Evolution-guided large language model is a predictor of virus mutation trends

Emerging viral infections, especially the global pandemic COVID-19, have had catastrophic impacts on public health worldwide. The culprit of this pandemic, SARS-CoV-2, continues to evolve, giving rise to numerous sublineages with distinct characteristics. The traditional post-hoc wet-lab approach is lagging behind, and it cannot quickly predict the evolutionary trends of the virus while consuming high costs. Capturing the evolutionary drivers of virus and predicting potential high-risk mutations has become an urgent and critical problem to address. To tackle this challenge, we introduce ProtFound-V, an evolution-inspired deep-learning framework designed to explore the mutational trajectory of virus. Take SARS-CoV-2 as an example, ProtFound-V accurately identifies the evolutionary advantage of Omicron and proposes evolutionary trends consistent with wet-lab experiments through in silico deep mutational scanning. This showcases the potential of deep learning predictions to replace traditional wet-lab experimental measurements. With the evolution-guided large language model, ProtFound-V presents a new state-of-the-art performance in key property predictions. Despite the challenge posed by epistasis to model generalization, ProtFound-V remains robust when extrapolating to lineages with different genetic backgrounds. Overall, this work paves the way for rapid responses to emerging viral infections, allowing for a plug-and-play approach to understanding and predicting virus evolution. ### Competing Interest Statement The authors have declared no competing interest.