Peng Sihua, Lyu Leke, Carmola Ludy Registre, Subedi Sachin, Mubassir M H M, Bakheet Mohamed A, Bahl Justin
Center for Ecology of Infectious Diseases, University of Georgia, Athens, GA, United States.
Department of Infectious Diseases, University of Georgia, Athens, GA, United States.
Front Immunol. 2025 Jun 18;16:1576414. doi: 10.3389/fimmu.2025.1576414. eCollection 2025.
The COVID-19 pandemic posed global health challenges. Understanding SARS-CoV-2's evolutionary dynamics, especially fitness and immune escape, is vital for public health. This study uses protein language models to assess how genetic variations affect viral adaptability and immunity.
We applied the CoVFit model to predict Fitness and Immune Escape Index (IEI), validated by a null model based on neutral evolution. We analyzed 2,504,278 SARS-CoV-2 spike sequences, including 160,892 variants, tracking evolution from 2020 to May 2024, comparing real and random mutants' Fitness and IEI.
Our analysis revealed an increase in Fitness (mean rising from 0.227 in 2020 to 0.930 in 2024) and IEI (mean increasing from 0.171 to 0.555) for North American samples. Globally, the comparison of Fitness and IEI between real and random mutants (generated by the null model) revealed statistically significant differences (real mutant Fitness 0.3849 vs. random mutant 0.2046, p < 0.001, KS test; real mutant IEI 0.2894 vs. random mutant 0.1895, p < 0.001, KS test), indicating strong selective pressure; the JN.1 lineage dominated (94% of sequences by April 2024), underscoring its evolutionary advantage.
CoVFit offers key insights into SARS-CoV-2 evolution, aiding vaccine design. Persistent viral adaptation despite interventions highlights the need for surveillance and adaptive strategies using tools like CoVFit for preparedness.
新冠疫情带来了全球健康挑战。了解严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的进化动态，尤其是适应性和免疫逃逸，对公共卫生至关重要。本研究使用蛋白质语言模型来评估基因变异如何影响病毒的适应性和免疫性。
我们应用CoVFit模型预测适应性和免疫逃逸指数（IEI），并通过基于中性进化的空模型进行验证。我们分析了2,504,278个SARS-CoV-2刺突蛋白序列，包括160,892个变体，追踪了2020年至2024年5月的进化情况，比较了真实突变体和随机突变体的适应性和IEI。
我们的分析显示，北美样本的适应性（平均值从2020年的0.227升至2024年的0.930）和IEI（平均值从0.171增至0.555）有所增加。在全球范围内，真实突变体和随机突变体（由空模型生成）之间的适应性和IEI比较显示出统计学上的显著差异（真实突变体适应性0.3849 vs.随机突变体0.2046，p < 0.001，柯尔莫哥洛夫-斯米尔诺夫检验；真实突变体IEI 0.2894 vs.随机突变体0.1895，p < 0.001，柯尔莫哥洛夫-斯米尔诺夫检验），表明存在强大的选择压力；JN.1谱系占主导地位（截至2024年4月占序列的94%），突出了其进化优势。
CoVFit为SARS-CoV-2的进化提供了关键见解，有助于疫苗设计。尽管采取了干预措施，但病毒持续适应凸显了使用CoVFit等工具进行监测和制定适应性策略以做好准备的必要性。
