The Role of Medical Structural Genomics in Discovering New Drugs for Infectious Diseases

The Role of Medical Structural Genomics in Discovering New Drugs for Infectious Diseases

  • Wesley C. Van Voorhis, 
  • Wim G. J. Hol, 
  • Peter J. Myler, 
  • Lance J. Stewart
  • Published: October 26, 2009
  • DOI: 10.1371/journal.pcbi.1000530


Whether we think of Alzheimer's disease, microbial infection, or any other modern-day disease, new medicines are urgently needed. The number of new drugs registered since the advent of genomics, however, has not lived up to expectations. One recent review revealed that over 70 high-throughput biochemical screens against genetically validated drug targets in bacteria failed to yield a single candidate that could be tested in the clinic [1]. The reasons for the failure of high-throughput biochemical screens are not completely clear, but it could reflect the limited diversity of chemical libraries used and/or the absence of structural information for many of the targets. Indeed, structure-based drug design is playing a growing role in modern drug discovery, with numerous approved drugs tracing their origins, at least in part, to the use of structural information from X-ray crystallography or nuclear magnetic resonance (NMR) analysis of protein targets and their ligand-bound complexes. Although it is beyond the scope of this brief overview to present a comprehensive list of structures that have led to useful drugs, Table 1 lists some examples in which protein structure information has provided insights to the design and development of new therapeutic entities. These cases include both novel drug design based on native and ligand-bound structures and optimization of inhibitors based on the binding mode revealed by the structures of inhibitor–target complexes. These approaches have allowed increased affinity for the target and/or improvement of pharmacological properties while maintaining target affinity.

Table 1. Examples of how target protein structure can assist drug discovery and development.


With the increasing availability of complete human and pathogen genome sequences and the substantial progress in structure determination methods, it is no surprise that the field of “structural genomics” has emerged recently. Its aim is to solve as many useful protein structures as possible from the entire genome of a single organism or group of related organisms. Over the past ten years, over 20 structural genomics initiatives have begun around the world (Table 2). The impact of these efforts on structural biology has been substantial, both in the sheer number of new structures and, perhaps even more importantly, in the development of new methodologies, especially the use of robotics and informatics to generate and capture data in a systematic way [2]. Over the next five years, thousands of new protein structures, many bound to their ligands, will be elucidated; laying the groundwork for structure-based design and development of new and improved chemotherapeutic agents against pathogen proteins. Here, we will focus on the intersection of structural biology with chemistry and biology—a field called “medical structural genomics”—particularly on how the structures of medically relevant drug targets in pathogens can serve as a starting point for inhibitor design and drug development. We argue that the pharmaceutical industry should be persuaded to complement the publicly funded structural genomics initiatives by making public the structural coordinates of their drug targets for important infectious disease organisms in a timely fashion and by developing public–private partnerships to provide the maximal synergy between target validation, structure determination, and hit-to-lead development.

Table 2. Structural genomics projects worldwide submitting to the Protein Data Bank.


Target Selection

A prerequisite of medical structural genomics is that the proteins whose structures are determined must be well-validated as good drug targets. The term “drugability” is often used to loosely describe how tractable any given target is for the development of a drug candidate. For infectious organisms, one key factor in defining drugability is that the target protein be essential for survival of the microbe. While essentiality has traditionally been defined using techniques such as “gene knockout” and RNA interference, these are not always feasible and should be complemented by chemical biology approaches (see below). Furthermore, the meaningfulness of these experiments can often be difficult to assess, since the interplay of host and pathogen is complex and full of surprises. For example, tremendous effort has been devoted recently to the development of antagonists for targets in the fatty acid biosynthesis pathway of bacteria [3]. Potent drug-like molecules with high bioavailability have been developed that can effectively shut down bacterial replication in vitro. These compounds were found to be ineffective in subsequent animal testing, however, because fatty acids are quite abundant in vertebrates, so bacteria can secure these host molecules for their survival and growth even if their own fatty acid biosynthesis pathways are blocked [4]. Thus, to improve target selection for medical structural genomics, it will be important to collaborate with chemical biology groups to undertake screening campaigns to identify compounds that cause the death of a pathogen under the appropriate assay conditions [5].

If the target protein of a drug is known, medical structural genomics offers a rapid and efficient way to obtain ligand-bound structures by using high-throughput X-ray crystallography and/or NMR. Conversely, when the target of a cell-active compound is unknown, medical structural genomics efforts provide purified protein for many potential drug targets that can be screened for interaction with the active compound by a number of biophysical methods (such as thermal stability [6]). The Medicinal Structural Genomics of Protozoan Pathogens (MSGPP, initiative has already begun such an effort by screening thousands of anti-malaria compounds against 67 potential Plasmodium falciparum targets expressed in bacteria (WC Van Voorhis, unpublished data). These approaches aim to generate knowledge about the biological effect of a small molecule on a target protein. Follow-up experiments are then needed to test the activity of this compound in live organisms in order to validate the target; this valuable “chemical validation” makes the target much more likely to be drugable, and thus worthy of more intensive effort. The future will likely see more medical structural genomics centers working with chemical biology groups that have collections of “phenotype-defined” compounds (i.e., those with known anti-pathogen activity). The result will be synergistic target validation and hit-to-lead development using structure-based drug design.

Fragment-Based Drug Discovery

Fragment-based drug discovery has rapidly gained interest within the pharmaceutical industry (reviewed in [7] with roots of 128-compound cocktails in [8]), as an alternative to expensive and sometimes inefficient high-throughput screening methods for hit identification and optimization [9]. The general concept of fragment-based drug discovery involves screening libraries of “rule-of-three” compounds [10] against target macromolecules by using a variety of methods including X-ray crystallography, NMR, surface plasmon resonance, differential thermal denaturation, fluorescence polarization, and other techniques [7], [11][14]. The rule of three consists of molecular weight <300 daltons, ≤3 rotatable bonds, ≤3 hydrogen bond donors/acceptors, and Clog P (calculated log of octanol/water partition coefficient) <3. These compounds generally include fragments or “building blocks” of available drugs, on the assumption that these fragments are more likely to be “drug-like.” Fragment-based drug discovery has been used by commercial and academic groups, including our own, and has led to a number of leads for further drug development [15]. At deCODE biostructures, a partner in the Seattle Structural Genomics Center for Infectious Disease (SSGCID, consortium, the approach to assembling a fragment library has been somewhat different. The Fragments of Life (FOL) library (Figure 1) is a collection of approximately 1,400 structurally diverse small molecules found in the cellular environment, metabolites, natural products, and their derivatives or isosteres (molecules of similar size containing the same number and types of atoms). Also included in the FOL library are a series of biaryl small molecules (which contain two tethered five- or six-membered ring structures) that mimic protein secondary structure elements (e.g., α-helical turns). Thus, this fragment set is useful for targeting both the active sites of enzymes and more complex protein surfaces including allosteric small molecule binding sites and protein–protein interfaces [16].

Figure 1. Conceptual organization of the deCODE biostructures Fragments of Life library.

The current ∼1,400-compound library contains chemically tractable natural small molecule metabolites (FOL-Nat), metabolite-like compounds and their bioisosteres (FOL-NatD), and biaryl mimetics of protein architecture (FOL-Biaryl). The FOL-Nat members include any natural molecule of molecular weight <350 daltons that exists as a substrate, natural product, or allosteric regulator of any metabolic pathway in any cell type, such as the biosynthetic pathways for the neurotransmitter serotonin (1) and the plant hormone auxin (2). The FOL-Nat members also include secondary metabolites such as bestatin (3), a secondary metabolite of Streptomyces olivoreticuli [38]. FOL-NatD fragments are defined as heteroatom-containing derivatives, isosteres, or analogs of any FOL-Nat molecule. For example, fragments 4–7 contain the indole scaffold, which is known to be a privileged building block for drug molecules [39]. To emulate protein architecture, the FOL-Biaryl fragments were selected from a variety of biaryl compounds that are potential mimics of protein α, β, or γ turns [40][42]. These include a compound (8) whose structure in an energy-minimized state can be seen to mimic the architecture on an α-turn of a protein structure (here, residues Ser65-Ile66-Leu67-Lys68 of PDB ID:1RTP) and, similarly, a compound (9) whose structure mimics the β-turn of a protein structure (residues Ala20-Ala21-Asp22-Ser23).


Targeting Oligomeric Enzymes

Protein–protein interaction and assemblies, ranging from simple dimers to extremely complex arrangements as seen in the ribosome or the nuclear pore complex, form the basis of most biological processes, and there are usually numerous points of contact between the macromolecules involved. Yet the protein–protein interfaces formed by oligomerization are not necessarily accompanied by a large gain in free energy, and small molecules have been shown to prevent critical protein–protein interactions [17]. These findings have prompted recent discussion of a structure-based approach aimed at developing novel small-molecule antibiotics that modulate protein activity by binding to an interface between subunits within multi-protein complexes [18]. The bacterial enzyme inorganic pyrophosphatase may serve as an example for this approach, since it exists in a hexameric state that requires conformational flexibility for its essential role in converting inorganic pyrophosphate into phosphate [19][21]. Moreover, whereas all bacterial inorganic pyrophosphatases function as a homohexamer, the eukaryotic cytosolic and mitochondrial inorganic pyrophosphatases function as homodimers [21]. Hence eukaryotic inorganic pyrophosphatases have different oligomeric interfaces than those of bacterial enzymes. This suggests that it may be possible to inhibit the bacterial inorganic pyrophosphatase safely by targeting its oligomeric state rather than its highly conserved active site. A similar approach has recently been used to identify species-specific modulators of porphobilinogen synthase (PBGS) activity [22]. SSGCID has solved the high-resolution X-ray crystal structure of inorganic pyrophosphatase from the pathogenic bacterium Burkholderia pseudomallei, and a subsequent FOL screen of this target identified several fragments that specifically bind at multiple oligomerization pockets in a molecular interface between the two trimers of the homohexamer (Figure 2). While these fragments remain to be validated in terms of their species-specific inhibition of inorganic pyrophosphatase activity, they represent potential starting points for the development of novel antibiotics.

Figure 2. B. pseudomallei inorganic pyrophosphatase with bound ligand at an oligomeric interface.

Homo-hexameric bacterial inorganic pyrophosphatase is a dimer of trimers (blue and green). The illustration shows the hexamer structure in a complex with three ligand fragment molecules (red spheres and stick structures represent fragment FOL 110), each of which is located at one of three “dimer of trimer” interfaces (1.5 ligands per monomer) (PDBID:3EJ0). The location of one pyrophosphate substrate (cyan spheres) at the active site of one of the monomers is indicated here based on the superimposed structure of the hexamer with pyrophosphate bound in the active site (PDBID:3EIY). The binding sites of the ligands (red) are clearly seen in a pocket formed by the homo-oligomeric assemblage, which is distant from the active site where pyrophosphate (cyan) binds.


Industry-Generated Structures and the Protein Data Bank

As we have seen above, protein structure information is the bread and butter of structure-based drug discovery. Structural genomics projects (Table 2) have substantially increased the number of protein structures solved and have made this information freely and openly available (i.e., at no cost and without restriction by copyright or other constraints) by depositing it in the Protein Data Bank (PDB) [23]. Most publishers have policies that require authors to deposit structural data in the PDB at the time of publication, so structures determined by academic researchers worldwide are, for the most part, well disseminated. By contrast, the pharmaceutical industry is sitting on a mountain of structural data for protein–ligand complexes from globally important pathogens, which is not available to the wider scientific community. The secrecy engendered by the current economic incentives driving drug discovery in the commercial sector has led to a substantial waste of precious resources through duplication of effort and inability to learn from others' successes and failures. The situation is unlikely to change without a concerted effort to find ways to overcome the financial and intellectual property barriers that prevent dissemination of this information. A recent publication suggested that open access industry–academia partnerships may provide one possible model [24]. We propose that the United States National Institutes of Health, along with other national and international research-funding agencies, issue calls for proposals that will fund the transfer of the highly valuable structural information from corporate databases into the PDB. Such an effort would obviously require discussion with industrial parties to negotiate mutually acceptable policies and mechanisms for the deposition of these structures in the public databases. These might include relaxation of release standards for industrial entities, such that structural information could be safely deposited in PDB at the time of structure determination and released only at a later date more appropriate for protection of intellectual property.

Challenges for the Future

We are currently witnessing an explosion in technological and computational advances in structural genomics, with protein structures of hundreds or thousands of medically relevant targets from infectious disease organisms likely to be available over the next few years. This new information provides both academic and for-profit scientists with an unprecedented opportunity to accelerate the development of new and improved chemotherapeutic agents against these pathogens. One major challenge will be the adaptation of existing fragment-based drug design methods to match the scale of the structural genomics era. New high-throughput methods need to be developed for fragment-screening to enhance the success rate for protein–ligand structure determination.

Major attention is also needed to the development of fully automated, very high throughput crystal growth screening methods to elucidate the binding of well-selected compounds to medically relevant targets. These screens need to cover many (up to 100) protein variants [25],[26], 1,000–10,000 different small molecule compounds, and approximately 1,000 different crystal growth conditions [27], resulting in 108 to 109 conditions to be tested for a single drug target. Obviously, this will require development of even smaller volume assays than those currently in use [28][31]—down to the low picoliters—and automated detection of crystals in the millions of crystallization chambers [32][34]. Further development of automated capillary crystallization methods [35] might provide another way to achieve the very high throughput crystal screening required for reaching the full power of medical structural genomics in the future. Cryoprotection of the crystals is a specific hurdle, although it might be possible to routinely collect and merge partial datasets from multiple crystals under non-cryo conditions. Alternatively, the use of micromeshes [36],[37] and further miniaturization of trays and other crystal screening tools may allow cryoprotection of many crystals simultaneously.

In addition, existing databases will need to be modified to allow easy dissemination of the results from these fragment screens, and a serious effort should be made to persuade small and big pharma to release coordinates of drug targets from globally important infectious disease organisms. It will also be critical (but challenging) for structural biologists to collaborate with medicinal chemists and molecular biologists to turn these fragment from promising leads to effective drugs. Together, these steps should begin to release a flood of structures that provide a tremendous resource for improving health in rich and poor countries alike.


The authors wish to thank all the individuals who have dedicated themselves to the SSGCID and MSGPP projects. In particular, we thank Robin Stacy, Bart Staker, Alberto Napuli, Frank E. Zucker, Erkang Fan, Christophe Verlinde, Ethan Merritt, and Frederick Buckner, to name but a few.


  1. 1. Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL (2007) Drugs for bad bugs: Confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6: 29–40.
  2. 2. Haquin S, Oeuillet E, Pajon A, Harris M, Jones AT, et al. (2008) Data management in structural genomics: An overview. Methods Mol Biol 426: 49–79.
  3. 3. Wright HT, Reynolds KA (2007) Antibacterial targets in fatty acid biosynthesis. Curr Opin Microbiol 10: 447–453.
  4. 4. Brinster S, Lamberet G, Staels B, Trieu-Cuot P, Gruss A, et al. (2009) Type II fatty acid synthesis is not a suitable antibiotic target for gram-positive pathogens. Nature 458: 83–86.
  5. 5. Hoon S, Smith AM, Wallace IM, Suresh S, Miranda M, et al. (2008) An integrated platform of genomic assays reveals small-molecule bioactivities. Nat Chem Biol 4: 498–506.
  6. 6. Ericsson UB, Hallberg BM, Detitta GT, Dekker N, Nordlund P (2006) Thermofluor-based high-throughput stability optimization of proteins for structural studies. Anal Biochem 357: 289–298.
  7. 7. Congreve M, Chessari G, Tisi D, Woodhead AJ (2008) Recent developments in fragment-based drug discovery. J Med Chem 51: 3661–3689.
  8. 8. Verlinde CLMJ, Kim H, Bernstein BE, Mande SC, Hol WG (1997) Antitrypanosomiasis drug development based on structures of glycolytic enzymes. In: Veerapandian P, editor. Structure-based drug design. New York: Marcel Dekker. pp. 365–394.
  9. 9. Rees DC, Congreve M, Murray CW, Carr R (2004) Fragment-based lead discovery. Nat Rev Drug Discov 3: 660–672.
  10. 10. Congreve M, Carr R, Murray C, Jhoti H (2003) A “rule of three” for fragment-based lead discovery? Drug Discov Today 8: 876–877.
  11. 11. Nienaber VL, Greer J (2000) Discovering novel ligands for macromolecules using X-ray crystallographic screening. Nature Biotechnol 18: 1105–1108.
  12. 12. Neumann T, Junker HD, Schmidt K, Sekul R (2007) SPR-based fragment screening: Advantages and applications. Curr Top Med Chem 7: 1630–1642.
  13. 13. Jhoti H, Cleasby A, Verdonk M, Williams G (2007) Fragment-based screening using X-ray crystallography and NMR spectroscopy. Curr Opin Chem Biol 11: 485–493.
  14. 14. Erlanson DA (2006) Fragment-based lead discovery: A chemical update. Curr Opin Biotechnol 17: 643–652.
  15. 15. Bosch J, Robien MA, Mehlin C, Boni E, Riechers A, et al. (2006) Using fragment cocktail crystallography to assist inhibitor design of Trypanosoma brucei nucleoside 2-deoxyribosyltransferase. J Med Chem 49: 5939–5946.
  16. 16. Davies DR, Mamat B, Magnusson OT, Christensen J, Haraldsson MH, et al. (2009) Discovery of leukotriene A4 hydrolase inhibitors using metabolomics biased fragment crystallography. J Med Chem 52: 4694–4715.
  17. 17. Liuzzi M, Deziel R, Moss N, Beaulieu P, Bonneau AM, et al. (1994) A potent peptidomimetic inhibitor of HSV ribonucleotide reductase with antiviral activity in vivo. Nature 372: 695–698.
  18. 18. Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450: 1001–1009.
  19. 19. Kankare J, Salminen T, Lahti R, Cooperman BS, Baykov AA, et al. (1996) Structure of Escherichia coli inorganic pyrophosphatase at 2.2 Å resolution. Acta Crystallogr D Biol Crystallogr 52: 551–563.
  20. 20. Oksanen E, Ahonen AK, Tuominen H, Tuominen V, Lahti R, et al. (2007) A complete structural description of the catalytic cycle of yeast pyrophosphatase. Biochemistry 46: 1228–1239.
  21. 21. Sivula T, Salminen A, Parfenyev AN, Pohjanjoki P, Goldman A, et al. (1999) Evolutionary aspects of inorganic pyrophosphatase. FEBS Lett 454: 75–80.
  22. 22. Lawrence SH, Ramirez UD, Tang L, Fazliyez F, Kundrat L, et al. (2008) Shape shifting leads to small-molecule allosteric drug discovery. Chem Biol 15: 586–596.
  23. 23. Berman H, Henrick K, Nakamura H, Markley JL (2007) The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res 35: D301–303.
  24. 24. Edwards AM, Bountra C, Kerr DJ, Willson TM (2009) Open access chemical and clinical probes to support drug discovery. Nat Chem Biol 5: 436–440.
  25. 25. Choi KH, Groarke JM, Young DC, Rossmann MG, Pevear DC, et al. (2004) Design, expression, and purification of a Flaviviridae polymerase using a high-throughput approach to facilitate crystal structure determination. Protein Sci 13: 2685–2692.
  26. 26. Graslund S, Sagemark J, Berglund H, Dahlgren LG, Flores A, et al. (2008) The use of systematic N- and C-terminal deletions to promote production and structural studies of recombinant proteins. Protein Expr Purif 58: 210–221.
  27. 27. Luft JR, Collins RJ, Fehrman NA, Lauricella AM, Veatch CK, et al. (2003) A deliberate approach to screening for initial crystallization conditions of biological macromolecules. J Struct Biol 142: 170–179.
  28. 28. Santarsiero BDYD, Lee CC, Spraggon G, Gu J, Scheibe D, Uber EC, Cornell EW, Nordmeyer RA, Kolbe WF, Jin J, Jones AL, Jaklevic JM, Schultz PG, Stevens RC (2002) An approach to rapid protein crystallization using nanodroplets. J Appl Crystallogr 35: 278–281.
  29. 29. Hansen CL, Skordalakes E, Berger JM, Quake SR (2002) A robust and scalable microfluidic metering method that allows protein crystal growth by free interface diffusion. Proc Natl Acad Sci U S A 99: 16531–16536.
  30. 30. Zheng B, Roach LS, Ismagilov RF (2003) Screening of protein crystallization conditions on a microfluidic chip using nanoliter-size droplets. J Am Chem Soc 125: 11170–11171.
  31. 31. Gerdts CJ, Elliott M, Lovell S, Mixon MB, Napuli AJ, et al. (2008) The plug-based nanovolume Microcapillary Protein Crystallization System (MPCS). Acta Crystallogr D Biol Crystallogr 64: 1116–1122.
  32. 32. Wilson J (2002) Towards the automated evaluation of crystallization trials. Acta Crystallogr D Biol Crystallogr 58: 1907–1914.
  33. 33. Pan S, Shavit G, Penas-Centeno M, Xu DH, Shapiro L, et al. (2006) Automated classification of protein crystallization images using support vector machines with scale-invariant texture and Gabor features. Acta Crystallogr D Biol Crystallogr 62: 271–279.
  34. 34. Liu R, Freund Y, Spraggon G (2008) Image-based crystal detection: A machine-learning approach. Acta Crystallogr D Biol Crystallogr 64: 1187–1195.
  35. 35. Fan E, Baker D, Fields S, Gelb MH, Buckner FS, et al. (2008) Structural genomics of pathogenic protozoa: An overview. Methods Mol Biol 426: 497–513.
  36. 36. Wagner A, Diez J, Schulze-Briese C, Schluckebier G (2009) Crystal structure of ultralente—A microcrystalline insulin suspension. Proteins 74: 1018–1027.
  37. 37. Thorne RESZ, Kmetko J, O'Niell J, Gillilan R (2003) Microfabricated mounts for high-throughput macromolecular cryocrystallography. J Applied Crystallography 36: 1455–1460.
  38. 38. Schorlemmer HU, Bosslet K, Dickneite G, Luben G, Sedlacek HH (1984) Studies on the mechanisms of action of the immunomodulator Bestatin in various screening test systems. Behring Inst Mitt: 157–173.
  39. 39. Costantino L, Barlocco D (2006) Privileged structures as leads in medicinal chemistry. Curr Med Chem 13: 65–85.
  40. 40. Biros SM, Moisan L, Mann E, Carella A, Zhai D, et al. (2007) Heterocyclic alpha-helix mimetics for targeting protein-protein interactions. Bioorg Med Chem Lett 17: 4641–4645.
  41. 41. Robinson JA (2008) Beta-hairpin peptidomimetics: design, structures and biological activities. Acc Chem Res 41: 1278–1288.
  42. 42. Saraogi I, Hamilton AD (2008) alpha-Helix mimetics as inhibitors of protein-protein interactions. Biochem Soc Trans 36: 1414–1417.
  43. 43. Root MJ, Steger HK (2004) HIV-1 gp41 as a target for viral entry inhibition. Curr Pharm Des 10: 1805–1825.
  44. 44. Weissenhorn W, Dessen A, Harrison SC, Skehel JJ, Wiley DC (1997) Atomic structure of the ectodomain from HIV-1 gp41. Nature 387: 426–430.
  45. 45. Ferrer M, Kapoor TM, Strassmaier T, Weissenhorn W, Skehel JJ, et al. (1999) Selection of gp41-mediated HIV-1 cell entry inhibitors from biased combinatorial libraries of non-natural binding elements. Nat Struct Biol 6: 953–960.
  46. 46. Lapatto R, Blundell T, Hemmings A, Overington J, Wilderspin A, et al. (1989) X-ray analysis of HIV-1 proteinase at 2.7 Å resolution confirms structural homology among retroviral enzymes. Nature 342: 299–302.
  47. 47. Miller M, Schneider J, Sathyanarayana BK, Toth MV, Marshall GR, et al. (1989) Structure of complex of synthetic HIV-1 protease with a substrate-based inhibitor at 2.3 Å resolution. Science 246: 1149–1152.
  48. 48. Navia MA, Fitzgerald PM, McKeever BM, Leu CT, Heimbach JC, et al. (1989) Three-dimensional structure of aspartyl protease from human immunodeficiency virus HIV-1. Nature 337: 615–620.
  49. 49. Wlodawer A, Miller M, Jaskolski M, Sathyanarayana BK, Baldwin E, et al. (1989) Conserved folding in retroviral proteases: Crystal structure of a synthetic HIV-1 protease. Science 245: 616–621.
  50. 50. Wlodawer A, Vondrasek J (1998) Inhibitors of HIV-1 protease: A major success of structure-assisted drug design. Annu Rev Biophys Biomol Struct 27: 249–284.
  51. 51. Abdel-Rahman HM, Al-karamany GS, El-Koussi NA, Youssef AF, Kiso Y (2002) HIV protease inhibitors: Peptidomimetic drugs and future perspectives. Curr Med Chem 9: 1905–1922.
  52. 52. Chrusciel RA, Strohbach JW (2004) Non-peptidic HIV protease inhibitors. Curr Top Med Chem 4: 1097–1114.
  53. 53. Das K, Lewi PJ, Hughes SH, Arnold E (2005) Crystallography and the design of anti-AIDS drugs: Conformational flexibility and positional adaptability are important in the design of non-nucleoside HIV-1 reverse transcriptase inhibitors. Prog Biophys Mol Biol 88: 209–231.
  54. 54. Kohlstaedt LA, Wang J, Friedman JM, Rice PA, Steitz TA (1992) Crystal structure at 3.5 Å resolution of HIV-1 reverse transcriptase complexed with an inhibitor. Science 256: 1783–1790.
  55. 55. Smerdon SJ, Jager J, Wang J, Kohlstaedt LA, Chirino AJ, et al. (1994) Structure of the binding site for nonnucleoside inhibitors of the reverse transcriptase of human immunodeficiency virus type 1. Proc Natl Acad Sci U S A 91: 3911–3915.
  56. 56. Babu YS, Chand P, Bantia S, Kotian P, Dehghani A, et al. (2000) BCX-1812 (RWJ-270201): Discovery of a novel, highly potent, orally active, and selective influenza neuraminidase inhibitor through structure-based drug design. J Med Chem 43: 3482–3486.
  57. 57. Bossart-Whitaker P, Carson M, Babu YS, Smith CD, Laver WG, et al. (1993) Three-dimensional structure of influenza A N9 neuraminidase and its complex with the inhibitor 2-deoxy 2,3-dehydro-N-acetyl neuraminic acid. J Mol Biol 232: 1069–1083.
  58. 58. Kim CU, Lew W, Williams MA, Liu H, Zhang L, et al. (1997) Influenza neuraminidase inhibitors possessing a novel hydrophobic interaction in the enzyme active site: Design, synthesis, and structural analysis of carbocyclic sialic acid analogues with potent anti-influenza activity. J Am Chem Soc 119: 681–690.
  59. 59. von Itzstein M, Wu WY, Kok GB, Pegg MS, Dyason JC, et al. (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363: 418–423.
  60. 60. Hadfield AT, Lee W, Zhao R, Oliveira MA, Minor I, et al. (1997) The refined structure of human rhinovirus 16 at 2.15 Å resolution: Implications for the viral life cycle. Structure 5: 427–441.
  61. 61. Merritt EA, Zhang Z, Pickens JC, Ahn M, Hol WG, et al. (2002) Characterization and crystal structure of a high-affinity pentavalent receptor-binding inhibitor for cholera toxin and E. coli heat-labile enterotoxin. J Am Chem Soc 124: 8818–8824.
  62. 62. Hu X, Nguyen KT, Jiang VC, Lofland D, Moser HE, et al. (2004) Macrocyclic inhibitors for peptide deformylase: A structure-activity relationship study of the ring size. J Med Chem 47: 4941–4949.
  63. 63. Aronov AM, Verlinde CL, Hol WG, Gelb MH (1998) Selective tight binding inhibitors of trypanosomal glyceraldehyde-3-phosphate dehydrogenase via structure-based drug design. J Med Chem 41: 4790–4799.
  64. 64. Bressi JC, Choe J, Hough MT, Buckner FS, Van Voorhis WC, et al. (2000) Adenosine analogues as inhibitors of Trypanosoma brucei phosphoglycerate kinase: Elucidation of a novel binding mode for a 2-amino-N(6)-substituted adenosine. J Med Chem 43: 4135–4150.
  65. 65. Jin L, Harrison SC (2002) Crystal structure of human calcineurin complexed with cyclosporin A and human cyclophilin. Proc Natl Acad Sci U S A 99: 13522–13526.
  66. 66. Rahuel J, Rasetti V, Maibaum J, Rueger H, Goschke R, et al. (2000) Structure-based drug design: The discovery of novel nonpeptide orally active inhibitors of human renin. Chem Biol 7: 493–504.
  67. 67. Lam PY, Clark CG, Li R, Pinto DJ, Orwat MJ, et al. (2003) Structure-based design of novel guanidine/benzamidine mimics: Potent and orally bioavailable factor Xa inhibitors as novel anticoagulants. J Med Chem 46: 4405–4418.
  68. 68. Terasaka T, Kinoshita T, Kuno M, Seki N, Tanaka K, et al. (2004) Structure-based design, synthesis, and structure-activity relationship studies of novel non-nucleoside adenosine deaminase inhibitors. J Med Chem 47: 3730–3743.
  69. 69. Noble ME, Endicott JA, Johnson LN (2004) Protein kinase inhibitors: Insights into drug design from structure. Science 303: 1800–1805.