一个蛋白质语言模型用于探索病毒健身景观
Jumpei Ito1,2, Adam Strange3, Wei Liu3,4,5
1Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan. jampei@g.ecc.u-tokyo.ac.jp.
Nature communications
|May 13, 2025
概括
一个名为CoVFit的新工具使用尖端蛋白序列预测SARS-CoV-2变种的流行风险. 这种蛋白质语言模型通过分析突变和预测病毒适应性,帮助早期识别高风险变体.
科学领域:
- 病毒学 病毒学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 新兴的SARS-CoV-2变种反复导致由于增加的健康状况而导致流行病的激增.
- 了解基因型-适应性关系对于识别高风险变异及其贡献突变至关重要.
研究的目的:
- 开发和验证CoVFit,一种蛋白质语言模型,用于从尖端蛋白序列预测SARS-CoV-2变体的适应性.
- 为早期识别具有更高流行潜力的变种提供一个工具.
主要方法:
- 调整了ESM-2蛋白语言模型.
- 在病毒监测和免疫逃避试验中的基因型适应性数据上训练 CoVFit.
- 评估了CovFit在预测具有多个突变的新型变异的适应性方面的准确性.
主要成果:
- CoVFit准确地对以前未知的SARS-CoV-2变种的健康状况进行了排名.
- 在SARS-CoV-2进化中确定了959个健康提升事件,直到2023年底.
- 证明了CoVFit在预测由单氨基酸突变驱动的进化中的适用性.
结论:
- CoVFit为SARS-CoV-2健身环境提供了有价值的见解.
- 提供了一种有效的工具,用于标记具有增加流行风险的SARS-CoV-2变种.
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