  Predicting the binding of viral peptides to the major histocompatibility
complex with machine learning can potentially extend the computational
immunology toolkit for vaccine development, and serve as a key component in the
fight against a pandemic. In this work, we adapt and extend USMPep, a recently
proposed, conceptually simple prediction algorithm based on recurrent neural
networks. Most notably, we combine regressors (binding affinity data) and
classifiers (mass spectrometry data) from qualitatively different data sources
to obtain a more comprehensive prediction tool. We evaluate the performance on
a recently released SARS-CoV-2 dataset with binding stability measurements.
USMPep not only sets new benchmarks on selected single alleles, but
consistently turns out to be among the best-performing methods or, for some
metrics, to be even the overall best-performing method for this task.
