It has been observed that conformational B cell epitopes (~90% of all B cell epitopes) are more complex and hard to define than sequential epitopes. Several methods do exist for the prediction of conformational B cell epitope but they require antigen 3D structure or homology based model of the amino acid sequence. So far no method is available which can predict conformational B cell epitope using antigen primary sequence in the absence of any homology with the known structures. In the present study using amino acid composition as an input feature for Support vector machine (SVM) we developed a model with prediction accuracy of more than 85% and Area under curve (AUC) 0.9.
If you are using this webserver, please cite:
- Hifzur Rahman Ansari and Gajendra PS Raghava. (2010) Identification of conformational B-cell Epitopes in an antigen from its primary sequence.
Immunome Research 6:6.
- Ansari HR, Raghava GP. (2013) In silico models for B-cell epitope recognition and signaling. Methods Mol Biol. 993:129-38.
