Wu Guang, Yan Shaomin
Computational Mutation Project, DreamSciTech Consulting Co. Ltd., 301, Building 12, Nanyou A-zone, Jiannan Road, Shenzhen, Guangdong Province CN-518054, China.
Biochem Biophys Res Commun. 2005 Jan 14;326(2):475-82. doi: 10.1016/j.bbrc.2004.11.052.
In this study, we use the cross-impact analysis to define the relationship among impact, mutation, and outbreak of bird flu. Then we use the distribution rank, which is developed by us over last several years, to quantify the mutations from amino acid sequences of 134 hemagglutinins and 97 neuraminidases. With the help of Bayesian equation, we calculate the probability of occurring of mutation in H5, H6, and H9 hemagglutinins, and N1 and N2 neuraminidases. Finally, we estimate the probability of occurring of mutation with different intensities of an impact. Although we have no means to predict an impact, which is severe enough to lead to the mutations in hemagglutinins and neuraminidases resulting in the outbreak of bird flu, we can in principle monitor the changes in distribution rank along the time course, and predict the trend of mutations, even to predict the degree of outbreak of bird flu.
在本研究中,我们使用交叉影响分析来定义禽流感的影响、突变和爆发之间的关系。然后我们使用过去几年我们开发的分布秩来量化134种血凝素和97种神经氨酸酶氨基酸序列的突变。借助贝叶斯方程,我们计算H5、H6和H9血凝素以及N1和N2神经氨酸酶发生突变的概率。最后,我们估计不同影响强度下发生突变的概率。虽然我们无法预测足以导致血凝素和神经氨酸酶突变从而引发禽流感爆发的影响,但原则上我们可以监测分布秩随时间的变化,并预测突变趋势,甚至预测禽流感爆发的程度。