ABCPred: This tool relies on an RNN algorithm and is used to predict B-Cell epitopes in an antigen sequence. This tool has a prediction accuracy of 65.93%.
https://drive.google.com/drive/folders/1XEQ59MvcARE2EqSRIDFnzWVckIz8GnSP?usp=sharing
BepiPred: This tool is based on the HMM algorithm, and helps in the prediction of B-Cell epitopes. The prediction accuracy of the tool varies with the threshold. At default threshold (0.5), the accuracy can be approximated at 58%, which goes up till 99.9% at a threshold exceeding 0.7.
docs.google.com/spreadsheets/d/1N4fqT0CazTmEcnqikAKP3LSuCzlEJMQY8Rk7gJk5IHs/edit?usp=sharing
BCEPRED: This server allows users to predict B-cell epitopes using any of the physico-chemical properties ( hydrophilicity, flexibility/mobility, accessibility, polarity, exposed surface and turns) or combination of properties and works with the accuracy of 58.70% at threshold 2.38
NetCTL: This tool predicts CTL epitopes in protein sequences. The accuracy of the MHC class I peptide binding affinity is significantly improved compared to the earlier version.
MHC2Pred: This tool relies on a SVM based method for prediction of promiscuous MHC class II binders. This tool is claimed to have a prediction accuracy
exceeding 78%.
docs.google.com/spreadsheets/d/1IRkEa2PMmyJZcCRzByfAcHNGenEGo9sCpkNg3E8Ie3A/edit?usp=sharing
Propred: This tool is used to predict MHC Class-II binding regions in an antigen sequence, using quantitative matrices derived from published literature by Sturniolo et. al., 1999.
IEDB MHC-2: This website provides access to predictions of peptide binding to MHC class II molecules.