Abstract:  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.

Key words: convolutional neural network, polyproline type II helix, deep learning, prediction