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

Abstract

AbstractEmerging 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 deeplearning 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 wetlab experiments throughin silicodeep 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.