A unified evolution-driven deep learning framework for virus variation driver prediction

Zhiwei Nie, Xudong Liu, Jie Chen, Zhennan Wang, Yutian Liu, Haorui Si, Tianyi Dong, Fan Xu, Guoli Song, Yu Wang, Peng Zhou, Wen Gao, Yonghong Tian

Abstract: The increasing frequency of emerging viral infections necessitates a rapid human response, highlighting the cost-effectiveness of computational methods. However, existing computational approaches are limited by their input forms or incomplete functionalities, preventing a unified prediction of diverse virus variation drivers and hindering in-depth applications. To address this issue, we propose a unified evolution-driven framework for predicting virus variation drivers, named Evolution-driven Virus Variation Driver prediction (E2VD), which is guided by virus evolutionary traits. With evolution-inspired design, E2VD comprehensively and significantly outperforms state-of-the-art methods across various virus mutational driver prediction tasks. Moreover, E2VD effectively captures the fundamental patterns of virus evolution. It not only distinguishes different types of mutations but also accurately identifies rare beneficial mutations that are critical for viruses to survive, while maintaining generalization capabilities across different lineages of SARS-CoV-2 and different types of viruses. Importantly, with predicted biological drivers, E2VD perceives virus evolutionary trends in which potential high-risk mutation sites are accurately recommended. Overall, E2VD represents a unified, structure-free and interpretable approach for analysing and predicting viral evolutionary fitness, providing an ideal alternative to costly wet-lab measurements to accelerate responses to emerging viral infections. A unified evolution-driven deep learning framework is presented, which outperforms state-of-the-art methods across various virus mutational driver predictions, and which captures fundamental patterns of virus evolution.