基于卷积神经网络的多聚脯氨酸二型二级结构预测

多聚脯氨酸二型螺旋是一种特殊且稀少的蛋白质二级结构。为了节省实验方法测定该结构的时间和成本，本文设计一种基于卷积神经网络的深度学习算法用于预测多聚脯氨酸二型螺旋。首先，对蛋白质序列信息进行特征编码生成特征矩阵，特征编码方式包括氨基酸正交码、氨基酸物理化学性质和位置特异性打分矩阵。其次，将归一化处理后的特征矩阵输入到卷积神经网络中，自动提取蛋白质序列的局部深层特征并输出多聚脯氨酸二型螺旋的预测结果。实验结果表明，该算法的性能相较于支持向量机之类的6种传统机器学习算法有明显的提升。

Prediction of Polyproline Type II Secondary Structure Based on Convolutional Neural Network

 Polyproline type II helix is a special and rare protein secondary structure. In order to save the time and cost of determine the structure by experimental method, a deep learning algorithm based on convolution neural network is designed to predict polyproline type II helix. First of all, the protein sequence information feature is encoded to generate feature matrix, which includes amino acid orthogonal code, physical and chemical properties of amino acids and position-specific scoring matrix. Secondly, the normalized feature matrix is inputted into convolution neural network to automatically extract the local deep features of protein sequence and output the prediction results of polyproline type II helix. The experimental results show that the performance of this algorithm is better than six traditional machine learning algorithms such as support vector machine.