Computational Approach for Protein Structure Prediction.
10.4258/hir.2013.19.2.137
- Author:
Amouda VENKATESAN
1
;
Jeyakodi GOPAL
;
Manimozhi CANDAVELOU
;
Sowjanya GOLLAPALLI
;
Kayathri KARTHIKEYAN
Author Information
1. Centre for Bioinformatics, Pondicherry University, Kalapet, Pondicherry, India. amouda@yahoo.com
- Publication Type:Original Article
- Keywords:
Chromosomes;
Peptide;
Methionine Enkephalin;
Islet Amyloid Polypeptide;
Mutation
- MeSH:
Enkephalin, Methionine;
Islet Amyloid Polypeptide;
Operator Regions, Genetic;
Peptides
- From:Healthcare Informatics Research
2013;19(2):137-147
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
