Evolving fragments to lead molecules

ABSTRACT

This article describes a variable string length genetic algorithm for de novo ligand design. The input to the algorithm is the active site dimensions which guides the ligand construction. A library of forty one fragments is used to construct the ligands by evaluating the combinations of these fragments. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to evaluate the internal energy of the ligand and the interaction energy of the ligand receptor complex. Domain specific genetic operators are used to evolve the solutions to obtain better ligands. Experimental results for HIV-1 Protease and Thrombin are provided which underline the superiority of the proposed scheme over three existing approaches.

References

1. S. Bandyopadhyay, A. Bagchi, and U. Maulik. Active Site Driven Ligand Design: An Evolutionary Approach. Journal of Bioinformatics and Computational Biology, 3(5):1053--1070, October 2005.
2. D. W. Banner and P. Hadvary. Crystallographic analysis at 3.0-a resolution of the binding to human thrombin of four active site-directed inhibitors. J. Biol. Chem., 266(30):20085--20093, October 1991.
3. R. S. Bohacek and C. McMartin. Multiple highly diverse structures complementary to enzyme binding sites: Results of extensive application of a de novo design method incorporating combinatorial growth. J. Am. Chem. Soc., 116(13):5560--5571, June 1994.
4. I. J. Bruno, J. C. Cole, P. R. Edgington, M. Kessler, C. F. Macrae, P. McCabe, J. Pearson, and R. Taylor. New software for searching the cambridge structural database and visualizing crystal structures. Acta Crystallographica Section B, 58(Pt 3 Pt 1):389--97, June 2002.
5. N. Budin, N. Majeux, C. Tenette Souaille, and A. Caflisch. Structure-based Ligand Design by a Build-up Approach and Genetic Algorithm Search in Conformational Space. J. Comput. Chem., 22(16):1956--1970, June 2001.
6. G. B. Dreyer, D. M. Lambert, T. D. Meek, T. J. Carr, T. A. Tomaszek, Jr. A. V. Fernandez, H. Bartus, E. Cacciavillani, and A. M. Hassell. Hydroxyethylene isostere inhibitors of human immunodeficiency virus-1 protease: structure-activity analysis using enzyme kinetics, x-ray crystallography, and infected T-cell assays. Biochemistry, 31(29):6646--6659, July 1992.
7. A. Globus, J. Lawton, and T. Wipke. Automatic Molecular Design Using Evolutionary Techniques. Sixth Foresight Conference on Molecular Nanotechnology, 1998, Nanotechnology, 10(3):290--299, March 1999.
8. G. Goh and J. A. Foster. Evolving molecules for drug design using genetic algorithm via Molecular Tree. In Int. Conf. Genet. Evol. Comput., pages (27--33), USA 2000.
9. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York, 1989.
10. J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, AnnArbor, 1975.
11. A. R. Leach. Molecular Modelling Principles and Applications. Prentice Hall, 2001.
12. Y. Nishibata and A. Itai. Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation. J. Med. Chem., 36(20):2921--2928, October 1993.
13. S. C. Pegg, J. J. Haresco, and I. D. Kuntz. A Genetic Algorithm for Structure-based De Novo Design. J. Comput. Aided. Mol. Des., 15(10):911--933, October 2001.
14. V. Tschinke and N. C. Cohen. The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypothesis. Journal of Medicinal Chemistry, 36(24):3863--3870, November 2006.
15. R. Wang, T. Gao, and L. Lai. Ligbuilder: A multi-purpose program for structure-based drug design. J. Mol. Model., 6(7--8):498--516, August 2000.
16. J. M. Yang and C. Y. Kao. Flexible Ligand Docking Using a Robust Evolutionary Algorithm. Journal of Computational Chemistry, 21(11):988--998, March 2000.

Index Terms

• Applied computing

  • Life and medical sciences