Lytras Spyros, Lamb Kieran D, Ito Jumpei, Grove Joe, Yuan Ke, Sato Kei, Hughes Joseph, Robertson David L
Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom.
J Virol. 2025 Feb 25;99(2):e0160124. doi: 10.1128/jvi.01601-24. Epub 2025 Jan 29.
The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence approach called protein language models (pLMs) can be used for effectively analyzing pathogen genomic data. We highlight examples of pLMs applied to predicting viral properties and evolution and lay out a framework for integrating pLMs into genomic surveillance pipelines.
在新冠疫情期间前所未有的测序工作为基因组监测成为监测流行病毒进化的强大工具铺平了道路。在此,我们讨论一种称为蛋白质语言模型(pLMs)的最先进人工智能方法如何可用于有效分析病原体基因组数据。我们重点介绍了pLMs应用于预测病毒特性和进化的实例,并提出了将pLMs整合到基因组监测流程中的框架。