Spatial distribution of selection pressure on a protein based on the hierarchical Bayesian model
Spatial distribution of selection pressure on a protein based on the hierarchical Bayesian model
复制标题
基于分层贝叶斯模型的蛋白质选择压力空间分布
DOI：
10.1093/molbev/mst151
复制
发表时间：
2013
影响因子：
10.7
通讯作者：
Teruaki Watabe and Hirohisa Kishino
中科院分区：
文献类型：
--
作者：
千葉貴裕;Gerrit E. W. Bauer;高橋三郎;Teruaki Watabe and Hirohisa Kishino
蛋白质通过氨基酸序列的取代来适应新的环境和/或获得功能。因此，蛋白质编码基因的突变受到选择压力的影响。选择压力的强度和特征在蛋白质的不同区域之间可能有所不同。因此，选择压力的空间分布提供了蛋白质适应性进化的信息。我们开发了一个分层贝叶斯模型，检测蛋白质上的选择压力的空间分布。我们用DNA序列中非同义替换与同义替换的替换率比来表示选择压力。Potts模型描述了选择压力空间聚集的先验分布。定义空间聚类的强度和范围的超参数通过最大化边缘似然来估计。因为我们的先验分布是非标准化的，我们通过“热力学积分”计算了对数边际似然。我们将该方法应用于流感血凝素蛋白的历史数据，比较抗原位点A-E的替代率比的估计空间分布。具有较高置换率的氨基酸残基与抗原位点重叠，代表多样化的选择压力。
Proteins adapt to novel environments and/or gain function by substitution in amino acid sequences. Therefore, mutations in protein-coding genes are subject to selection pressure. The strength and character of selection pressure may vary among the regions of the protein. Thus, the spatial distribution of selection pressure provides information on the adaptive evolution of the protein. We developed a hierarchical Bayesian model that detects the spatial distribution of selection pressure on a protein. We expressed selection pressure by the substitution rate ratio of nonsynonymous to synonymous substitutions in the DNA sequence. The Potts model describes the prior distribution of spatial aggregation of selection pressure. The hyperparameters that define the strength and range of spatial clustering are estimated by maximizing the marginal likelihood. Because our prior distribution is un-normalized, we calculated the log marginal likelihood by “thermodynamic integration.” We applied the method to historical data on the influenza hemagglutinin protein, comparing the estimated spatial distribution of the substitution rate ratio with that of antigenic sites A–E. The amino acid residues with higher substitution rate ratios, representing diversifying selection pressure, overlapped the antigenic sites.