[MHC-I epitope presentation prediction based on transfer learning]

Accurate epitope presentation prediction is a key procedure in tumour immunotherapies based on neoantigen for targeting T cell specific epitopes. Epitopes identified by mass spectrometry (MS) is valuable to train an epitope presentation prediction model. In spite of the accelerating accumulation of MS data, the number of epitopes that match most of human leukocyte antigens (HLAs) is relatively small, which makes it difficult to build a reliable prediction model. Therefore, this research attempted to use the transfer learning method to train a model to learn common features among the mixed allele specific epitopes. Then based on this pre-trained model, we used the allele-specific epitopes to train the final epitope presentation prediction model, termed Pluto. The average 0.1% positive predictive value (PPV) of Pluto outperformed the prediction model without pretraining with a margin of 0.078 on the same validation dataset. When evaluating Pluto on external HLA eluted ligand datasets, Pluto achieved an averaged 0.1% PPV of 0.4255, which is better than the prediction model without pretraining (0.3824) and other popular methods, including MixMHCpred (0.3369), NetMHCpan4.0-EL (0.4000), NetMHCpan4.0-BA (0.3188) and MHCflurry (0.3002). Moreover, when it comes to the evaluation of predicting immunogenicity, Pluto can identify more neoantigens than other tools. Pluto is publicly available at https://github.com/weipenegHU/Pluto.