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DOI: 10.1055/s-2006-941506
Integrated in Silico Tools for Exploiting the Natural Products’ Bioactivity
Publication History
Received: February 12, 2006
Accepted: April 17, 2006
Publication Date:
19 June 2006 (online)
Abstract
Whereas computational methods for molecular design are well established in medicinal chemistry research, their application in the field of natural products is still not exhaustively explored. This article gives a short introduction into both the potential for the application of computer-assisted approaches, such as pharmacophore modelling, virtual screening, docking, and neural networking to efficiently access the bioactive metabolites, and the requirements and limitations related to this specific field. The challenge is which selection criteria and/or multiple filtering tools to apply for a target-oriented isolation of potentially bioactive secondary metabolites. Application examples are provided where in silico tools and classical methods used by natural product scientists are used in an effort to maximize their efficacy in drug discovery. Thus, integrated computer-assisted strategies may help to process the huge amount of available structural and biological information in a reasonably short time for a straightforward search of bioactive natural products.
Key words
Natural products - drug discovery - virtual screening - neural network - pharmacophore modelling - in combo approach
References
-
1 Schmitz R. Geschichte der Pharmazie. Vol. 1,
Von den Anfängen bis zum Ausgang des Mittelalters . Eschborn; Govi-Verlag 1998: p 3 - 2 Cragg G M, Newman D J. Biodiversity: A continuing source of novel drug leads. Pure Appl Chem. 2005; 77 7-24
- 3 Newman D J, Cragg G M, Snader K M. Natural products as sources of new drugs over the period 1981 - 2002. J Nat Prod. 2003; 66 1022-37
- 4 Hadacek F. Secondary metabolites as plant traits: current assessment and future perspectives. CRC Crit Rev Plant Sci. 2002; 21 273-322
- 5 Lahana R. How many leads from HTS?. Drug Discov Today. 1999; 4 447-8
- 6 Gasteiger J, Teckentrup A, Terfloth L, Spycher S. Neural networks as data mining tools in drug design. J Phys Org Chem. 2003; 16 232-45
- 7 Chapman & Hall/CRC Press L LC. Dictionary of natural products. Available at http://www.chemnetbase.com. Accessed February 2006
- 8 Xu J, Hagler A. Chemoinformatics and drug discovery. Molecules. 2002; 7 566-600
- 9 Güner O F. Pharmacophore perception, development, and use in drug design. IUL Biotechnology Series. La Jolla; International University Line 2000
-
10 Böhm H -J, Schneider G, Kubinyi H, Mannhold R, Timmerman H. Virtual screening for bioactive molecules. Vol. 10, In: Mannhold R, Kubinyi H, Timmermann H, editors
Methods and principles in medicinal chemistry . New York; Wiley 2000 - 11 Abagyan R, Totrov M. High-throughput docking for lead generation. Curr Opin Chem Biol. 2001; 5 375-82
- 12 Schneider G, Böhm H J. Virtual screening and fast automated docking methods. Drug Discov Today. 2002; 7 64-70
- 13 Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H. et al . The protein data bank. Nucleic Acids Res. 2000; 28 235-42
- 14 Wolber G, Langer T. LigandScout: 3D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005; 45 160-9
- 15 Inte:Ligand G mbH. Vienna, Austria. LigandScout: 3D pharmacophore modelling tool. Available at http://www.inteligand.com. Accessed February 2006
- 16 Congreve M, Murray C W, Blundell T L. Structural biology and drug discovery. Drug Discov Today. 2005; 10 895-907
- 17 Rollinger J M, Hornick A, Langer T, Stuppner H, Prast H. Acetylcholinesterase inhibitory activity of scopolin and scopoletin discovered by virtual screening of natural products. J Med Chem. 2004; 47 6248-54
- 18 Nikolovska-Coleska Z, Xu L, Hu Z, Tomita Y, Li P, Roller P P. et al . Discovery of embelin as a cell-permeable, small-molecular weight inhibitor of XIAP through structure-based computational screening of a traditional herbal medicine three-dimensional structure database. J Med Chem. 2004; 47 2430-40
- 19 Wu G, Chai J, Suber T L, Wu J W, Du C, Wang X. et al . Structural basis of IAP recognition by Smac/DIABLO. Nature. 2000; 408 1008-12
- 20 Morris G M, Goodsell D S, Huey R, Olson A J. Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4 J Comput Aided Mol Des. 1996; 10 293-304
- 21 Langer T, Krovat E M. Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr Opin Drug Discov Dev. 2003; 6 370-6
- 22 Accelrys I nc., San D iego. CA, USA. Catalyst: programme for modelling, database management, and querying for drug discovery. Available at http://www.accelrys.com. Accessed January 2006
- 23 Laggner C, Schieferer C, Fiechtner B, Poles G, Hoffmann R D, Glossmann H. et al . Feature based pharmacophore models for sigma1 receptor, ERG2 and EBP. J Med Chem. 2005; 48 4754 -64
- 24 Zupan J, Gasteiger J. Neural networks in chemistry and drug design, 2nd edition. Weinheim; Wiley-VCH 1999
- 25 Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern. 1982; 43 59-69
- 26 Wagner S, Hofmann A, Siedle B, Terfloth L, Merfort I, Gasteiger J. Development of a structural model for NF-κB inhibition of sesquiterpene lactones using self-organizing neural networks. J Med Chem. 2006; 49 241-52
- 27 Bernard P, Berton J Y, Chrétien J R. Computer-aided molecular selection and design of natural bioactive molecules. Curr Opin Drug Discov Dev. 1999; 2 213-23
- 28 Kirchmair J, Laggner C, Wolber G, Langer T. Comparative analysis of protein-bound ligand conformations with respect to catalyst's conformational space subsampling algorithms. J Chem Inf Model. 2005; 45 422-30
- 29 Lu A, Liu B, Liu H, Zhou J, Xie G. A traditional Chinese medicine plant-compound database aid its application for searching. Int Electron J Mol Des. 2004; 3 672-83
- 30 Lei J, Zhou J. A marine natural product database. J Chem Inf Comput Sci. 2002; 42 742-8
- 31 Rollinger J M, Haupt S, Stuppner H, Langer T. Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example. J Chem Inf Comput Sci. 2004; 44 480-8
- 32 Rollinger J M, Bodensieck A, Seger C, Ellmerer E P, Bauer R, Langer T. et al . Discovering COX-inhibiting constituents of Morus root bark: activity-guided versus computer-aided methods. Planta Med. 2005; 71 399-405
- 33 Development Therapeutics Program N CI/NIH. Open NCI database. Available at http://dtp.nci.nih.gov/webdata.html. Accessed in March 2006
- 34 Poroikov V V, Filimonov D M, Ihlenfeldt W -D, Gloriozova T A, Lagunin A A, Borodina Y V. et al . PASS biological activity predictions in the enhanced open NCI database browser. J Chem Inf Comput Sci. 2003; 43 228-36
- 35 Cherkasov A, Shi Z, Fallahi M, Hammond G L. Successful in silico discovery of novel nonsteroidal ligands for human sex hormone binding globulin. J Med Chem. 2005; 48 3203-13
- 36 Van de Waterbeemd H. High-throughput and in silico techniques in drug metabolism and pharmacokinetics. Curr Opin Drug Discov Dev. 2002; 5 33-43
- 37 Bernard P, Scior T, Didier B, Hibert M, Berthon J -Y. Ethnopharmacology and bioinformatic combination for leads discovery: application to phospholipase A2 inhibitors. Phytochemistry. 2001; 58 865-74
- 38 Bernard P, Pintore M, Berthon J -Y, Chretien J R. A molecular modeling and 3D QSAR study of a large series of indole inhibitors of human non-pancreatic secretory phospholipase A2 . Eur J Med Chem. 2001; 36 1-19
- 39 Rollinger J M, Mock P, Zidorn C, Ellmerer E P, Langer T, Stuppner H. Application of the in combo screening approach for the discovery of non-alkaloid acetylcholinesterase inhibitors from Cichorium intybus . Curr Drug Discov Technol. 2005; 2 185-93. Corrections: Curr Drug Discov Technol 2006; in press
Mag. pharm. Dr. Judith Maria Rollinger
Institute of Pharmacy/Pharmacognosy
Leopold-Franzens-Universität Innsbruck
Innrain 52c
Josef-Moeller Haus
6020 Innsbruck
Austria
Phone: +43-512-507-5308
Fax: +43-512-507-2939
Email: judith.rollinger@uibk.ac.at