A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution
A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution
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DOI：
10.1007/s00180-017-0730-6
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发表时间：
2017-09-01
影响因子：
1.3
通讯作者：
Husmeier, Dirk
中科院分区：
文献类型：
--
作者：
Davies, Vinny;Reeve, Richard;Husmeier, Dirk
了解病毒如何提供对密切相关的新出现毒株的保护，对于创造有效的疫苗至关重要。对于许多病毒来说，多个血清型通常是共同循环的，测试大量疫苗可能是不可行的。因此，发展一种病毒株间交叉保护的电子预报器对于优化疫苗选择是重要的。在这里，我们提出了一个稀疏的分层贝叶斯模型来检测病毒进化中的相关抗原位点(SABRE)，该模型可以解释数据中的实验可变性并预测抗原可变性。该方法使用尖峰和板条先验来识别病毒蛋白中对病毒中和重要的位置。使用SABRE方法，我们能够识别几种病毒中的一些关键抗原点，并提供对血清型进化历史中重大变化的估计。我们展示了我们的方法如何优于其他已有的方法：标准混合效应模型、混合效应套索和混合效应弹性网络。我们还提出了新的马尔可夫链蒙特卡罗模拟机制，它比已建立的分量吉布斯采样器的混合和收敛性能更好。
Understanding how viruses offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, multiple serotypes often co-circulate and testing large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Here we present a sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (SABRE) which can account for the experimental variability in the data and predict antigenic variability. The method uses spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. Using the SABRE method we are able to identify a number of key antigenic sites within several viruses, as well as providing estimates of significant changes in the evolutionary history of the serotypes. We show how our method outperforms alternative established methods; standard mixed effects models, the mixed effects LASSO, and the mixed effects elastic nets. We also propose novel proposal mechanisms for the Markov chain Monte Carlo simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler.