An Efficient Method for Protein Secondary Structure Prediction

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper, a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes, the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result.