DocumentCode :
599142
Title :
A population-based evolutionary algorithm for sampling minima in the protein energy surface
Author :
Saleh, Saleh ; Olson, Brian ; Shehu, Amarda
Author_Institution :
Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
fYear :
2012
fDate :
4-7 Oct. 2012
Firstpage :
64
Lastpage :
71
Abstract :
Obtaining a structural characterization of the biologically active (native) state of a protein is a long standing problem in computational biology. The high dimensionality of the conformational space and ruggedness of the associated energy surface are key challenges to algorithms in search of an ensemble of low-energy decoy conformations relevant for the native state. As the native structure does not often correspond to the global minimum energy, diversity is key. We present a memetic evolutionary algorithm to sample a diverse ensemble of conformations that represent low-energy local minima in the protein energy surface. Conformations in the algorithm are members of an evolving population. The molecular fragment replacement technique is employed to obtain children from parent conformations. A greedy search maps a child conformation to its nearest local minimum. Resulting minima and parent conformations are merged and truncated back to the initial population size based on potential energies. Results show that the additional minimization is key to obtaining a diverse ensemble of decoys, circumvent premature convergence to sub-optimal regions in the conformational space, and approach the native structure with IRMSDs comparable to state-of-the-art decoy sampling methods.
Keywords :
biology computing; evolutionary computation; greedy algorithms; molecular biophysics; molecular configurations; potential energy surfaces; proteins; biological active state; circumvent premature convergence; computational biology; conformational space; global minimum energy; greedy search; low-energy decoy conformations; low-energy local minima; memetic evolutionary algorithm; minima sampling; molecular fragment replacement technique; native state; native structure; parent conformations; population-based evolutionary algorithm; protein energy surface; state-of-the-art decoy sampling methods; structural characterization; Evolutionary computation; Memetics; Minimization; Potential energy; Proteins; Sociology; Statistics; evolutionary computation; greedy local search; local minima; molecular fragment replacement; near-native conformations; protein native state;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
978-1-4673-2746-6
Electronic_ISBN :
978-1-4673-2744-2
Type :
conf
DOI :
10.1109/BIBMW.2012.6470207
Filename :
6470207
Link To Document :
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