Differential Evolution for Protein Structure Prediction Using the HP Model - Foundations on Natural and Artificial Computation - page 331

parameters in its implementation. The differences of vectors used in DE are ad-
equate to search in the multimodal energy landscape inherent to the HP protein
folding problem, where the adaptive perturbations or mutations are applied over
the segments of the protein conformations that are different in the vectors or
individuals of the genetic population. The hybridization of DE with the repair
procedures provided a significant reduction of the necessary conformations to
obtain the best energy values in different benchmark sequences.
Acknowledgments. This paper has been funded by the Ministry of Science
and Innovation of Spain (project TIN2007-64330).
References
1. Anfinsen, C.B.: Principles that govern the folding of proteins. Science 181(96),
223-230 (1973)
2. Cotta, C.: Protein structure prediction using evolutionary algorithms hybridized
with backtracking. In: Mira, J., Alvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2687,
pp. 321-328. Springer, Heidelberg (2003)
3. Cox, G.A., Mortimer-Jones, T.V., Taylor, R.P., Johnston, R.L.: Development and
optimization of a novel genetic algorithm for studying model protein folding. Theor.
Chem. Acc. 112, 163-178 (2004)
4. Custodio, F.L., Barbosa, H.J.C., Dardenne, L.E.: Investigation of the three dimen-
sional lattice HP protein folding model using a genetic algorithm. Genetics and
Molecular Biology 27(4), 611-615 (2004)
5. Cutello, V., Nicosia, G., Pavone, M., Timmis, J.: Immune algorithm for protein
structure prediction on lattice models. IEEE T. Evol. Comp. 11(1), 101-117 (2007)
6. Dill, K.A.: Dominant forces in protein folding. Biochemestry 29, 7133-7155 (1990)
7. Dor, O., Zhou, Y.: Achieving 80% tenfold cross-validated accuracy for secondary
structure prediction by large-scale training. Proteins 66(4), 838-845 (2007)
8. Dill, K.A.: et al. Principles of protein folding: a perspective from simple exact
models. Protein. Science 4(3), 561-602 (1995)
9. Feoktistov, V.: Differential Evolution: In Search of Solutions. Springer, Heidelberg
(2006)
10. Hart, W.E., Istrail, S.: Robust proofs of NP-hardness for protein folding: General
lattices and energy potentials. Journal of Computational Biology 4(1), 1-22 (1997)
11. Krasnogor, N., Blackburne, B.P., Burke, E.K., Hirst, J.D.: Multimeme algorithms
for protein structure prediction. In: Guervos, J.J.M., Adamidis, P.A., Beyer, H.-G.,
Fernandez-Villacanas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439,
pp. 769-778. Springer, Heidelberg (2002)
12. Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein structure prediction
with evolutionary algorithms. In: Proc. GECCO 1999, pp. 1596-1601 (1999)
13. Lopes, H.S., Bitello, R.A.: Differential evolution approach for protein folding using
a lattice model. J. of Computer Science and Technology 22(6), 904-908 (2007)
14. Lopes, H.S., Scapin, M.P.: An enhanced genetic algorithm for protein struc-
ture prediction using the 2D hydrophobic-polar model. In: Talbi, E.-G., Liardet,
P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871,
pp. 238-246. Springer, Heidelberg (2006)