Multimeme Algorithms for Protein Structure Prediction
Despite intensive studies during the last 30 years researchers are yet far from the “holy grail” of blind structure prediction of the three dimensional native state of a protein from its sequence of amino acids. We introduce here a Multimeme Algorithm which is robust across a range of protein structure models and instances. New benchmark sequences for the triangular lattice in the HP model and Functional Model Proteins in two and three dimensions are included here with their known optima. As there is no favourite protein model nor exact energy potentials to describe proteins, robustness accross a range of models must be present in any putative structure prediction algorithm. We demonstrate in this paper that while our algorithm present this feature it remains, in terms of cost, competitive with other techniques.
Unable to display preview. Download preview PDF.
- 1.U. Bastolla, H. Frauenkron, E. Gerstner, P. Grassberger, and W. Nadler. Testing a new monte carlo algorithm for protein folding. Proteins: Structure, Function and Genetics, 32,52, 1998.Google Scholar
- 2.B. Berger and T. Leight. Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. In Proceedings of The Second Annual International Conference on Computational Molecular Biology, RECOMB 98, 1998.Google Scholar
- 4.B. Derrida. Random energy model: Limit of a family of disordered models. Physical Review Letters, 45, 1980.Google Scholar
- 6.A.L. Patton et al. A standard ga approach to native protein conformation prediction. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 574–581. Morgan Kauffman, 1995.Google Scholar
- 7.M. Feig and C.L. Brooks III. Multiscale modeling protocol for ab initio structure prediction. In press, 2000.Google Scholar
- 8.G.W. Greenwood, B. Lee, J. Shin, and G.B. Fogel. A survey of recent work on evolutionary approaches to the protein folding problem. In Proceedings of the Congress of Evolutionary Computation (CEC), pages 488–495. IEEE, 1999.Google Scholar
- 9.W.E. Hart. Hp instances. In http://www.cs.sandia.gov/tech reports/compbio/ tortilla-hp-benchmarks.html.
- 12.A. Kolinski, M.R. Betancourt, D. Kihara, P. Rotkiewicz, and J. Skolnick. Generalized comparative modeling (genecomp): A combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement. PROTEINS: Structure, Function, and Genetics, 44:133–149, 2001.CrossRefGoogle Scholar
- 13.N. Krasnogor. Standard hp and functional model proteins instances. http://dirac.chem.nott.ac.uk/~natk/Public/index.html, 2001.
- 14.N. Krasnogor. Studies on the Theory and Design Space of Memetic Algorithms. Ph.D. Thesis, Faculty of Computing, Mathematics and Engineering, University of the West of England, Bristol, United Kingdom, 2002.Google Scholar
- 15.N. Krasnogor, W.E. Hart, J. Smith, and D.A. Pelta. Protein structure prediction with evolutionary algorithms. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakaiela, and R.E. Smith, editors, GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufman, 1999.Google Scholar
- 16.N. Krasnogor and J.E. Smith. Emergence of pro.table search strategies based on a simple inheritance mechanism. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 2001.Google Scholar
- 18.P. Montanari, A. Colosimo, and P. Sirabella. The application of a genetic algorithm to the protein folding problem. In Proceedings of Engineering of Intelligent Systems (EIS 98), 1998.Google Scholar