Healthc Inform Res.  2013 Jun;19(2):137-147. 10.4258/hir.2013.19.2.137.

Computational Approach for Protein Structure Prediction

• Venkatesan A
• Gopal J
• Candavelou M
• Gollapalli S
• Karthikeyan K

• KMID: 2229495
• DOI: http://doi.org/10.4258/hir.2013.19.2.137

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.

Keyword

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

MeSH Terms

Enkephalin, Methionine
Islet Amyloid Polypeptide
Operator Regions, Genetic
Peptides
Enkephalin, Methionine
Islet Amyloid Polypeptide
Peptides

Figure

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  Figure 1 Flowchart for self-organizing genetic algorithm for protein structure prediction.

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  Figure 2 Self-organizing crossover operation.

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  Figure 3 Self-organizing mutation operation.

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  Figure 4 (A) 1PLW - graphical schema of energy variations of crossover in standard genetic algorithm (SGA). (B) Graphical schema of energy variations of mutation in SGA.

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  Figure 5 (A) 1PLW - graphical schema of energy variations in crossover of self-organizing genetic algorithm (SOGA). (B) Graphical schema of energy variations in mutation of SOGA.

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  Figure 6 (A) Native structure and its Ramachandran plot of 1 PLW, (B) Predicted structure and its Ramachandran plot. (C) Differences between the native and predicted structures on superimposition.

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  Figure 7 (A) 3DGJ - graphical schema of energy variations in crossover of self-organizing genetic algorithm (SOGA). (B) Graphical schema of energy variations in mutation of SOGA.

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  Figure 8 (A) Native structure and Ramachandran plot of 3DGJ. (B) Predicted structure and its Ramachandran plot. (C) Differences between the native and predicted structures on superimposition.

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