α-淀粉酶Amy7C及其突变体最适pH值的定量预测

摘要

Objective]pH is an important parameter in enzymatic reaction,and its determination is often through experimental path,which is generally costly and time-consuming.So,it is nec-essary to develop methods that can use as simple as possible information to predict pH opti-mum for enzyme.[Methods]20-1 feedforward backpropagation neural network was used to screen 535 amino acids properties as predictors to predict the optimal pH ofα-amylase Amy7C and its 54 mutant,which were divided as two groups.35 of them served as training group for fitting,and the other 20 were treated as valida-tion.The models for different structures and neuron numbers were also compared.[Results]109 amino acid properties,which converged dur-ing fitting in the 20-1 neural network model, could be used to predict optimal pH.Different a-mino acid properties presented different predic-ting effect,and some of them revealed better prediction for optimal pH.The multi-model results showed that there was significant difference between R values in training groups,but there was no significant difference between P values in training groups,as well as R and P values in validation groups.[Conclusion]The distribution probability and some amino acid properties could be used to predict optimal pH ofα-amylase, for which 20-1 feedforward backpropagation neural network was the relative ideal model.%【目的】pH值是影响酶催化效率的关键参数，通常需要通过实验方式才能确定生物酶的最适 pH 值，而该方式要消耗较多的人力、物力和时间。因此，有必要发展一种利用酶的简单结构信息即可预测其最适 pH 值的方法。【方法】以20-1前馈反向传播的神经网络为模型，完成535种氨基酸属性对α-淀粉酶 pH 值的拟合。同时，将α-淀粉酶 Amy7C及其54个突变体的数据分为2组，用35个酶作为训练组进行拟合，20个酶作为验证组进行检验，并对不同层次及神经元个数的模型进行比较。【结果】109个氨基酸属性可实现20-1神经网络模型收敛，表明这些氨基酸属性可用于预测α-淀粉酶的最适 pH值，但是不同氨基酸属性预测 pH 值的效果差别较大，只有部分指标预测 pH值的效果较好。多模型的分析结果显示，不同模型对训练组R值的结果具有显著性差异，而对训练组P值、验证组R值和验证组P 值结果无显著性差异。【结论】氨基酸分布概率等属性可用于预测α-淀粉酶的最适 pH值。20-1神经网络模型是预测α-淀粉酶最适 pH值相对理想的模型。