Knowledge of the three-dimensional structures of target proteins provides a starting point for structure-based approaches to drug design by defining the topographies of the complementary surfaces of ligands and their protein targets.
X-ray crystallography is the most widely used technique for protein structure determination, but technical challenges and time constraints have traditionally limited its use primarily to lead optimization.
Recent advances in molecular biology, biochemistry, crystallography, chemoinformatics and bioinformatics have faciliated the development of high-throughput X-ray crystallography. Consequently, the use of crystallographic techniques is now being extended beyond structure determination, into new approaches for lead discovery.
Once the structure of the target has been solved, virtual screening, coupled with high-throughput X-ray crystallography, can be used to identify one or more weakly binding small-molecule fragments from compound libraries that consist of hundreds of small-molecule fragments.
High-resolution definition of these binding interactions provides an information-rich starting point for medicinal chemistry. X-ray crystallography can then be used to rapidly guide the elaboration of the fragments into larger molecular-weight lead compounds.
Knowledge of the three-dimensional structures of protein targets now emerging from genomic data has the potential to accelerate drug discovery greatly. X-ray crystallography is the most widely used technique for protein structure determination, but technical challenges and time constraints have traditionally limited its use primarily to lead optimization. Here, we describe how significant advances in process automation and informatics have aided the development of high-throughput X-ray crystallography, and discuss the use of this technique for structure-based lead discovery.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Molecular Biomedicine Open Access 22 December 2022
Communications Biology Open Access 31 July 2020
In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Scientific Reports Open Access 02 May 2019
Subscribe to Journal
Get full journal access for 1 year
only $6.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Blundell, T. L. & Mizuguchi, K. Structural genomics: an overview. Prog. Biophys. Mol. Biol. 73, 289–295 (2000).
Campbell, S. F. Science, art and drug discovery: a personal perspective. Clin. Sci. 99, 255–260 (2000).
Whittle, P. J. & Blundell, T. L. Protein structure-based drug design. Annu. Rev. Biophys. Biomol. Struct. 23, 349–375 (1994).A discussion of structure-based lead optimization, describing approaches developed over the previous decade and some of the successes.
Blundell, T. L. Structure-based drug design. Nature 384, S23–S26 (1996).
Greer, J., Erickson, J. W., Baldwin, J. J. & Varney, M. D. Application of the three-dimensional structures of protein target molecules in structure-based drug design. J. Med. Chem. 37, 1035–1054 (1994).
Toh, H., Ono, M., Saogo, K. & Miyata, T. Retroviral protease-like sequence in the yeast transposon TY1. Nature 315, 691 (1985).
Blundell, T. L. et al. Knowledge-based protein modelling and design; 18th Sir Hans Krebs Lecture. Eur. J. Biochem. 172, 513–520 (1988).
Pearl, L. H., & Taylor, W. R. A structural model for the retroviral proteases. Nature 329, 351–354 (1987).
Varghese, J. N. Development of neuraminidase inhibitors as anti-influenza virus drugs. Drug Dev. Res. 46, 176–196 (1999).
Schindler, T. et al. Structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science 289, 1938–1942 (2000).
Gray, N. S. et al. Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors. Science 281, 533–538 (1998).Iterative chemical synthesis and biological screening of 2,6,9-tri-substituted purines are used to develop potent inhibitors of the human CDK2. The structural bases for the binding affinity and selectivity are determined by analyses of the crystal structure of a CDK2–inhibitor complex, and the cellular effects are characterized in yeast by monitoring changes in messenger RNA levels using high-density DNA arrays.
Tan, D. S., Foley, M. A., Shair, M. D. & Schreiber, S. L. Stereoselective synthesis of over two million compounds having structural features both reminiscent of natural products and compatible with miniaturized cell-based assays. J. Am. Chem. Soc. 120, 8565–8566 (1998).
Keating, T. A. & Armstrong, R. W. Molecular diversity by a convertible isocyanide in the Ugi 4-component condensation. J. Am. Chem. Soc. 117, 7842–7843 (1995).
Nicolaou, K. C. et al. Solid and solution phase synthesis and biological evaluation of combinatorial sarcodictyin libraries. J. Am. Chem. Soc. 120, 10814–10826 (1998).
Leach, A. R. & Hann, M. M. The in silico world of virtual libraries. Drug Discov. Today 5, 326–336 (2000).
Moy, F. J. et al. MS/NMR: a structure-based approach for discovering protein ligands and for drug design by coupling size exclusion chromatography, mass spectrometry, and nuclear magnetic resonance spectroscopy. Anal. Chem. 73, 571–581 (2001).
Myszka, D. G. & Rich, R. L. Implementing surface plasmon resonance biosensors in drug discovery. Pharm. Sci. Tech. Today 3, 310–317 (2000).
Hajduk, P. J., Bures, M., Praestgaard, J. & Fesik, S. W. Privileged molecules for protein binding identified from NMR-based screening. J. Med. Chem. 43, 3443–3447 (2000).
Fejzo, J. et al. The SHAPES strategy: an NMR-based approach for lead generation in drug discovery. Chem. Biol. 6, 755–769 (1999).
Rigler, R. Fluorescence correlations, single molecule detection and large number screening — applications in biotechnology. J. Biotechnol. 41, 177–186 (1995).
Nienaber, V. L. et al. Discovering novel ligands for macromolecules using X-ray crystallographic screening. Nature Biotechnol. 18, 1105–1108 (2000).Screening techniques that are driven by X-ray crystallography are able to combine lead identification, structural assessment and lead optimization. A method is described that is rapid, efficient and high throughput, and which results in detailed crystallographic structural information. The utility of the method is shown by the discovery and optimization of a new class of urokinase inhibitors for the treatment of cancer.
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Heinemann, U., Illing, G., & Oschkinat, H. High throughput three-dimensional protein structure determination. Curr. Opin. Biotechnol. 12, 348–354 (2001).
Burke, D. F. et al. An iterative structure-assisted approach to sequence alignment and comparative modeling. Proteins Struct. Funct. Genet. 3, 1–6 (1999).
Longenecker, K. L., Garrard, S. M., Sheffield, P. J. & Derewenda, Z. S. Protein crystallisation by the rational mutagenesis of surface residues: Lys to Ala mutations promote the crystallisation of RhoGD1 Acta Crystallogr. D 57, 679–688 (2001).
Lesley, S. A. High throughput proteomics: protein expression and purification in the post-genomic world. Protein Exp. Purif. 22, 159–164 (2001).The application of high-throughput screening technologies is the most appropriate response to the challenge of parallel expression and purification of large numbers of gene products.
Waldo, G. S., Standish, B. M., Berendzen, J. & Terwilliger, T. C. Rapid protein folding assay using green fluorescent protein. Nature Biotechnol. 17, 691–695 (1999).
Kigawa, T. et al. Bacterial cell free systems: Based on E. coli S30 extract used for NMR 13C and 15N labelled proteins. Cell free production and stable isotope labelling of milligram quantities of proteins. FEBS Lett. 442, 15–19 (1999).
Crowe, J. et al. 6xHis-Ni-NTA chromatography as a superior technique in recombinant protein expression/purification. Methods. Mol. Biol. 31, 371–387 (1994).
Stevens, R. C. High-throughput protein crystallization. Curr. Opin. Struct. Biol. 10, 558–563 (2000).High-throughput crystallization of proteins has been advanced by exploiting techniques developed for the combinatorial chemistry industry, including robust liquid systems for handling and mixing small volumes. These are assisted by the availability of intense synchrotron X-ray sources, with improved beam-line optics, which are suitable for studying micrometre-sized crystals.
Mueller, U. et al. Development for automation and miniaturisation of protein crystallisation. J. Biotechnol. 85, 7–14 (2001).
Asanov, A. N., McDonald, H. M., Oldham, P. B., Jedrezejas, M. J. & Wilson, W. W. Intrinsic fluorescence as a rapid scoring tool for protein crystals. J. Cryst. Growth 232, 603–609 (2001).
Abola, E., Kuhn, P., Earnest, T. & Stevens, R. C. Automation of X-ray crystallisation. Nature Struct. Biol. 7, 973–977 (2000).
Muchmore, S. W. et al. Automated crystal mounting and data collection in protein crystallography. Structure 8, R243–R246 (2000).
Lamzin, V. S. & Perrakis, A. Current state of automated crystallographic analysis. Nature Struct. Biol. 7, 978–981 (2000).A goal of structural biology is to improve the underlying methodology of high-throughput determination of three-dimensional structures of biological macromolecules. This will be achieved by development, automation and streamlining of the process of X-ray-crystal structure solution.
Kuhn, P. & Soltis, S. M. Macromolecular structure determination in the post genomic era. Nucl. Instrum. Methods Phys. Res. A 467, 1363–1366 (2001).
Hendickson, W. A. & Ogata, C. M. Phase determination from multiwavelength anomalous diffraction measurements. Methods Enzymol. 276, 494–523 (1997).
Dauter, Z., Li, M., & Wlodawer, A. Practical experience with the use of halides in phasing macromolecular structures: a powerful tool for structural genomics. Acta Crystallogr. D 57, 239–249 (2001).
Sheldrick, G. M. Patterson superposition and ab initio phasing. Methods Enzymol. 276, 628–641 (1997).
Weeks, C. M. & Miller, R. Optimizing shake and bake for proteins. Acta Crystallogr. D 55, 492–500 (1999).
Terwilliger, T. C. & Berendzen, J. Automated MAD and MIR structure solution. Acta Crystallogr. D 55, 849–861 (1999).
De La Fortelle, E. & Bricogne, G. Maximum likelihood heavy-atom parameter refinement for the MIR and MAD methods. Methods Enzymol. 276, 590–620 (1997).
Kissinger, C. R., Gehlhaar, D. K. & Fogel, D. B. Rapid automated molecular replacement by evolutionary search. Acta Crystallogr. 55, 484–491 (1999).
Perrakis, A., Morris, R. & Lamzin, V. S. Automated protein model building combined with iterative structure refinement. Nature Struct. Biol. 6, 458–463 (1999).
Johnson, M. S., Srinivasan, N., Sowdhamini, R. & Blundell, T. L. Knowledge-based protein modeling. Crit. Rev. Biochem. Mol. Biol. 29, 1–70 (1994).
Jones, D. T. GenTHREADER: an efficient and reliable protein fold recognition for genomic sequences. J. Mol. Biol. 287, 797–815 (1999).
Shi, J., Blundell, T. L. & Mizuguchi, K. FUGUE: Sequence–structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310, 243–257 (2001).
Sali, A. & Blundell, T. L. Comparative modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
Leach, A. R. & Kuntz, I. D. Conformational analysis of flexible ligands in macromolecular receptor sites. J. Comput. Chem. 13, 730–748 (1992).
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 Dev. Rev. 23, 3–25 (1997).
Abagyan, R. & Totrov, M. High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 5, 375–382 (2001).Recent improvements in flexible ligand-docking technology are leading to a more central role for computational methods in lead discovery. Docking and screening procedures can select small sets of likely candidates from large libraries of either commercially or synthetically available compounds.
Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).
Abagyan, R., Totrov, M. & Kuznetsov, D. A. ICM: a new method for structure modelling and design. J. Comput. Chem. 15, 488–506 (1994).
Trosset, J. Y. & Scheraga, H. A. Reaching the global minimum in docking simulations: a Monte Carlo energy minimisation approach using Bezier splines. Proc. Natl Acad. Sci. USA 95, 8011–8015 (1998).
Schapira, M., Raaka, B. M., Samuels, H. H. & Abagyan, R. Rational discovery of novel nuclear hormone receptor antagonists. Proc. Natl Acad. Sci. USA 97, 1008–1013 (2000).
Payne, A. W. R. & Glen, R. C. Molecular recognition using a binary genetic search algorithm. J. Mol. Graph. 11, 74–91 (1993).
Lewis, R. A. Automated site-directed drug design: a method for the generation of general three-dimensional molecular graphs. J. Mol. Graph. 10, 131–143 (1992).
Cohen, N. C. & Tschinke, N. Generation of new-lead structures in computer-aided drug design. Prog. Drug Res. 45, 205–243 (1995).
Bohacek, R. S. & McMartin, C. 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, 5560–5565 (1994).
Rotstein, S. H. & Murcko, M. A. GenStar: a method for de novo drug design. J. Comp. Aided Molec. Design 7, 23–43 (1993).
Miranker, A., & Karplus, M. Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11, 29–34 (1991).
Bohm, H. J. LUDI-ruled-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. -Aided Mol. Design 6, 593–606 (1992).
Rusinko, A. Using CONCORD to construct a large database of three-dimensional coordinates from connection tables. J. Chem. Inf. Comput. Sci. 29, 327–333 (1989).
Muegge, I., Martin, Y. C., Hajduk, P. J. & Fesik, S. W. Evaluation of PMF scoring in docking weak ligands of the FK506 binding protein. J. Med. Chem. 42, 2498–2503 (1999).
Gohlke, H., Hendlich, M. & Klebe, G. Knowledge-based scoring function to predict protein–ligand interactions. J. Mol. Biol. 295, 337–356 (2000).
Bissantz, C., Folkers, G. & Rognan, D. Protein-based virtual screening of chemical databases. J. Med. Chem. 43, 4759–4767 (2000).
Shuker, S. B., Hajduk, P. J., Meadows, R. P. & Fesik, S. W. Discovery of high affinity ligands for proteins: SAR by NMR. Science 274, 1531–1534 (1996).
Stout, T. J., Sage, C. R. & Stroud, R. M. The additivity of substrate fragments in enzyme-ligand binding. Structure 6, 839–848 (1998).
Verlinde, C. L. M. J., Kim, H., Bernstein, B. E., Mande, S. C. & Hol, W. G. J. in Structure-based Drug Design (ed. Veerapandian, P.) 365–394 (Marcel Dekker, New York, 1997).
Blundell, T. L. et al. High-throughput X–ray Crystallography for Drug Discovery (ed. Flower, D.) (Royal Soc. Chem., London, in the press).
Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).
Drews, J. Drug discovery: a historical perspective. Science 287, 1960–1964 (2000).
International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
Pellegrini, L., Burke, D. F., von Delft, F., Mulloy, B. & Blundell, T. L. Crystal structure of fibroblast growth factor receptor ectodomain bound to ligand and heparin. Nature 407, 1029–1034 (2000).
Kim, E. E. et al. Crystal-structure of HIV-1 protease in complex with VX-478, a potent and orally bioavailable inhibitor of the enzyme. J. Am. Chem. Soc. 117, 1181–1182 (1995).
We would like to acknowledge the help of A. Cleasby, M. Hartshorn, E. Southern, I. Tickle, A. Scharff, M. Verdonk, J. Yon and N. Wallace in the preparation of this manuscript.
- CPK COLOURING
The CPK colour scheme for elements is based on the colours of the popular plastic space-filling models developed by Corey, Pauling and Kultun, and is conventionally used by chemists. In this scheme, carbon is represented in light grey, oxygen in red, nitrogen in blue and sulphur in yellow.
A synchrotron accelerates charged particles in a circular orbit. This produces very intense X-rays, which allows the use of smaller and more easily obtained crystals than can be used with conventional X-ray crystallography, and also boosts relevant signals while minimizing noise. The wavelength of synchrotron X-radiation can be varied to perform multiwavelength anomalous diffraction (MAD) experiments.
- GEL ELECTROPHORESIS
A method that separates macromolecules on the basis of size, electric charge and other physical properties.
- INCLUSION BODIES
Protein overexpression often leads to the production of insoluble aggregates of misfolded protein, which are known as inclusion bodies.
- GREEN FLUORESCENT PROTEIN
Autofluorescent protein originally identified in the jellyfish Aequorea victoria.
Measure of the degree of order of a crystal. Lower mosaicity indicates better-ordered crystals and hence better diffraction.
- STRUCTURE-FACTOR AMPLITUDES
Structure factors are related to the electron density by a mathematical operation called a Fourier transform. Structure-factor amplitudes are determinable from the measured intensities in an X-ray diffraction experiment, but the phases of the diffracted beams, which are needed to reconstitute the electron density, cannot be determined directly.
- VAN DER WAALS SURFACE
The van der Waals radius is that which defines the normal contact distance with another non-covalently bound atom. The van der Waals surface is defined by the radii of all such atoms in the molecule.
- sp3 CARBON
An sp3 carbon has four substituents.
The ensemble of steric and electronic features that is necessary to ensure optimal interactions with a specific biological target structure and to trigger (or to block) its biological response.
About this article
Cite this article
Blundell, T., Jhoti, H. & Abell, C. High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1, 45–54 (2002). https://doi.org/10.1038/nrd706
This article is cited by
Molecular Biomedicine (2022)
Biomass Conversion and Biorefinery (2021)
Communications Biology (2020)
Radical scavenging and antiproliferative effect of novel phenolic derivatives isolated from Nerium indicum against human breast cancer cell line (MCF-7)—an in silico and in vitro approach
Environmental Science and Pollution Research (2020)
In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Scientific Reports (2019)