Protein Structure Prediction in Structural Genomics without Alignment Using Support Vector Machine with Fuzzy Logic

Abstract

Protein secondary structure prediction from amino acid sequences is a challenging and complex task as it has become a must in oder to identifying the similarities/dissimilarities between protein structure. The protein secondary structure is used for studying the biological functionality of species in order to develop new drugs. A sustainable number of research has been done for predicting protein structure but yet the performance is not satisfactory. For this reason, it is necessary and time demanding to develop a technique for predicting protein structure that gives the satisfactory performance for large datasets termed as big datasets. In this article, propose a method based on the support vector machine and fuzzy logic in order to predict protein secondary structure without alignment. In this case, generate the optimal hyper plane of support vector machine using the membership values. Moreover, in order to increase the generalization ability a hybrid kernel support vector machine is propose that gives the better results in terms of classification and learning ability. We have tested the proposed method performance on the several benchmark datasets. The simulation results shows that the proposed technique outperforms better than other existing conventional techniques.