National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Guangxi, China.
Ann Biomed Eng. 2010 Mar;38(3):984-92. doi: 10.1007/s10439-010-9907-7.
Quantification of mutation capacity within a protein could be a way to model the mutation relationship not only because history might not leave many cues on the causes for mutations but also the evolved protein might no longer be subject to previous mutation causes. Randomness should play a constant role in engineering mutations in proteins because randomness suggests the maximal probability of occurrence by which a protein would be constructed with the least time and energy to meet the speed of rapidly changing environments. Since 1999, we have developed three approaches for quantifying of randomness of protein by which each amino acid has three numeric values. In this study, we model our three random numeric values in each amino acid with occurrence and non-occurrence of mutation, which are classified as unity and zero, using a 3-6-1 feedforward backpropagation neural network to predict the mutation positions in H5N1 neuraminidases. The results show that the neural network can capture the mutation relationship as measured by prediction sensitivity, specificity, and total correct rate. With the help of translation probability between RNA codes and mutated amino acids, we predict the would-be-mutated amino acids at predicted mutation positions.
量化蛋白质中的突变能力可以成为模拟突变关系的一种方法，这不仅是因为历史可能没有留下很多关于突变原因的线索，而且进化后的蛋白质可能不再受以前突变原因的影响。随机性应该在蛋白质的工程突变中发挥恒定的作用，因为随机性表明蛋白质以最少的时间和能量构建的最大概率，以满足快速变化的环境的速度。自 1999 年以来，我们已经开发了三种量化蛋白质随机性的方法，其中每个氨基酸都有三个数值。在这项研究中，我们使用 3-6-1 前馈反向传播神经网络将我们在每个氨基酸中的三个随机数值与突变的发生和不发生建模，这些数值被分类为 1 和 0，以预测 H5N1 神经氨酸酶中的突变位置。结果表明，神经网络可以通过预测灵敏度、特异性和总正确率来捕捉突变关系。借助 RNA 密码子和突变氨基酸之间的翻译概率，我们预测了预测突变位置的可能突变氨基酸。
