Analysis of Protein Folding using Structural Concealed Markov Model

Protein Structure Prediction (PSP) has significant applications in the fields of drug design, disease prediction and so on. Since PSP has been a great confrontation in the field of Protein Folding Research, this paper presents a novel method for protein using Structural Concealed Markov Model (SCMM). Typically, the contribution of this work has been made for appropriate mapping of protein primary structure to its 2D fold. Moreover, the model incorporates Extended Genet ic Algorithm (EGA) for effectively folding the protein sequences that are having long chain lengths. The protein sequences are preprocessed, classified and then, analyzed with some parameters such as fitness, similarity and sequence gaps in order to form the optimal protein structures. The experimental results reveal the improved efficiency and accuracy of the proposed method with a performance analysis.