探索病毒适应度景观的蛋白质语言模型

IF 15.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jumpei Ito, Adam Strange, Wei Liu, Gustav Joas, Spyros Lytras, Kei Sato
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引用次数: 0

摘要

连续出现的SARS-CoV-2变体通过适应度升级(即变体之间的相对有效繁殖数)导致重复的流行病激增。对基因型-适应度关系进行建模,使我们能够确定增强病毒适应度的突变,并在检测后立即标记高风险变异。在这里,我们提出了CoVFit,一种基于ESM-2的蛋白质语言模型,旨在仅基于刺突蛋白序列预测变异适应度。CoVFit是根据来自病毒基因组监测和与免疫逃避相关的功能突变测定的基因型适应度数据进行训练的。CoVFit先后对包含近15个突变的未知未来变异的适应度进行了排序,具有信息准确性。CoVFit在截至2023年底的SARS-CoV-2进化过程中确定了959个健康度提升事件。此外,我们表明CoVFit适用于通过单氨基酸突变预测病毒进化。我们的研究深入了解了SARS-CoV-2的适应度景观,并为有效识别具有较高流行风险的SARS-CoV-2变体提供了工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A protein language model for exploring viral fitness landscapes

A protein language model for exploring viral fitness landscapes

Successively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated fitness (i.e., relative effective reproduction number between variants). Modeling the genotype–fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we present CoVFit, a protein language model adapted from ESM-2, designed to predict variant fitness based solely on spike protein sequences. CoVFit was trained on genotype–fitness data derived from viral genome surveillance and functional mutation assays related to immune evasion. CoVFit successively ranked the fitness of unknown future variants harboring nearly 15 mutations with informative accuracy. CoVFit identified 959 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, we show that CoVFit is applicable for predicting viral evolution through single amino acid mutations. Our study gives insight into the SARS-CoV-2 fitness landscape and provides a tool for efficiently identifying SARS-CoV-2 variants with higher epidemic risk.

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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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