  Accurate in silico modeling of the antigen processing pathway is crucial to
enable personalized epitope vaccine design for cancer. An important step of
such pathway is the degradation of the vaccine into smaller peptides by the
proteasome, some of which are going to be presented to T cells by the MHC
complex. While predicting MHC-peptide presentation has received a lot of
attention recently, proteasomal cleavage prediction remains a relatively
unexplored area in light of recent advancesin high-throughput mass
spectrometry-based MHC ligandomics. Moreover, as such experimental techniques
do not allow to identify regions that cannot be cleaved, the latest predictors
generate decoy negative samples and treat them as true negatives when training,
even though some of them could actually be positives. In this work, we thus
present a new predictor trained with an expanded dataset and the solid
theoretical underpinning of positive-unlabeled learning, achieving a new
state-of-the-art in proteasomal cleavage prediction. The improved predictive
capabilities will in turn enable more precise vaccine development improving the
efficacy of epitope-based vaccines. Pretrained models are available on GitHub
