Computational Approach for Protein Structure Prediction

Amouda Venkatesan; Jeyakodi Gopal; Manimozhi Candavelou; Sowjanya Gollapalli; Kayathri Karthikeyan

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자료유형: 학술저널

저자정보: Amouda Venkatesan Jeyakodi Gopal Manimozhi Candavelou Sowjanya Gollapalli Kayathri Karthikeyan

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제19권 제2호

발행연도: 2013.1

수록면: 137 - 147 (11page)

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Objectives: To predict the structure of protein, which dictates the function it performs, a newly designed algorithm is developed which blends the concept of self-organization and the genetic algorithm. Methods: Among many other approaches, genetic algorithm is found to be a promising cooperative computational method to solve protein structure prediction in a reasonable time. To automate the right choice of parameter values the influence of self-organization is adopted to design a new genetic operator to optimize the process of prediction. Torsion angles, the local structural parameters which define the backbone of protein are considered to encode the chromosome that enhances the quality of the confirmation. Newly designed self-configured genetic operators are used to develop self-organizing genetic algorithm to facilitate the accurate structure prediction. Results: Peptides are used to gauge the validity of the proposed algorithm. As a result, the structure predicted shows clear improvements in the root mean square deviation on overlapping the native indicates the overall performance of the algorithm. In addition, the Ramachandran plot results implies that the conformations of phi-psi angles in the predicted structure are better as compared to native and also free from steric hindrances. Conclusions: The proposed algorithm is promising which contributes to the prediction of a native-like structure by eliminating the time constraint and effort demand. In addition, the energy of the predicted structure is minimized to a greater extent, which proves the stability of protein.

#Chromosomes #Peptide #Methionine Enkephalin #Islet Amyloid Polypeptide #Mutation

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