Abstract: Pseudoamino Acid(PseAA) composition can incorporate influence factor of a protein sequence, so as to remarkably enhance the predictive accuracy rate. The feature of PseAA composition is selected as the input of the neural network to make a model of classifying and predicting the protein third structure. Particle Swarm Optimization(PSO) algorithm optimizes the parameters of the neural network. A new classifying method named one vs. others binary classifier is introduced. Two hundred and four protein sequences studied by Chou is used as the dataset. Experimental result is tested by the rigorous Jackknife cross validation and it shows the method can improve the predictive accuracy rate.

Key words: Pseudoamino Acid(PseAA) composition, Particle Swarm Optimization(PSO) algorithm, Jackknife cross validation

摘要： 在伪氨基酸组成中加入与序列相关的影响因子能够提高蛋白质三级结构预测的准确率。将伪氨基酸组成的特征作为神经网络的输入，建立分类预测模型。选用粒子群优化算法对神经网络的参数进行优化。分类方法采用一对多的二分类方法。数据集选用Chou提出的204条蛋白质。实验结果使用Jackknife交叉验证，表明该方法能提高预测准确率。

关键词: 伪氨基酸组成, 粒子群优化算法, Jackknife交叉验证

CLC Number: 

• TP181