Prediction of Protein Secondary Structure With Clonal Selection Algorithm and Multilayer Perceptron

Abstract

The recent studies indicate that the protein secondary structure provides very important advantages in determining the function of a protein, treating numerous diseases and drug design. Determining the secondary structure in the laboratory environment is both costly and challenging. Therefore, the prediction of protein secondary structure has been an important study field of bioinformatics and computational biology for many years. The aim of this paper was to provide a contribution to the prediction of protein secondary structure using the nature-inspired methods. The data in the first phase were trained with clonal selection algorithm (CSA) which was modeled by being inspired by the live immune system. The classification was then performed with multilayer perceptron which is one of the deep learning methods modeled by being inspired by the biological nervous system. The results obtained indicated that training of the data with CSA prior to classification contributed positively to classification success.