Abstract
Discovering small molecules that interact with protein targets identified by structural genomics, proteomics and bioinformatics will be a key part of future drug discovery efforts. Computational screening of drug-like molecules is likely to be valuable in this respect; however, the vast number of such molecules makes the potential size of this task enormous. Here, I describe how massively distributed computing using screensavers has allowed databases of billions of compounds to be screened against protein targets in a matter of days.
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References
Abagyan, R. & Totrov, M. High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 5, 375–382 (2001).
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. J. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1083–1092 (1953).
Kirkpatrick, S., Gelatt, C. D. Jr & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).
Goodsell, D. S. & Olson, A. J. Automated docking of substrates to proteins by simulated annealing. Proteins 8, 195–202 (1990).
Osterberg, F., Morris, G. M., Sanner, M. F., Olson, A. J. & Goodsell, D. S. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46, 34–40 (2002).
Jones, G., Willett, P. & Glen, R. C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 254, 43–53 (1995).
Jones, G., Willett, P., Glen, R. C., Leach, A. R. & Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267, 727–748 (1997).
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. 10, 293–304 (1996).
Morris, G. M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998).
Miranker, A. & Karplus, M. Functionality maps of binding sites — a multiple copy simultaneous search method. Proteins 11, 29–34 (1991).
Meng, E. C., Shoichet, B. K. & Kuntz, I. D. Automated docking with grid-based energy evaluation. J. Comput. Chem. 13, 505–524 (1992).
Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R. & Ferrin, T. E. A geometric approach to macromolecule– ligand interactions. J. Mol. Biol. 161, 269–288 (1982).
Shoichet, B. K. & Kuntz, I. D. Matching chemistry and shape in molecular docking. Protein Eng. 6, 723–732 (1993).
Miller, M. D., Kearsley, S. K., Underwood, D. J. & Sheridan, R. P. FLOG — a system to select quasi-flexible ligands complementary to a receptor of known three-dimensional structure. J. Comput. Aided Mol. Des. 8, 153–174 (1994).
Rarey, M., Kramer, B., Lengauer, T. & Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261, 470–479 (1996).
Dolle, R. E. Comprehensive survey of combinatorial library synthesis: 2000. J. Comb. Chem. 3, 477–517 (2001).
Dolle, R. E. Comprehensive survey of combinatorial library synthesis: 1999. J. Comb. Chem. 2, 383–433 (2000).
Dolle, R. E. & Nelson, K. H. Jr. Comprehensive survey of combinatorial library synthesis: 1998. J. Comb. Chem. 1, 235–282 (1999).
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).
Clark, D. E. & Pickett, S. D. Computational methods for the prediction of 'drug-likeness'. Drug Discov. Today 5, 58–59 (2000).
Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).
Boobbyer, D. N. A., Goodford, P. J., McWhinnie, P. M. & Wade, R. C. New hydrogen-bond potentials for use in determining energetically favorable binding sites on molecules of known structure. J. Med. Chem. 32, 1083–1094 (1989).
Wade, R. C., Clark, K. J. & Goodford, P. J. Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 1. Ligand probe groups with the ability to form two hydrogen bonds. J. Med. Chem. 36, 140–147 (1993).
Wade, R. C. & Goodford, P. J. Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 2. Ligand probe groups with the ability to form more than two hydrogen bonds. J. Med. Chem. 36, 148–156 (1993).
Glick, M., Robinson, D. D., Grant, G. H. & Richards, W. G. Identification of ligand binding sites on proteins using a multi-scale approach. J. Am. Chem. Soc. 124, 2337–2344 (2002).
Glick, M., Grant, G. H. & Richards, W. G. Pinpointing anthrax toxin inhibitors. Nature Biotechnol. 20, 118–119 (2002).
Leppla, S. H. in Comprehensive Sourcebook of Bacterial Protein Toxins 2nd edn (eds Alouf, J. A. & Freer, J.) 243–263 (Academic, London, 1999).
Dixon, T. C., Meselson, M., Guillemin, J. & Hanna, P. C. Medical progress: anthrax. N. Engl. J. Med. 341, 815–862 (1999).
Pezard, C., Berche, P. & Mock, M. Contribution of individual toxin components to virulence of Bacillus anthracis. Infect. Immun. 59, 3472–3477 (1991).
Mourez, M. et al. Designing a polyvalent inhibitor of anthrax toxin. Nature Biotechnol. 19, 958–962 (2001).
Petosa, C., Collier, R. J., Klimpel, K. R., Leppla, S. H. & Liddington, R. C. Crystal structure of the anthrax toxin protective antigen. Nature 385, 833–838 (1997).
Acknowledgements
This work has been supported in part by the National Foundation for Cancer Research and The Wellcome Trust.
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Richards, W. Virtual screening using grid computing: the screensaver project. Nat Rev Drug Discov 1, 551–555 (2002). https://doi.org/10.1038/nrd841
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DOI: https://doi.org/10.1038/nrd841
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