Predicting the Secondary Structure of Proteins by Cascading Neural Networks

Abstract

Protein Secondary Structure Prediction (PSSP) is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle this problem, Artificial Neural Networks (ANNs) are considered as one of the successful tools that are widely used in this field. Recently, many efforts have been devoted to modify, improve and combine this methodology with other machine learning methods in order to get better results. In this work, we have proposed a two-stage feed forward neural network for prediction of protein secondary structures. To evaluate our approach, it is applied on RS126 dataset and its results are compared with some other NN-based methods.