Criterion based Two Dimensional Protein Folding Using Extended GA

In the dynamite field of biological and pr otein research, the protein fold recognition for long pat tern protein sequences is a great confrontation for many years. With that consideration, this paper contributes to the protei n folding research field and presents a novel procedure for m apping appropriate protein structure to its correct 2D fol d by a concrete model using swarm intelligence. Moreover, the model incorporates Extended Genetic Algorithm (EGA) with concealed Markov model (CMM) for effectively folding the protein sequences that are having long chain lengths. The p rotein sequences are preprocessed, classified and then, an alyzed with some parameters (criterion) such as fitness, simila rity and sequence gaps for optimal formation of protein stru ctures. Fitness correlation is evaluated for the determination of b onding strength of molecules, thereby involves in efficient fold re cognition task. Experimental results have shown that the proposed method is more adept in 2D protein folding and outperforms the existing algorithms.