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 .

本研究提出了一种名为Vaxformer的新型条件蛋白质语言模型，用于生成类自然的抗原控制的SARS-CoV-2刺突蛋白。实验结果表明，Vaxformer模型比现有的状态-of-the-art条件变分自编码器模型表现更优，为疫苗设计和缓解全球健康挑战的角色带来了希望。

Vaxformer：抗原性可控Transformer用于设计SARS-CoV-2疫苗