IMPROVING SEARCH FOR LOW ENERGY PROTEIN STRUCTURES WITH AN ITERATIVE NICHE GENETIC ALGORITHM

Glennie Helles

Abstract

In attempts to predict the tertiary structure of proteins we use almost exclusively metaheuristics. However, despite known differences in performance of metaheuristics for different problems, the effect of the choice of metaheuristic has received precious little attention in this field. Particularly parallel implementations have been demonstrated to generally outperform their sequential counterparts, but they are nevertheless used to a much lesser extent for protein structure prediction. In this work we focus strictly on parallel algorithms for protein structure prediction and propose a parallel algorithm, which adds an iterative layer to the traditional niche genetic algorithm. We implement both the traditional niche genetic algorithm and the parallel tempering algorithm in a fashion that allows us to compare the algorithms and look at how they differ in performance. The results show that the iterative niche algorithm converges much faster at lower energy structures than both the traditional niche genetic algorithm and the parallel tempering algorithm.

References

  1. Alba, E. (2005). Parallel Metaheuristics. Wiley.
  2. Cant-Paz, E. and Goldberg, D. E. (1996). Modeling idealized bounding cases of parallel genetic algorithms. In In, pages 353-361. Morgan Kaufmann Publishers.
  3. Earlab, D. J. and Deem, M. W. (2005). Parallel tempering: Theory, applications, and new perspectives. Phys. Chem. Chem. Phys., 7:3910.
  4. Fonseca, R. and Helles, G. (2009). Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks. In submission with BMC Bioinformatics.
  5. Heiler, M. (1998). Massively parallel gas for protein structure.
  6. Helles, G. (2008). A comparative study of the reported performance of Ab Initio protein structure prediction algorithms. J. R. Soc. Interface, 5:387396.
  7. Kone, A. and Kofke, D. A. (2005). Selection of temperature intervals for parallel-tempering simulations. J. Chem. Phys., 122:206101.
  8. Lin, M. S., Fawzi, N. L., and Head-Gordon, T. (2007). Hydrophobic potential of mean force as a solvation function for protein structure prediction. Structure, 15:727-740.
  9. Oakley, M. T., Barthel, D., Bykov, Y., Garibaldi, J. M., Burke, E. K., Krasnogor, N., and Hirst, J. D. (2008). Search strategies in structural bioinformatics. Current Protein and Peptide Science, 9:260274.
  10. Ramachandran, G. N. and Sasisekharan, V. (1968). Conformations of polypeptides and proteins. Adv. Protein Chem., 23:283-437.
  11. Sanvicente-Snchez, H. and Frausto-Sols, J. (2004). A method to establish the cooling scheme in simulated annealing like algorithms. LNCS, 3945:755-763.
  12. Swendsen, R. H. and Wang, J.-S. (1986). Replica monte carlo simulation of spin-glasses. Physical review letters, 57:2607-2609.
Download


Paper Citation


in Harvard Style

Helles G. (2010). IMPROVING SEARCH FOR LOW ENERGY PROTEIN STRUCTURES WITH AN ITERATIVE NICHE GENETIC ALGORITHM . In Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010) ISBN 978-989-674-019-1, pages 226-232. DOI: 10.5220/0002743702260232


in Bibtex Style

@conference{bioinformatics10,
author={Glennie Helles},
title={IMPROVING SEARCH FOR LOW ENERGY PROTEIN STRUCTURES WITH AN ITERATIVE NICHE GENETIC ALGORITHM},
booktitle={Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)},
year={2010},
pages={226-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002743702260232},
isbn={978-989-674-019-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)
TI - IMPROVING SEARCH FOR LOW ENERGY PROTEIN STRUCTURES WITH AN ITERATIVE NICHE GENETIC ALGORITHM
SN - 978-989-674-019-1
AU - Helles G.
PY - 2010
SP - 226
EP - 232
DO - 10.5220/0002743702260232