TY - GEN
T1 - A population-based evolutionary algorithm for sampling minima in the protein energy surface
AU - Saleh, Sameh
AU - Olson, Brian
AU - Shehu, Amarda
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - evolutionary computation
KW - greedy local search
KW - local minima
KW - molecular fragment replacement
KW - near-native conformations
KW - protein native state
UR - http://www.scopus.com/inward/record.url?scp=84875598484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875598484&partnerID=8YFLogxK
U2 - 10.1109/BIBMW.2012.6470207
DO - 10.1109/BIBMW.2012.6470207
M3 - Conference contribution
AN - SCOPUS:84875598484
SN - 9781467327466
T3 - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012
SP - 64
EP - 71
BT - Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012
T2 - 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2012
Y2 - 4 October 2012 through 7 October 2012
ER -