Title: An improved genetic algorithm for statistical potential function design and protein structure prediction

Authors: Xin Geng; Jihong Guan; Qiwen Dong; Shuigeng Zhou

Addresses: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China ' Department of Computer Science and Technology, Tongji University, Shanghai 201804, China ' Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China; School of Computer Science, Fudan University, Shanghai 200433, China ' Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China; School of Computer Science, Fudan University, Shanghai 200433, China

Abstract: Protein structure prediction is an important but far from being well-resolved problem in computational biology. It is generally regarded that the native structures of proteins correspond to minimum-energy states. Potential functions are useful in protein structure prediction. To obtain the optimal parameters of protein potential functions, we introduced several strategies to improve the basic Genetic Algorithm (GA). The improved GA was employed in statistical potential function design and protein structure prediction, and experimental results validate the effectiveness and efficiency of the proposed algorithm.

Keywords: GAs; genetic algorithms; statistical potential function; protein structure prediction; amino acids; dihedral angles.

DOI: 10.1504/IJDMB.2012.048174

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.2, pp.162 - 177

Received: 15 Jan 2010
Accepted: 07 Sep 2010

Published online: 17 Dec 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article