Deepitope: Prediction of HLA-independent T-cell epitopes mediated by MHC class II using a convolutional neural network

Abstract

Computational linear T-cell epitope prediction tools allow cost and labor reduction in downstream in vitro testing, but the quality of currently available methods is compromised by the scarcity of experimental data and extensive HLA polymorphism. However, it is possible to improve prediction quality by forgoing HLA-dependency that allows treating all immunogenic sequences as a single group. This reduces the problem to a much simpler two-classes classification of determining whether a peptide is immunogenic or not. Here, we use a deep convolutional neural network capable of predicting linear T-cell epitope regions in primary structures trained using all peptides deposited in the IEDB website. We also investigate the possibility of using peptides derived from known human proteins as non-immunogenic counterexamples. We compared our model with a state-of-the-art tool and analyze the benefits of using larger databases. Our results corroborate the usefulness of HLA-free methods for practical applications that require the identification of immunogenic sequences. Deepitope is an open source project that can be found at https://github.com/raphaeltrevizani/deepitope.