Predicting the Mutating Distribution at Antigenic Sites of the Influenza Virus.
Predicting the Mutating Distribution at Antigenic Sites of the Influenza Virus.
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DOI：
10.1038/srep20239
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发表时间：
2016-02-03
影响因子：
4.6
通讯作者：
Cao Z
中科院分区：
文献类型：
--
作者：
Xu H;Yang Y;Wang S;Zhu R;Qiu T;Qiu J;Zhang Q;Jin L;He Y;Tang K;Cao Z
流感病毒的突变导致抗原变化，从而导致反复流行和疫苗耐药性。预防措施将大大受益于预测未来菌株中新抗原位点的潜在分布的能力。通过利用90年来HA序列的广泛历史记录，我们设计了一个计算模型来模拟A/H1N1抗原位点的动态进化。以抗原序列为模板，该模型可以有效地预测未来抗原突变体的潜在分布。验证10932 HA序列从过去的16年中，超过94%的报告菌株的突变抗原位点落在我们的预测谱。同时，我们的模型可以成功捕获这些优势表位中96%的抗原位点。在完整的H3 N2历史数据集上观察到类似的结果，支持我们的模型对多种亚型流感的普遍适用性。我们的研究结果表明，未来抗原位点的突变谱可以预测的基础上，尽管在流感的广泛，随机突变的历史进化痕迹。再加上来自流感监测网络的密切监测的序列数据，我们的方法可以帮助预测季节性流感病毒抗原性的变化，并为公共卫生干预提供信息。
Mutations of the influenza virus lead to antigenic changes that cause recurrent epidemics and vaccine resistance. Preventive measures would benefit greatly from the ability to predict the potential distribution of new antigenic sites in future strains. By leveraging the extensive historical records of HA sequences for 90 years, we designed a computational model to simulate the dynamic evolution of antigenic sites in A/H1N1. With templates of antigenic sequences, the model can effectively predict the potential distribution of future antigenic mutants. Validation on 10932 HA sequences from the last 16 years showing that the mutated antigenic sites of over 94% of reported strains fell in our predicted profile. Meanwhile, our model can successfully capture 96% of antigenic sites in those dominant epitopes. Similar results are observed on the complete set of H3N2 historical data, supporting the general applicability of our model to multiple sub-types of influenza. Our results suggest that the mutational profile of future antigenic sites can be predicted based on historical evolutionary traces despite the widespread, random mutations in influenza. Coupled with closely monitored sequence data from influenza surveillance networks, our method can help to forecast changes in viral antigenicity for seasonal flu and inform public health interventions.