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Brief Bioinform
. 2025 May 1;26(3):bbaf276.
doi: 10.1093/bib/bbaf276. Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution
Xudong Liu 1 , Zhiwei Nie 1 2 , Haorui Si 3 4 5 , Xurui Shen 5 , Yutian Liu 2 6 , Xiansong Huang 2 , Tianyi Dong 5 7 8 , Fan Xu 2 , Zhixiang Ren 2 , Peng Zhou 3 5 , Jie Chen 1 2
Affiliations
Predicting the mutation prevalence trends of emerging viruses in the real world is an efficient means to update vaccines or drugs in advance. It is crucial to develop a computational method for the prediction of real-world prevalent SARS-CoV-2 mutations considering the impact of multiple selective pressures within and between hosts. Here, a deep-learning generative framework for real-world prevalent SARS-CoV-2 mutation prediction, named ViralForesight, is developed on top of protein language models and in silico virus evolution. Through the paradigm of host-to-herd in silico virus evolution, ViralForesight reproduced previous real-world prevalent SARS-CoV-2 mutations for multiple lineages with superior performance. More importantly, ViralForesight correctly predicted the future prevalent mutations that dominated the COVID-19 pandemic in the real world more than half a year in advance with in vitro experimental validation. Overall, ViralForesight demonstrates a proactive approach to the prevention of emerging viral infections, accelerating the process of discovering future prevalent mutations with the power of generative deep learning.
Keywords: generative deep learning; in silico virus evolution; mutation prediction; protein language model.
. 2025 May 1;26(3):bbaf276.
doi: 10.1093/bib/bbaf276. Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution
Xudong Liu 1 , Zhiwei Nie 1 2 , Haorui Si 3 4 5 , Xurui Shen 5 , Yutian Liu 2 6 , Xiansong Huang 2 , Tianyi Dong 5 7 8 , Fan Xu 2 , Zhixiang Ren 2 , Peng Zhou 3 5 , Jie Chen 1 2
Affiliations
- PMID: 40532108
- DOI: 10.1093/bib/bbaf276
Predicting the mutation prevalence trends of emerging viruses in the real world is an efficient means to update vaccines or drugs in advance. It is crucial to develop a computational method for the prediction of real-world prevalent SARS-CoV-2 mutations considering the impact of multiple selective pressures within and between hosts. Here, a deep-learning generative framework for real-world prevalent SARS-CoV-2 mutation prediction, named ViralForesight, is developed on top of protein language models and in silico virus evolution. Through the paradigm of host-to-herd in silico virus evolution, ViralForesight reproduced previous real-world prevalent SARS-CoV-2 mutations for multiple lineages with superior performance. More importantly, ViralForesight correctly predicted the future prevalent mutations that dominated the COVID-19 pandemic in the real world more than half a year in advance with in vitro experimental validation. Overall, ViralForesight demonstrates a proactive approach to the prevention of emerging viral infections, accelerating the process of discovering future prevalent mutations with the power of generative deep learning.
Keywords: generative deep learning; in silico virus evolution; mutation prediction; protein language model.