Abstract
Evolutionary approaches to protein-ligand docking typically use a real-value encoding and mutation operators based on Gaussian and Cauchy distributions. The choice of mutation is important for an efficient algorithm for this problem. We investigate the effect of mutation operators by locality analysis. High locality means that small variations in the genotype imply small variations in the phenotype. Results show that Gaussian-based operators have stronger locality than Cauchy-based ones, especially if an annealing scheme is used to control the variance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Neumaier, A.: Molecular modeling of proteins and mathematical prediction of protein structure. SIAM Review 39, 407–460 (1997)
Morris, G.M., Olson, A.J., Goodsell, D.S.: Protein-ligand docking. In: Clark, D.E. (ed.) Evolutionary Algorithms in Molecular Design, pp. 31–48. Wiley-VCH (2000)
Thomsen, R.: Protein-ligand docking with evolutionary algorithms. In: Fogel, G.B., Corne, D.W., Pan, Y. (eds.) Computational Intelligence in Bioinformatics, pp. 169–195. Wiley-IEEE Press, Chichester (2008)
Korb, O., Stützle, T., Exner, T.: An ant colony optimization approach to flexible protein-ligand docking. Swarm Intelligence 1, 115–134 (2007)
Thomsen, R.: Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems 72, 57–73 (2003)
Sendhoff, B., Kreutz, M., Seelen, W.V.: A condition for the genotype-phenotype mapping: Casualty. In: 7th Int. Conf. on Genetic Algorithms, pp. 73–80 (1997)
Rothlauf, F.: On the locality of representations. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), pp. 1608–1609 (2003)
Raidl, G.R., Gottlieb, J.: Empirical analysis of locality heriability and heuristic bias in evolutionary algorithms: A case study for the multidimensional knapsack problem. Evolutionary Computation Journal 13, 441–475 (2005)
Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a lamarckian genetic algorithm and and empirical binding free energy function. Journal of Computational Chemistry 19, 1639–1662 (1998)
Pereira, F.B., Marques, J., Leitão, T., Tavares, J.: Analysis of locality in hybrid evolutionary cluster optimization. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp. 8049–8056. IEEE Press, Los Alamitos (2006)
Dixon, J.S.: Flexible docking of ligands to receptor sites using genetic algorithms. In: Proc. of the 9th European Symposium on Structure-Activity Relationships, Leiden, The Netherlands, pp. 412–413. ESCOM Science Publishers (1993)
Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., Corbeil, C.: Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. British Journal of Pharmacology 153, 1–20 (2007)
Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the VI International Conference on Genetics, vol. 1, pp. 356–366 (1932)
Jones, T.: Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico, Albuquerque, New Mexico (1995)
Weinberger, E.D.: Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics 63, 325–336 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tavares, J., Tantar, AA., Melab, N., Talbi, EG. (2008). The Influence of Mutation on Protein-Ligand Docking Optimization: A Locality Analysis. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)