  B-cell epitopes play a key role in stimulating B-cells, triggering the
primary immune response which results in antibody production as well as the
establishment of long-term immunity in the form of memory cells. Consequently,
being able to accurately predict appropriate linear B-cell epitope regions
would pave the way for the development of new protein-based vaccines. Knowing
how much confidence there is in a prediction is also essential for gaining
clinicians' trust in the technology. In this article, we propose a calibrated
uncertainty estimation in deep learning to approximate variational Bayesian
inference using MC-DropWeights to predict epitope regions using the data from
the immune epitope database. Having applied this onto SARS-CoV-2, it can more
reliably predict B-cell epitopes than standard methods. This will be able to
identify safe and effective vaccine candidates against Covid-19.
