Combining Artificial Neural Networks and GOR-V Information Theory to Predict Protein Secondary Structure from Amino Acid Sequences

Protein secondary structure prediction is a fundamental step in determining the 3D structure of a protein. In this paper, a new method for predicting protein secondary structure from amino acid sequences has been proposed and implemented. Cuff and Barton 513 protein data set is used in training and testing the prediction methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GOR-V information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structure classes (helices, strands, and coils). The newly developed method (NN-GORV-I) is improved further by applying a filtering mechanism to the searched database and, hence, named NN-GORV-II. The developed prediction methods are rigorously analyzed and tested, together with other five well-known prediction methods in this domain in order to allow easy comparison and clear conclusions.