Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development
This work addresses the need for accurate and trustworthy epitope prediction to identify safe and effective COVID-19 vaccine candidates, representing an incremental improvement in domain-specific methods.
The paper tackled the problem of predicting B-cell epitopes for SARS-CoV-2 vaccine development by proposing a calibrated uncertainty estimation method using MC-DropWeights, resulting in more reliable predictions than standard methods.
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.