A Protein Secondary Structure Prediction Method Based on BP Neural Network

Abstract

Protein structure prediction is a main task in the field of bioinformatics, and the prediction of protein secondary structure is the key point of this task. Extracting representative features and efficient classification methods are the basis of the prediction process. In this paper, a prediction method based on BP neural network is proposed. We use valid protein features extracted by variable-sized sliding window and two different encoding modes (5-bit encoding and Profile encoding) as input data, to make predictions for the secondary structure of proteins. The prediction accuracies are calculated by Jackknife test on three commonly used low-similarity protein datasets: 25PDB, 1189 and 640, and this method achieves a high overall accuracy upon these three datasets.