Artificial Neural network and support vector machine .
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
Bhasin,M. and Raghava, G.P.S. (2004)
Prediction of CTL epitopes using QM, SVM and ANN techniques.
Vaccine,22,3195-3201.
:: DESCRIPTION
ABCpred server is to predict linear B cell epitope regions in an antigen sequence, using artificial neural network. This server will assist in locating epitope regions that are useful in selecting synthetic vaccine candidates, disease diagonosis and also in allergy research.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
Saha, S and Raghava G.P.S. (2006)
Prediction of Continuous B-cell Epitopes in an Antigen Using Recurrent Neural Network.
Proteins,65(1),40-48
:: DESCRIPTION
The BcePred server predicts B cell epitope based on physio-chemical properties of amino acids.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
Saha.S and Raghava G.P.S.
BcePred:Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties.
In G.Nicosia, V.Cutello, P.J. Bentley and J.Timis (Eds.) ICARIS 2004, LNCS 3239, 197-204, Springer,2004.
:: DESCRIPTION
CBTOPE server discriminate the antibody epitope residues and non-epitope residues for a given protein sequence.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Immunome Res. 2010 Oct 20;6:6. doi: 10.1186/1745-7580-6-6.
Identification of conformational B-cell Epitopes in an antigen from its primary sequence.
Ansari HR1, Raghava GP.
:: DESCRIPTION
IFNepitope is a web server to predict and design the epitope, which could induce the release of interferon gamma. The webserver has been developed on the basis of a dataset, which comprises of IFN-gamma inducing and non-inducing MHC class II binders.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
Biol Direct. 2013 Dec 5;8:30. doi: 10.1186/1745-6150-8-30.
Designing of interferon-gamma inducing MHC class-II binders.
Dhanda SK1, Vir P, Raghava GP.
:: DESCRIPTION
BJTEpitope is a software for T-cell epitope prediction for the MHC class I allele HLA-A*0201
::DEVELOPER
Center of Computational Biology, Beijing Institute of Basic Medical Sciences
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
Predivac implements a method for prediction of CD4+ T-cell epitopes based on the specificity-determining residues (SDRs) approach. SDRs are small set of structurally conserved amino acids in the peptide:protein interaction interface that are responsable for specific recognition events.
::DEVELOPER
:: SCREENSHOTS
N/A
::REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
BMC Bioinformatics. 2013 Feb 14;14(1):52. [Epub ahead of print]
PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity.
Oyarzún P, Ellis JJ, Bodén M, Kobe BT.
:: DESCRIPTION
FRED is a framework for T-cell epitope detection that offers consistent, easy, and simultaneous access to well established prediction methods for MHC binding and antigen processing. FRED can handle polymorphic proteins and offers analysis tools to combine, benchmark, or compare different methods. It is implemented in Python in a modular way and can easily be extended by user defined methods.
DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Feldhahn, M, Dönnes, P, Thiel, P, and Kohlbacher, O (2009).
FRED – A Framework for T-cell Epitope Detection
Bioinformatics, 25(20):2758-9.