Abstract Interleukin-2 (IL-2) based immunotherapy has been already approved to treat certain type of cancers as it plays vital role in immune system. Thus it is important to discover new peptides or epitopes that can induce IL-2 with high efficiency. We analyzed experimentally validated IL-2 inducing and non-inducing peptides and observed differ in average amino acid composition, motifs, length, and positional preference of amino acid residues at the N- and C-terminus. In this study, 2528 IL-2 inducing and 2104 non-IL-2 inducing peptides have been used for traning, testing, traing and validation of our models. A large number of machine learning techniques and around 10,000 peptide features have been used for developing prediction models. The Random Forest-based model using hybrid features achieved a maximum accuracy of 73.25%, with AUC of 0.73 on the training set; accuracy of 72.89% with AUC of 0.72 on validation dataset. A web-server IL2pred has been developed for predicting IL-2 inducing peptides, scanning IL-inducing regions in a protein and designing IL-2 specific epitopes by ranking peptide analogs ( https://webs.iiitd.edu.in/raghava/il2pred/ ).

In silico model for predicting IL-2 inducing peptides in human