Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Deterministic Pharmacophore Detection Via Multiple Flexible Alignment of Drug-Like Molecules

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4453))

Abstract

We present a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is responsible for the interaction with a specific receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a standard default parameter set, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large drug-like molecule databases.

Supplementary Material:

http://bioinfo3d.cs.tau.ac.il/pharma/supp.html

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Dror, O., Shulman-Peleg, A., Nussinov, R., Wolfson, H.J.: Predicting molecular interactions in silico: I. an updated guide to pharmacophore identification and its applications to drug design. Frontiers in Medicinal Chemistry 3, 551–584 (2006)

    Google Scholar 

  2. Güner, O.F. (ed.): Pharmacophore Perception, Development, and Use in Drug Design. International University Line, La Jolla, CA, USA (2000)

    Google Scholar 

  3. Akutsu, T., Halldorsson, M.M.: On the approximation of largest common subtrees and largest common point sets. Theoretical Computer Science 233, 33–50 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  4. Shatsky, M., Shulman-Peleg, A., Nussinov, R., Wolfson, H.J.: The multiple common point set problem and its application to molecule binding pattern detection. J. Comp. Biol. 13, 407–442 (2006)

    Article  MathSciNet  Google Scholar 

  5. Holliday, J.D., Willet, P.: Using a genetic algorithm to identify common structural features in sets of ligands. J. of Molecular Graphics and Modelling 15, 203–253 (1997)

    Article  Google Scholar 

  6. Handschuh, S., Wagener, M., Gasteiger, J.: The search for the spatial and electronic requirements of a drug. J. Mol. Model. 6, 358–378 (2000)

    Article  Google Scholar 

  7. Finn, P.W., Kavraki, L.E., Latombe, J.-C., Motwani, R., Shelton, C., Venkatasubramanian, S., Yao, A.: RAPID: Randomized pharmocophore indentification for drug design. Computational Geometry: Theory and Applications 10, 263–272 (1998)

    Google Scholar 

  8. Chen, X., Rusinko III., A., Tropsha, A., Young, S.S.: Automated pharmacophore identification for large chemical data sets. J. Chem. Inf. Comput. Sci. 39, 887–896 (1999)

    Article  Google Scholar 

  9. Güner, O.F., Clement, O., Kurogi, Y.: Pharmacophore modeling and three dimensional database searching for drug design using Catalyst: Recent advances. Current Medicinal Chemistry 11, 2991–3005 (2004)

    Google Scholar 

  10. Clement, O.A., Mehl, A.T.: HipHop: Pharmacophores Based on Multiple Common-Feature Alignments. In: Pharmacophore Perception, Development, and Use in Drug Design, pp. 69–84. International University Line, La Jolla, CA, USA (2000)

    Google Scholar 

  11. Barnum, D., Greene, J., Smellie, A., Sprague, P.: Identification of common functional configurations among molecules. J. of Chemical Information and Computer Sciences 36, 563–571 (1996)

    Article  Google Scholar 

  12. Li, H., Sutter, J., Hoffmann, R.: HypGen: An automated system for generating 3D predictive pharmacophore models. In: Pharmacophore Perception, Development, and Use in Drug Design, pp. 171–189. International University Line, La Jolla, CA, USA (2000)

    Google Scholar 

  13. Crandell, C.W., Smith, D.H.: Computer-assisted examination of compounds for common three-dimensional substructures. J. of Chemical Information and Computer Sciences 23, 186–197 (1983)

    Article  Google Scholar 

  14. Brint, A.T., Willett, P.: Algorithms for the identification of three-dimensional maximal common substructures. J. of Chemical Information and Computer Sciences 27, 152–158 (1987)

    Article  Google Scholar 

  15. Takahashi, Y., Satoh, Y., Suzuki, H., Sasaki, S.: Recognition of largest common structural fragment among a variety of chemical structures. Analytical Sciences 3, 23–28 (1987)

    Article  Google Scholar 

  16. Hessler, G., Zimmermann, M., Matter, H., Evers, A., Naumann, T., Lengauer, T., Rarey, M.: Multiple-ligand-based virtual screening: Methods and applications of the MTree approach. J. Med. Chem. 48, 6575–6584 (2005)

    Article  Google Scholar 

  17. Martin, Y., Bures, M., Dahaner, E., DeLazzer, J., Lico, I., Pavlik, P.: A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J. Comput. Aided Mol. Des. 7, 83–102 (1993)

    Article  Google Scholar 

  18. Richmond, N.J., Abrams, C.A., Wolohan, P.R., Abrahamian, E., Willett, P., Clark, R.D.: GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J. Comput. Aided Mol. Des. 20, 567–587 (2006)

    Article  Google Scholar 

  19. Dixon, S.L., Smondyrev, A.M., Knoll, E.H., Rao, S.N., Shaw, D.E., Friesner, R.A.: PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput. Aided Mol. Des. 20, 647–671 (2006)

    Article  Google Scholar 

  20. Jones, G., Willett, P., Glen, R.C.: A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J. Comput. Aided Mol. Des. 9, 532–549 (1995)

    Article  Google Scholar 

  21. Cottrell, S.J., Gillet, V.J., Taylor, R., Wilton, D.J.: Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques. J. Comput. Aided Mol. Des. 18, 665–682 (2004)

    Article  Google Scholar 

  22. Lemmen, C., Lengauer, T.: Time-efficient flexible superposition of medium-sized molecules. J. Comput. Aided Mol. Des. 11, 357–368 (1997)

    Article  Google Scholar 

  23. Krämer, A., Horn, H.W., Rice, J.E.: Fast 3D molecular superposition and similarity search in databases of flexible molecules. J. Comput. Aided Mol. Des. 17, 13–38 (2003)

    Article  Google Scholar 

  24. Baum, D.: Multiple semi-flexible 3D superposition of drug-sized molecules. In: Berthold, M.R., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds.) CompLife 2005. LNCS (LNBI), vol. 3695, pp. 198–207. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Lemmen, C., Lengauer, T., Klebe, G.: FlexS: A method for fast flexible ligand superposition. J. Med. Chem. 41, 4502–4520 (1998)

    Article  Google Scholar 

  26. Stockman, G.: Object recognition and localization via Pose Clustering. J. of Computer Vision, Graphics, and Image Processing 40, 361–387 (1987)

    Article  Google Scholar 

  27. Lamdan, Y., Wolfson, H.J.: Geometric hashing: A general and efficient model-based recognition scheme. In: Proceedings of the IEEE Int. Conf. on Computer Vision, Tampa, Florida, USA, pp. 238–249. IEEE Computer Society Press, Los Alamitos (1988)

    Google Scholar 

  28. Kabsch, W.: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Cryst. A 34, 827–828 (1978)

    Google Scholar 

  29. Rarey, M., Wefing, S., Lengauer, T.: Placement of medium-sized molecular fragment into active sites of protein. J. Comput. Aided Mol. Des. 10, 41–54 (1996)

    Article  Google Scholar 

  30. Mehlhorn, K.: The LEDA Platform of Combinatorial and Geometric Computing. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  31. Huang, L., Chiang, D.: Better k-best parsing. In: Proceedings of the Ninth International Workshop on Parsing Technologies (IWPT), Vancouver, pp. 53–64 (Oct. 2005)

    Google Scholar 

  32. Staal, A.V.: Privacy: A Machine Learning View. IEEE Transactions on knowledge and data engineering 16, 939–948 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Terry Speed Haiyan Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Inbar, Y., Schneidman-Duhovny, D., Dror, O., Nussinov, R., Wolfson, H.J. (2007). Deterministic Pharmacophore Detection Via Multiple Flexible Alignment of Drug-Like Molecules. In: Speed, T., Huang, H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science(), vol 4453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71681-5_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71681-5_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71680-8

  • Online ISBN: 978-3-540-71681-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics