Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms

Natural Computing Series

Volumn , Issue , 2008, Pages 93-114

  Cutello, Vincenzo ^a   Narzisi, Giuseppe ^a,b   Nicosia, Giuseppe ^a  

^a UNIVERSITY OF CATANIA   (Italy)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85128819292     PISSN: 16197127     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-72964-8_5     Document Type: Chapter

References (28)

1. C. Anfinsen. Principles that govern the folding of protein chains. Science, 181: 223–230, 1973.
2. C. Anfinsen, E. Haber, M. Sela, and J. F. H. White. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. USA, 47:1309–1314, 1961.
3. J. U. Bowie and D. Eisemberg. An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function. Proc. Natl Acad Sci USA, 91:4436–4440, 1994.
4. J. Branke, K. Deb, H. Dierolf, and M. Osswald. Finding knees in multi-objective optimization. In PPSN, pages 722–731, 2004.
5. W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell,, and P. A. Kollman. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc., 117(19):5179–5197, 1995.
6. Y. Cui, R. S. Chen, and W. H. Wong. Protein folding simulation using genetic algorithm and supersecondary structure constraints. Proteins: Structure, Function and Genetics, 31(3):247–257, 1998.
7. V. Cutello, G. Narzisi, and G. Nicosia. A class of pareto archived evolution strategy algorithms using immune inspired operators for ab-initio protein structure prediction. In EvoWorkshops, pages 54–63, 2005.
8. V. Cutello, G. Narzisi, and G. Nicosia. A multi-objective evolutionary approach to the protein structure prediction problem. Journal of Royal Society Interface, 3(6):139–151, Feb. 2006.
9. A. Dal Palu, A. Dovier, and F. Fogolari. Constraint logic programming approach to protein structure prediction. BMC Bioinformatics, 5(1):186, 2004. URL http://www.biomedcentral.com/1471-2105/5/186.
10. R. O. Day, J. B. Zydallis, and G. B. Lamont. Solving the protein structure prediction problem through a multiobjective genetic algorithm. ICNN, 2:31– 35, 2001.
11. J. Handl and J. D. Knowles. Exploiting the trade-off-the benefits of multiple objectives in data clustering. In EMO, pages 547–560, 2005.
12. U. H. Hansmann and Y. Okamoto. Numerical comparisons of three recently proposed algorithms in the protein folding problem. J Comput Chem, 18:920– 933, 1997.
13. J. Hermans, H. J. C. Berendsen, W. F. V. Gunsteren, and J. P. M. Postma. A consistent empirical potential for water-protein interactions. Biopolymers, 23 (8):1513–1518, 1984.
14. E. Huang, S. Subbiah, and M. Levitt. Recognizing native folds by the arrangement of hydrophobic and polar residues. J. Mol. Biol., 252:709–720, 1995.
15. J. L. Klepeis, M. J. Pieja, and C. A. Floudas. Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids. Biophys. J., 84(2):869–882, 2003.
16. J. D. Knowles and D. W. Corne. The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99), 10:98–105, 1999.
17. J. D. Knowles and D. W. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computing, 8(2):149–172, 2000.
18. C. Levinthal. Are there pathways to protein folding ? J. Chem. Phys., 65 (44-45), 1968.
19. F. A. Momany, R. F. McGuire, A. W. Burgess,, and H. A. Scheraga. Energy parameters in polypeptides. vii. geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J. Phys. Chem., 79(22):2361– 2381, 1975.
20. N. F. N and J. A. D. MacKerell A. D. All-atom empirical force field for nucleic acids: I. parameter optimization based on small molecule and condensed phase macromolecular target data. J Comput Chem, 21:86–104, 2000.
21. M. Nanias, M. Chinchio, J. Pillardy, D. R. Ripoll, and H. A. Scheraga. Packing helices in proteins by global optimization of a potential energy function. PNAS, 100(4):1706–1710, 2003.
22. G. Nicosia. Immune Algorithms for Optimization and Protein Structure Prediction. PhD thesis, Department of Mathematics and Computer Science, University of Catania, 2004.
23. J. T. Pendersen and J. Moult. Protein folding simulations with genetic algorithms and a detailed molecular description. J Mol Biol, 169:240–259, 1997.
24. S. S. Plotkin and J. N. Onuchic. Understanding protein folding with energy landscape theory. Quarterly Reviews of Biophysics, 35(2):111–167, 2002.
25. G. Pollastri, D. Przybylski, B. Rost, and P. Baldi. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins: Structure, Function, and Genetics, 47(2):228– 235, 2002.
26. J. R. L. Dunbrack and F. E. Cohen. Bayesian statistical analysis of protein sidechain rotamer preferences. Protein Science, 6:1661–1681, 1997.
27. K. T. Simons, C. Kooperberg, E. Huang, and D. Baker. Assembly of of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring function. J Mol Biol, 306:1191–1199, 1997.
28. Z. Sun, X. Rao, L. Peng, and D. Xu. Prediction of protein supersecondary structures based on the artificial neural network method. Protein Eng., 10: 763–769, 1997.