Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.
Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.
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通过抗原和抗血清相似性的矩阵完成来预测流感抗原性
DOI：
10.3389/fmicb.2018.02500
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发表时间：
2018
影响因子：
5.2
通讯作者：
Yang J
中科院分区：
文献类型：
--
作者：
Wang P;Zhu W;Liao B;Cai L;Peng L;Yang J
流感病毒的快速变异，特别是在血凝素（HA）和神经氨酸酶（NA）两种表面蛋白上的变异，使其具有逃避群体免疫的能力，这成为流感疫苗设计的关键挑战。因此，及时预测流感抗原演变和鉴定新的抗原变异是至关重要的。然而，传统的实验方法，如血凝抑制（HI）试验选择疫苗株是时间和劳动密集型的，而流行的计算方法是不太敏感，这提出了更准确的算法的需要。在这项研究中，我们提出了一种新的低秩矩阵完成模型MCAAS来推断抗原和抗血清之间的抗原距离的基础上部分揭示的抗原距离，基于HA蛋白序列的病毒相似性，和基于疫苗株的疫苗相似性。该模型利用了病毒和疫苗在血清学试验中的相关性以及来自病毒和疫苗株的HA推断流感抗原性的能力。我们还比较了全面的65个氨基酸取代矩阵预测流感抗原性的效果。因此，我们将MCAAS应用于H3N2季节性流感病毒数据。我们的模型实现了0.5982的10倍交叉验证均方根误差（RMSE），显著优于现有的计算方法，如抗原制图，抗原图和BMCSI。构建了H3N2亚型流感病毒的抗原图谱，研究了H3N2亚型流感病毒的遗传进化与抗原进化之间的关系。最后，我们的分析表明，同源结构衍生的氨基酸取代矩阵（HSDM）是最强大的预测流感抗原性，这与以前的研究是一致的。
The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies.
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DOI：
10.1084/jem.78.5.407
发表时间：
1943-11-01
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Hirst GK
通讯作者：
Hirst GK
影响因子：
4.3
作者：
Cai Z;Zhang T;Wan XF
通讯作者：
Wan XF
影响因子：
5.8
作者：
Huang, Li;Li, Xianhong;Yang, Jialiang
通讯作者：
Yang, Jialiang
影响因子：
4
作者：
Lee, Hyojung;Lee, Sunmi;Lee, Chang Hyeong
通讯作者：
Lee, Chang Hyeong
MRUniNovo：利用 hadoop 分布式计算框架进行从头肽测序的高效工具
影响因子：
5.8
作者：
Li, Chuang;Chen, Tao;Li, Kenli
通讯作者：
Li, Kenli