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High-throughput crystallography for lead discovery in drug design

Key Points

  • 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.

Abstract

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.

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Figure 1: Examples of structure-based drug design.
Figure 2: Close-up of the three-dimensional structure of the active site of renin in complex with a peptidic inhibitor.
Figure 3: Fragment-based screening.
Figure 4: Examples of small-molecule fragments bound into a pocket of trypsin.
Figure 5: Structural screening.
Figure 6: Targeting protein–protein interactions.

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References

  1. Blundell, T. L. & Mizuguchi, K. Structural genomics: an overview. Prog. Biophys. Mol. Biol. 73, 289–295 (2000).

    Article  CAS  Google Scholar 

  2. Campbell, S. F. Science, art and drug discovery: a personal perspective. Clin. Sci. 99, 255–260 (2000).

    Article  CAS  Google Scholar 

  3. 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.

    Article  CAS  Google Scholar 

  4. Blundell, T. L. Structure-based drug design. Nature 384, S23–S26 (1996).

    Article  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. Toh, H., Ono, M., Saogo, K. & Miyata, T. Retroviral protease-like sequence in the yeast transposon TY1. Nature 315, 691 (1985).

    Article  CAS  Google Scholar 

  7. Blundell, T. L. et al. Knowledge-based protein modelling and design; 18th Sir Hans Krebs Lecture. Eur. J. Biochem. 172, 513–520 (1988).

    Article  CAS  Google Scholar 

  8. Pearl, L. H., & Taylor, W. R. A structural model for the retroviral proteases. Nature 329, 351–354 (1987).

    Article  CAS  Google Scholar 

  9. Varghese, J. N. Development of neuraminidase inhibitors as anti-influenza virus drugs. Drug Dev. Res. 46, 176–196 (1999).

    Article  CAS  Google Scholar 

  10. Schindler, T. et al. Structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science 289, 1938–1942 (2000).

    Article  CAS  Google Scholar 

  11. 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.

    Article  CAS  Google Scholar 

  12. 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).

    Article  CAS  Google Scholar 

  13. 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).

    Article  CAS  Google Scholar 

  14. 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).

    Article  CAS  Google Scholar 

  15. Leach, A. R. & Hann, M. M. The in silico world of virtual libraries. Drug Discov. Today 5, 326–336 (2000).

    Article  CAS  Google Scholar 

  16. 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).

    Article  CAS  Google Scholar 

  17. Myszka, D. G. & Rich, R. L. Implementing surface plasmon resonance biosensors in drug discovery. Pharm. Sci. Tech. Today 3, 310–317 (2000).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  Google Scholar 

  19. Fejzo, J. et al. The SHAPES strategy: an NMR-based approach for lead generation in drug discovery. Chem. Biol. 6, 755–769 (1999).

    Article  CAS  Google Scholar 

  20. Rigler, R. Fluorescence correlations, single molecule detection and large number screening — applications in biotechnology. J. Biotechnol. 41, 177–186 (1995).

    Article  CAS  Google Scholar 

  21. 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.

    Article  CAS  Google Scholar 

  22. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  Google Scholar 

  23. Heinemann, U., Illing, G., & Oschkinat, H. High throughput three-dimensional protein structure determination. Curr. Opin. Biotechnol. 12, 348–354 (2001).

    Article  CAS  Google Scholar 

  24. Burke, D. F. et al. An iterative structure-assisted approach to sequence alignment and comparative modeling. Proteins Struct. Funct. Genet. 3, 1–6 (1999).

    Google Scholar 

  25. 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).

    Article  CAS  Google Scholar 

  26. 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.

    Article  CAS  Google Scholar 

  27. 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).

    Article  CAS  Google Scholar 

  28. 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).

    Article  CAS  Google Scholar 

  29. Crowe, J. et al. 6xHis-Ni-NTA chromatography as a superior technique in recombinant protein expression/purification. Methods. Mol. Biol. 31, 371–387 (1994).

    CAS  PubMed  Google Scholar 

  30. 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.

    Article  CAS  Google Scholar 

  31. Mueller, U. et al. Development for automation and miniaturisation of protein crystallisation. J. Biotechnol. 85, 7–14 (2001).

    Article  CAS  Google Scholar 

  32. 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).

    Article  CAS  Google Scholar 

  33. Abola, E., Kuhn, P., Earnest, T. & Stevens, R. C. Automation of X-ray crystallisation. Nature Struct. Biol. 7, 973–977 (2000).

    Article  CAS  Google Scholar 

  34. Muchmore, S. W. et al. Automated crystal mounting and data collection in protein crystallography. Structure 8, R243–R246 (2000).

    Article  CAS  Google Scholar 

  35. 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.

    Article  CAS  Google Scholar 

  36. Kuhn, P. & Soltis, S. M. Macromolecular structure determination in the post genomic era. Nucl. Instrum. Methods Phys. Res. A 467, 1363–1366 (2001).

    Article  Google Scholar 

  37. Hendickson, W. A. & Ogata, C. M. Phase determination from multiwavelength anomalous diffraction measurements. Methods Enzymol. 276, 494–523 (1997).

    Article  Google Scholar 

  38. 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).

    Article  CAS  Google Scholar 

  39. Sheldrick, G. M. Patterson superposition and ab initio phasing. Methods Enzymol. 276, 628–641 (1997).

    Article  CAS  Google Scholar 

  40. Weeks, C. M. & Miller, R. Optimizing shake and bake for proteins. Acta Crystallogr. D 55, 492–500 (1999).

    Article  CAS  Google Scholar 

  41. Terwilliger, T. C. & Berendzen, J. Automated MAD and MIR structure solution. Acta Crystallogr. D 55, 849–861 (1999).

    Article  CAS  Google Scholar 

  42. De La Fortelle, E. & Bricogne, G. Maximum likelihood heavy-atom parameter refinement for the MIR and MAD methods. Methods Enzymol. 276, 590–620 (1997).

    Google Scholar 

  43. Kissinger, C. R., Gehlhaar, D. K. & Fogel, D. B. Rapid automated molecular replacement by evolutionary search. Acta Crystallogr. 55, 484–491 (1999).

    Article  CAS  Google Scholar 

  44. Perrakis, A., Morris, R. & Lamzin, V. S. Automated protein model building combined with iterative structure refinement. Nature Struct. Biol. 6, 458–463 (1999).

    Article  CAS  Google Scholar 

  45. Johnson, M. S., Srinivasan, N., Sowdhamini, R. & Blundell, T. L. Knowledge-based protein modeling. Crit. Rev. Biochem. Mol. Biol. 29, 1–70 (1994).

    Article  CAS  Google Scholar 

  46. Jones, D. T. GenTHREADER: an efficient and reliable protein fold recognition for genomic sequences. J. Mol. Biol. 287, 797–815 (1999).

    Article  CAS  Google Scholar 

  47. 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).

    Article  CAS  Google Scholar 

  48. Sali, A. & Blundell, T. L. Comparative modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    Article  CAS  Google Scholar 

  49. Leach, A. R. & Kuntz, I. D. Conformational analysis of flexible ligands in macromolecular receptor sites. J. Comput. Chem. 13, 730–748 (1992).

    Article  CAS  Google Scholar 

  50. 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).

    Article  CAS  Google Scholar 

  51. 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.

    Article  CAS  Google Scholar 

  52. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).

    Article  CAS  Google Scholar 

  53. Abagyan, R., Totrov, M. & Kuznetsov, D. A. ICM: a new method for structure modelling and design. J. Comput. Chem. 15, 488–506 (1994).

    Article  CAS  Google Scholar 

  54. 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).

    Article  CAS  Google Scholar 

  55. 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).

    Article  CAS  Google Scholar 

  56. Payne, A. W. R. & Glen, R. C. Molecular recognition using a binary genetic search algorithm. J. Mol. Graph. 11, 74–91 (1993).

    Article  CAS  Google Scholar 

  57. 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).

    Article  CAS  Google Scholar 

  58. Cohen, N. C. & Tschinke, N. Generation of new-lead structures in computer-aided drug design. Prog. Drug Res. 45, 205–243 (1995).

    CAS  PubMed  Google Scholar 

  59. 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).

    Article  CAS  Google Scholar 

  60. Rotstein, S. H. & Murcko, M. A. GenStar: a method for de novo drug design. J. Comp. Aided Molec. Design 7, 23–43 (1993).

    Article  CAS  Google Scholar 

  61. Miranker, A., & Karplus, M. Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11, 29–34 (1991).

    Article  CAS  Google Scholar 

  62. Bohm, H. J. LUDI-ruled-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. -Aided Mol. Design 6, 593–606 (1992).

    Article  CAS  Google Scholar 

  63. 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).

    Article  Google Scholar 

  64. 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).

    Article  CAS  Google Scholar 

  65. Gohlke, H., Hendlich, M. & Klebe, G. Knowledge-based scoring function to predict protein–ligand interactions. J. Mol. Biol. 295, 337–356 (2000).

    Article  CAS  Google Scholar 

  66. Bissantz, C., Folkers, G. & Rognan, D. Protein-based virtual screening of chemical databases. J. Med. Chem. 43, 4759–4767 (2000).

    Article  CAS  Google Scholar 

  67. 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).

    Article  CAS  Google Scholar 

  68. Stout, T. J., Sage, C. R. & Stroud, R. M. The additivity of substrate fragments in enzyme-ligand binding. Structure 6, 839–848 (1998).

    Article  CAS  Google Scholar 

  69. 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).

    Google Scholar 

  70. Blundell, T. L. et al. High-throughput X–ray Crystallography for Drug Discovery (ed. Flower, D.) (Royal Soc. Chem., London, in the press).

  71. Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).

    Article  CAS  Google Scholar 

  72. Drews, J. Drug discovery: a historical perspective. Science 287, 1960–1964 (2000).

    Article  CAS  Google Scholar 

  73. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  74. 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).

    Article  CAS  Google Scholar 

  75. 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).

    Article  CAS  Google Scholar 

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Acknowledgements

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.

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Correspondence to Tom L. Blundell.

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DATABASES

Medscape DrugInfo

amprenavir

imatinib

nelfinavir

zanamivir

Protein Data Bank

c-ABL

FGF1–FGFR2–heparin

HIV protease

neuraminidase

pepsin

renin

SWISS-PROT

GFP

FURTHER INFORMATION

Cambridge Crystallographic Data Centre

Stanford Synchrotron Radiation Laboratory

Astex Technology Ltd

Integrative Proteomics, Inc.

Plexxikon, Inc.

Structural GenomiX, Inc.

Syrrx, Inc.

TRIAD Therapeutics

Accelrys

Nature

Glossary

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.

SYNCHROTRON

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.

MOSAICITY

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.

PHARMACOPHORE

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.

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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

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