摘要
【目的】α-淀粉酶是一种重要淀粉水解酶,而Km值是酶反应中重要的参数,尝试建立一种利用α-淀粉酶初级结构定量预测米氏常数Km值的有效模型。【方法】通过神经网络模型,利用535种氨基酸属性定量预测α-淀粉酶Amy7C及其52个突变体反应的Km值,其中33个酶用于模型训练,其余的用于模型验证。首先用双层的20-1前馈反向传播的神经网络进行预测,然后对多层神经网络模型进行筛选。【结果】535种氨基酸属性中有109种属性可以用模型预测,其中动态属性拟合结果较好,4个动态氨基酸属性中有3个属性可以用于模型预测,但拟合结果最好的氨基酸属性分别来自氨基酸理化性质和二级结构。对9种拟合和验证结果最好的氨基酸属性进行7种多层神经网络模型拟合,结果显示增加模型的复杂度并不能提高预测结果的精准度,表明较为简单的模型,如20-1或20-5-1是定量预测建模的首选。【结论】α-淀粉酶酶解反应的米氏常数Km,可以利用某些氨基酸属性通过神经网络模型进行定量预测。为今后利用酶的初级结构定量预测酶反应中各参数最适条件提供思路。
Objective]We attempted to develop models to quantitatively predict the Michaelis-Menten constant Km with information about primary structure ofα-amylase,which is a crucial enzyme forα-1-4 glucosidic linkages hydrolysis in starch,while Km is a very important parame-ter in enzymatic reactions.[Methods]By means of neural network,535 properties of amino acids were used to quantitatively predict Km value ofα-amylase Amy7C and its 52 mutants,which were divided into two datasets,3 3 used for model training and the rest for model validation.The training and validation were conducted firstly by means of two-layer (20-1)feedforward back-propagation neural network,and then by multi-layer neural network models.[Results]Among 535 screened properties of amino acids,109 properties can work as predictor and the dynamic properties give better results with 3 converged out of 4 in 20-1 neural network model.Howev-er,the best predicted results came from the amino acid properties with physicochemical proper-ty and second structure,of which nine predictors were conducted by seven multi-layer neural network models.The results showed that the increase in complexity of predictive models did not give too much improvement,indicating that the simpler 20-1 and 20-5-1 models should be the first choice.[Conclusion]The Michaelis-Menten constant Km ofα-amylase can be quantita-tively predicted by some amino acid properties through neural network,which paves the way for quantitatively predicting parameters in enzymat-ic reactions according to the information of pri-mary structure of enzyme.
出处
《广西科学》
CAS
2014年第6期656-663,共8页
Guangxi Sciences
基金
广西自然科学基金重点项目(2013GXNSFDA019007)
广西科技创新能力与条件建设计划项目(桂科能12237022)
广西人才小高地建设专项基金项目资助
关键词
氨基酸属性
Α-淀粉酶
KM值
定量预测
amino acid properties
α-amylase
Km value
quantitative prediction