  The SARS-CoV-2 pandemic has emphasised the importance of developing a
universal vaccine that can protect against current and future variants of the
virus. The present study proposes a novel conditional protein Language Model
architecture, called Vaxformer, which is designed to produce natural-looking
antigenicity-controlled SARS-CoV-2 spike proteins. We evaluate the generated
protein sequences of the Vaxformer model using DDGun protein stability measure,
netMHCpan antigenicity score, and a structure fidelity score with AlphaFold to
gauge its viability for vaccine development. Our results show that Vaxformer
outperforms the existing state-of-the-art Conditional Variational Autoencoder
model to generate antigenicity-controlled SARS-CoV-2 spike proteins. These
findings suggest promising opportunities for conditional Transformer models to
expand our understanding of vaccine design and their role in mitigating global
health challenges. The code used in this study is available at
https://github.com/aryopg/vaxformer .
