A hybrid method for prediction of protein secondary structure based on multiple artificial neural networks
The prediction of protein secondary structure is the method of finding the way in which an amino acid sequence causes the protein structure to fold and bend into alpha helices, beta strands and other shapes. Until today, the problem of finding protein secondary structure is not fully resolved. Classification or clusterization based methods have an accuracy rate of circa 80 percent and they mainly work on a reduced set of shapes and folds. It is very difficult to predict how a local sequence of amino acids is going to behave and in which way it is going to affect the future of protein structure. Based upon the predicted secondary structure of the protein, the tertiary and quaternary predictions show the real nature and function of the protein as a whole. In this paper, we address the problem of the secondary structure prediction of protein and propose a new hybrid method based on the usage of multiple neural networks with the use of a consensus function and compare our approach with other efficient methods.