Immune recognition of infected and malignant cells requires presentation on their surface of antigens (i.e. short peptides) by human leukocyte antigen class I (HLA-I) proteins, which are coded by one of the most polymorphic alleles in the human genome. The identification of clinically relevant, tumour-specific neoantigens (mutated antigens) is currently a highly sought-after goal in designing novel cancer immunotherapeutic strategies. Algorithms aimed at predicting peptide presentation by HLA-I proteins are therefore valuable tools to accelerate the validation of putative neoantigens. To tackle this problem, we resort to a framework of inference from aminoacid sequences based on Restricted Boltzmann Machines, probabilistic graphical models characterized by a layer of ‘feature’ variables. This approach ensures efficient prediction of what antigens can be presented along with their HLA-I binding specificity; furthermore, it can be used to study in a model-guided way the effect of mutations on antigen presentation.