Abstract
This paper proposes a two-stage optimization approach for protein folding simulation in the FCC lattice, inspired from the phenomenon of hydrophobic collapse. Given a protein sequence, the first stage of the approach produces compact protein structures with the maximal number of contacts among hydrophobic monomers, using the CPSP tools for optimal structure prediction in the HP model. The second stage uses those compact structures as starting points to further optimize the protein structure for the input sequence by employing simulated annealing local search and a 20 amino acid pairwise interactions energy function. Experiment results with PDB sequences show that compact structures produced by the CPSP tools are up to two orders of magnitude better, in terms of the pairwise energy function, than randomly generated ones. Also, initializing simulated annealing with these compact structures, yields better structures in fewer iterations than initializing with random structures. Hence, the proposed two-stage optimization outperforms a local search procedure based on simulated annealing alone.
Research partially supported by EPSRC Grant No. EP/D062012/1.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aarts, E.H.L.: Local search in combinatorial optimization. Wiley, New York (1998)
Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181, 223–230 (1973)
Albrecht, A.A., Skaliotis, A., Steinhöfel, K.: Stochastic protein folding simulation in the three-dimensional HP-model. Computational Biology and Chemistry 32(4), 248–255 (2008)
Backofen, R., Will, S.: A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models. Constraints 11(1), 5–30 (2006)
Backofen, R., Will, S.: Optimally Compact Finite Sphere Packings - Hydrophobic Cores in the FCC. In: Amir, A., Landau, G.M. (eds.) CPM 2001. LNCS, vol. 2089, pp. 257–272. Springer, Heidelberg (2001)
Böckenhauer, H.-J., Dayem Ullah, A.Z.M., Kapsokalivas, L., Steinhöfel, K.: A Local Move Set for Protein Folding in Triangular Lattice Models. In: Crandall, K.A., Lagergren, J. (eds.) WABI 2008. LNCS (LNBI), vol. 5251, pp. 369–381. Springer, Heidelberg (2008)
Berrera, M., Molinari, H., Fogolari, F.: Amino acid empirical contact energy definitions for fold recognition in the space of contact maps. BMC Bioinformatics 4, 8 (2003)
Cerny, V.: A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 41–51 (1985)
Crescenzi, P., Goldman, D., Papadimitriou, C., et al.: On the complexity of protein folding. Journal of Computational Biology 5, 423–465 (1998)
Cheon, M., Chang, I.: Clustering of the Protein Design Alphabets by Using Hierarchical Self-Organizing Map. Journal of the Korean Physical Society 44, 1577–1580 (2004)
Dal Palú, A., Dovier, A., Fogolari, F.: Constraint Logic Programming approach to protein structure prediction. BMC Bioinformatics 5(1) (2004)
DeLano, W.L.: The PyMOL Molecular Graphics System. DeLano Scientific, Palo Alto, CA, USA (2002), http://www.pymol.org
Dill, K.A., Bromberg, S., Yue, K., et al.: Principles of protein folding - A perspective from simple exact models. Protein Sci. 4, 561–602 (1995)
Hajek, B.: Cooling schedules for optimal annealing. Mathem. Oper. Res. 13, 311–329 (1988)
Herráez, A.: Biomolecules in the Computer: Jmol to the rescue. Biochem. Educ. 34(4), 255–261 (2006)
Kapsokalivas, L., Gan, X., Albrecht, A.A., Steinhöfel, K.: Two Local Search Methods for Protein Folding Simulation in the HP and the MJ Lattice Models. In: Proc. BIRD 2008. CCIS, vol. 13, pp. 167–179. Springer, Heidelberg (2008)
Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Krippahl, L., Barahona, P.: PSICO: Solving Protein Structures with Constraint Programming and Optimization. Constraints 7(4-3), 317–331 (2002)
Lesh, N., Mitzenmacher, M., Whitesides, S.: A complete and effective move set for simplified protein folding. In: Proc. 7th Annual International Conference on Computational Biology, pp. 188–195. ACM Press, New York (2003)
Levinthal, C.: Are there pathways for protein folding? J. de Chimie Physique et de Physico-Chimie Biologique 65, 44–45 (1968)
Miyazawa, S., Jernigan, R.L.: Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18, 534–552 (1985)
Mann, M., Will, S., Backofen, R.: CPSP-tools - Exact and Complete Algorithms for High-throughput 3D Lattice Protein Studies. BMC Bioinformatics 9 (2008)
Park, B.H., Levitt, M.: The complexity and accuracy of discrete state models of protein structure. Journal of Molecular Biology 249(2), 493–507 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ullah, A.D., Kapsokalivas, L., Mann, M., Steinhöfel, K. (2009). Protein Folding Simulation by Two-Stage Optimization. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-04962-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04961-3
Online ISBN: 978-3-642-04962-0
eBook Packages: Computer ScienceComputer Science (R0)