Application of NMR and Molecular Docking in Structure-Based Drug Discovery

Chapter
Part of the Topics in Current Chemistry book series (TOPCURRCHEM, volume 326)

Abstract

Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.

Keywords

Drug discovery FAST-NMR In silico screening Ligand affinity screens Molecular docking Nuclear magnetic resonance Virtual screening 

References

  1. 1.
    Venter JC et al (2001) The sequence of the human genome. Science 291(5507):1304–1351CrossRefGoogle Scholar
  2. 2.
    Paul SM et al (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214Google Scholar
  3. 3.
    Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3(8):711–715CrossRefGoogle Scholar
  4. 4.
    Cuatrecasas P (2006) Drug discovery in jeopardy. J Clin Invest 116(11):2837–2842CrossRefGoogle Scholar
  5. 5.
    Bernal A, Ear U, Kyrpides N (2001) Genomes OnLine Database (GOLD): a monitor of genome projects world-wide. Nucleic Acids Res 29(1):126–127CrossRefGoogle Scholar
  6. 6.
    Frishman D et al (2003) The PEDANT genome database. Nucleic Acids Res 31(1):207–211CrossRefGoogle Scholar
  7. 7.
    White RH (2006) The difficult road from sequence to function. J Bacteriol 188(10):3431–3432CrossRefGoogle Scholar
  8. 8.
    Gerlt JA, Babbitt PC (2000) Can sequence determine function? Genome Biol 1(5):REVIEWS0005Google Scholar
  9. 9.
    Rishton GM (1997) Reactive compounds and in vitro false positives in HTS. Drug Discov Today 2(9):382–384CrossRefGoogle Scholar
  10. 10.
    Seidler J et al (2003) Identification and prediction of promiscuous aggregating inhibitors among known drugs. J Med Chem 46(21):4477–4486CrossRefGoogle Scholar
  11. 11.
    McGovern SL et al (2003) A specific mechanism of nonspecific inhibition. J Med Chem 46(20):4265–4272CrossRefGoogle Scholar
  12. 12.
    McGovern SL et al (2002) A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem 45(8):1712–1722CrossRefGoogle Scholar
  13. 13.
    Kenny BA et al (1998) The application of high-throughput screening to novel lead discovery. Prog Drug Res 51:245–269CrossRefGoogle Scholar
  14. 14.
    Macarron R (2006) Critical review of the role of HTS in drug discovery. Drug Discov Today 11(7–8):277–279CrossRefGoogle Scholar
  15. 15.
    Powers R (2009) Advances in nuclear magnetic resonance for drug discovery. Expert Opin Drug Discov 4(10):1077–1098CrossRefGoogle Scholar
  16. 16.
    Pellecchia M et al (2008) Perspectives on NMR in drug discovery: a technique comes of age. Nat Rev Drug Discov 7(9):738–745CrossRefGoogle Scholar
  17. 17.
    Roberts GCK (2000) Applications of NMR in drug discovery. Drug Discov Today 5(6):230–240CrossRefGoogle Scholar
  18. 18.
    Huth JR et al (2004) ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J Am Chem Soc 127(1):217–224CrossRefGoogle Scholar
  19. 19.
    Dalvit C et al (2006) NMR-based quality control approach for the identification of false positives and false negatives in high throughput screening. Curr Drug Discov Technol 3(2):115–124CrossRefGoogle Scholar
  20. 20.
    Schade M (2007) Fragment-based lead discovery by NMR. Front Drug Des Discov 3:105–119Google Scholar
  21. 21.
    Zartler ER, Mo H (2007) Practical aspects of NMR-based fragment discovery. Curr Top Med Chem 7(16):1592–1599CrossRefGoogle Scholar
  22. 22.
    Dalvit C (2009) NMR methods in fragment screening: theory and a comparison with other biophysical techniques. Drug Discov Today 14(21/22):1051–1057CrossRefGoogle Scholar
  23. 23.
    Fesik SW (1993) NMR structure-based drug design. J Biomol NMR 3(3):261–269CrossRefGoogle Scholar
  24. 24.
    Kubinyi H (1998) Structure-based design of enzyme inhibitors and receptor ligands. Curr Opin Drug Discov Devel 1(1):4–15Google Scholar
  25. 25.
    Ishihara K et al (2009) Identification of urinary biomarkers useful for distinguishing a difference in mechanism of toxicity in rat model of cholestasis. Basic Clin Pharmacol Toxicol 105(3):156–166CrossRefGoogle Scholar
  26. 26.
    Ott K-H, Aranibar N (2007) Nuclear magnetic resonance metabonomics: methods for drug discovery and development. Methods Mol Biol 358:247–271CrossRefGoogle Scholar
  27. 27.
    Powers R (2009) NMR metabolomics and drug discovery. Magn Reson Chem 47(S1):S2–S11CrossRefGoogle Scholar
  28. 28.
    Powers R, Mercier KA, Copeland JC (2008) The application of FAST-NMR for the identification of novel drug discovery targets. Drug Discov Today 13(3–4):172–179CrossRefGoogle Scholar
  29. 29.
    Mercier KA et al (2006) FAST-NMR: functional annotation screening technology using NMR spectroscopy. J Am Chem Soc 128(47):15292–15299CrossRefGoogle Scholar
  30. 30.
    Kitchen DB et al (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3(11):935–949CrossRefGoogle Scholar
  31. 31.
    Halperin I et al (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47(4):409–443CrossRefGoogle Scholar
  32. 32.
    Warren GL et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931CrossRefGoogle Scholar
  33. 33.
    Hartshorn MJ et al (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 50(4):726–741CrossRefGoogle Scholar
  34. 34.
    Jacobsson M et al (2003) Improving structure-based virtual screening by multivariate analysis of scoring data. J Med Chem 46(26):5781–5789CrossRefGoogle Scholar
  35. 35.
    Loving K, Salam NK, Sherman W (2009) Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. J Comput Aided Mol Des 23(8):541–554CrossRefGoogle Scholar
  36. 36.
    Salaniwal S et al (2007) Critical evaluation of methods to incorporate entropy loss upon binding in high-throughput docking. Proteins 66(2):422–435CrossRefGoogle Scholar
  37. 37.
    Vasilyev V, Bliznyuk A (2004) Application of semiempirical quantum chemical methods as a scoring function in docking. Theor Chem Acc 112(4):313–317CrossRefGoogle Scholar
  38. 38.
    Wei D et al (2010) Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. J Chem Inf Model 50(10):1855–1864CrossRefGoogle Scholar
  39. 39.
    Zavodszky MI et al (2009) Scoring ligand similarity in structure-based virtual screening. J Mol Recognit 22(4):280–292CrossRefGoogle Scholar
  40. 40.
    Morris GM et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791CrossRefGoogle Scholar
  41. 41.
    Ewing TJ et al (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15(5):411–428CrossRefGoogle Scholar
  42. 42.
    Rarey M et al (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489CrossRefGoogle Scholar
  43. 43.
    Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749CrossRefGoogle Scholar
  44. 44.
    Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125(7):1731–1737CrossRefGoogle Scholar
  45. 45.
    Bohm HJ (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6(6):593–606CrossRefGoogle Scholar
  46. 46.
    Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6(1):61–78CrossRefGoogle Scholar
  47. 47.
    Cerqueira NMFSA et al. (2010) Virtual screening of compound libraries. Methods Mol Biol 572:57–70 (Ligand-Macromolecular Interactions in Drug Discovery)Google Scholar
  48. 48.
    Ripphausen P et al (2010) Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53(24):8461–8467CrossRefGoogle Scholar
  49. 49.
    Sousa SF et al (2010) Virtual screening in drug design and development. Comb Chem High Throughput Screen 13(5):442–453CrossRefGoogle Scholar
  50. 50.
    Eckert H, Bajorath J (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today 12(5&6):225–233CrossRefGoogle Scholar
  51. 51.
    Merz KM Jr (2010) Limits of free energy computation for protein-ligand interactions. J Chem Theory Comput 6(5):1769–1776CrossRefGoogle Scholar
  52. 52.
    Proschak E et al (2007) Shapelets: possibilities and limitations of shape-based virtual screening. J Comput Chem 29(1):108–114CrossRefGoogle Scholar
  53. 53.
    Wyss DF, McCoy MA, Senior MM (2002) NMR-based approaches for lead discovery. Curr Opin Drug Discov Devel 5(4):630–647Google Scholar
  54. 54.
    Lepre CA, Moore JM, Peng JW (2004) Theory and applications of NMR-based screening in pharmaceutical research. Chem Rev 104(8):3641–3676CrossRefGoogle Scholar
  55. 55.
    Mercier KA, Powers R (2005) Determining the optimal size of small molecule mixtures for high throughput NMR screening. J Biomol NMR 31(3):243–258CrossRefGoogle Scholar
  56. 56.
    Hajduk PJ, Olejniczak ET, Fesik SW (1997) One-dimensional relaxation- and diffusion-edited NMR methods for screening compounds that bind to macromolecules. J Am Chem Soc 119:12257–12261CrossRefGoogle Scholar
  57. 57.
    Mayer M, Meyer B (1999) Characterization of ligand binding by saturation transfer difference NMR spectroscopy. Angew Chem Int Ed 38(12):1784–1788CrossRefGoogle Scholar
  58. 58.
    Dalvit C et al (2000) Identification of compounds with binding affinity to proteins via magnetization transfer from bulk water. J Biomol NMR 18(1):65–68CrossRefGoogle Scholar
  59. 59.
    Jahnke W, Rudisser S, Zurini M (2001) Spin label enhanced NMR screening. J Am Chem Soc 123(13):3149–3150CrossRefGoogle Scholar
  60. 60.
    Vanwetswinkel S et al (2005) TINS, target immobilized NMR screening: an efficient and sensitive method for ligand discovery. Chem Biol 12(2):207–216CrossRefGoogle Scholar
  61. 61.
    Fejzo J et al (1999) The SHAPES strategy: an NMR-based approach for lead generation in drug discovery. Chem Biol 6(10):755–769CrossRefGoogle Scholar
  62. 62.
    Dalvit C et al (2003) Fluorine-NMR experiments for high-throughput screening: theoretical aspects, practical considerations, and range of applicability. J Am Chem Soc 125(25):7696–7703CrossRefGoogle Scholar
  63. 63.
    Dalvit C et al (2002) Fluorine-NMR competition binding experiments for high-throughput screening of large compound mixtures. Comb Chem High Throughput Screen 5(8):605–611Google Scholar
  64. 64.
    Dalvit C et al (2003) A general NMR method for rapid, efficient, and reliable biochemical screening. J Am Chem Soc 125(47):14620–14625CrossRefGoogle Scholar
  65. 65.
    Dalvit C et al (2004) Reliable high-throughput functional screening with 3-FABS. Drug Discov Today 9(14):595–602CrossRefGoogle Scholar
  66. 66.
    Price SW (1997) Pulsed-field gradient nuclear magnetic resonance as a tool for studying translational diffusion: part 1. Basic theory. Concepts Magn Reson 9:299–336CrossRefGoogle Scholar
  67. 67.
    Price SW (1998) Pulsed-field gradient nuclear magnetic resonance as a tool for studying translational diffusion: part II. Experimental aspects. Concepts Magn Reson 10:197–237CrossRefGoogle Scholar
  68. 68.
    Shortridge MD et al (2008) Estimating protein-ligand binding affinity using high-throughput screening by NMR. J Comb Chem 10(6):948–958CrossRefGoogle Scholar
  69. 69.
    Ji Z, Yao Z, Liu M (2009) Saturation transfer difference nuclear magnetic resonance study on the specific binding of ligand to protein. Anal Biochem 385(2):380–382CrossRefGoogle Scholar
  70. 70.
    Muhandiram DR et al (1993) A gradient 13C NOESY-HSQC experiment for recording NOESY spectra of 13C-labeled proteins dissolved in H2O. J Magn Reson B 102(3):317–321CrossRefGoogle Scholar
  71. 71.
    Sklenar V et al (1993) Gradient-tailored water suppression for proton-nitrogen-15 HSQC experiments optimized to retain full sensitivity. J Magn Reson A 102(2):241–245CrossRefGoogle Scholar
  72. 72.
    Per VK et al (1997) Attenuated T2 relaxation by mutual cancellation of dipole-dipole coupling and chemical shift anisotropy indicates an avenue to NMR structures of very large biological macromolecules in solution. Proc Natl Acad Sci USA 94(23):12366–12371CrossRefGoogle Scholar
  73. 73.
    Shuker SB et al (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274(5292):1531–1534CrossRefGoogle Scholar
  74. 74.
    Fielding L (2007) NMR methods for the determination of protein-ligand dissociation constants. Prog Nucl Magn Reson Spectrosc 51:219–242CrossRefGoogle Scholar
  75. 75.
    Morton CJ et al (1996) Solution structure and peptide binding of the SH3 domain from human Fyn. Structure 4(6):705–714CrossRefGoogle Scholar
  76. 76.
    Stoll F (2003) Library design. Chimia 57(5):224–228CrossRefGoogle Scholar
  77. 77.
    Erlanson DA, McDowell RS, O’Brien T (2004) Fragment-based drug discovery. J Med Chem 47(14):3463–3482CrossRefGoogle Scholar
  78. 78.
    Siegal G, Ab E, Schultz J (2007) Integration of fragment screening and library design. Drug Discov Today 12(23&24):1032–1039CrossRefGoogle Scholar
  79. 79.
    Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337–341CrossRefGoogle Scholar
  80. 80.
    Schanda P, Kupce E, Brutscher B (2005) SOFAST-HMQC experiments for recording two-dimensional heteronuclear correlation spectra of proteins within a few seconds. J Biomol NMR 33(4):199–211CrossRefGoogle Scholar
  81. 81.
    Schanda P, Brutscher B (2006) Hadamard frequency-encoded SOFAST-HMQC for ultrafast two-dimensional protein NMR. J Magn Reson 178(2):334–339CrossRefGoogle Scholar
  82. 82.
    Mori S et al (1995) Improved sensitivity of HSQC spectra of exchanging protons at short interscan delays using a new fast HSQC (FHSQC) detection scheme that avoids water saturation. J Magn Reson B 108(1):94–98CrossRefGoogle Scholar
  83. 83.
    Taufer M et al (2005) Study of an accurate and fast protein-ligand docking algorithm based on molecular dynamics. Concurr Comput 17(14):1627–1641CrossRefGoogle Scholar
  84. 84.
    Garcia-Sosa AT, Sild S, Maran U (2009) Docking and virtual screening using distributed grid technology. QSAR Comb Sci 28:815–821CrossRefGoogle Scholar
  85. 85.
    Kuntz ID, Meng EC, Shoichet BK (1994) Structure-based molecular design. Acc Chem Res 27(5):117–123CrossRefGoogle Scholar
  86. 86.
    Krovat EM, Steindl T, Langer T (2005) Recent advances in docking and scoring. Curr Comput Aided Drug Des 1(1):93–102CrossRefGoogle Scholar
  87. 87.
    Cole JC et al (2005) Comparing protein-ligand docking programs is difficult. Proteins 60(3):325–332CrossRefGoogle Scholar
  88. 88.
    Wandzik I (2006) Current molecular docking tools and comparisons thereof. MATCH 55(2):271–278Google Scholar
  89. 89.
    Dias R, de Azevedo WF Jr (2008) Molecular docking algorithms. Curr Drug Targets 9(12):1040–1047CrossRefGoogle Scholar
  90. 90.
    Viji SN, Prasad PA, Gautham N (2009) Protein-ligand docking using mutually orthogonal Latin squares (MOLSDOCK). J Chem Inf Model 49(12):2687–2694CrossRefGoogle Scholar
  91. 91.
    Pearce BC et al (2009) E-novo: an automated workflow for efficient structure-based lead optimization. J Chem Inf Model 49(7):1797–1809CrossRefGoogle Scholar
  92. 92.
    Mizutani MY, Tomioka N, Itai A (1994) Rational automatic search method for stable docking models of protein and ligand. J Mol Biol 243(2):310–326CrossRefGoogle Scholar
  93. 93.
    Schlosser J, Rarey M (2009) Beyond the virtual screening paradigm: structure-based searching for new lead compounds. J Chem Inf Model 49(4):800–809CrossRefGoogle Scholar
  94. 94.
    Bostrom J, Greenwood JR, Gottfries J (2003) Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J Mol Graph Model 21(5):449–462CrossRefGoogle Scholar
  95. 95.
    Miller MD et al (1994) FLOG: a system to select quasi-flexible ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 8(2):153–174CrossRefGoogle Scholar
  96. 96.
    Griewel A et al (2009) Conformational sampling for large-scale virtual screening: accuracy versus ensemble size. J Chem Inf Model 49(10):2303–2311CrossRefGoogle Scholar
  97. 97.
    Hart TN, Read RJ (1994) Multiple-start Monte Carlo docking of flexible ligands. Birkhaeuser, BostonGoogle Scholar
  98. 98.
    Fuhrmann J et al (2010) A new Lamarckian genetic algorithm for flexible ligand-receptor docking. J Comput Chem 31(9):1911–1918Google Scholar
  99. 99.
    Cao T, Li T (2004) A combination of numeric genetic algorithm and tabu search can be applied to molecular docking. Comput Biol Chem 28(4):303–312CrossRefGoogle Scholar
  100. 100.
    Huang S-Y, Zou X (2010) Advances and challenges in protein-ligand docking. Int J Mol Sci 11:3016–3034CrossRefGoogle Scholar
  101. 101.
    Huang S-Y, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 12(40):12899–12908CrossRefGoogle Scholar
  102. 102.
    Huang S-Y, Zou X (2010) Mean-force scoring functions for protein-ligand binding. Annu Rep Comput Chem 6:281–296Google Scholar
  103. 103.
    Bohme A et al (1998) Piperacillin/tazobactam versus cefepime as initial empirical antimicrobial therapy in febrile neutropenic patients: a prospective randomized pilot study. Eur J Med Res 3(7):324–330Google Scholar
  104. 104.
    Eldridge MD et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11(5):425–445CrossRefGoogle Scholar
  105. 105.
    Tao P, Lai L (2001) Protein ligand docking based on empirical method for binding affinity estimation. J Comput Aided Mol Des 15(5):429–446CrossRefGoogle Scholar
  106. 106.
    Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16(1):11–26CrossRefGoogle Scholar
  107. 107.
    Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42(5):791–804CrossRefGoogle Scholar
  108. 108.
    Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295(2):337–356CrossRefGoogle Scholar
  109. 109.
    Velec HF, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48(20):6296–6303CrossRefGoogle Scholar
  110. 110.
    Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30(3):138–147CrossRefGoogle Scholar
  111. 111.
    McGann MR et al (2003) Gaussian docking functions. Biopolymers 68(1):76–90CrossRefGoogle Scholar
  112. 112.
    Stark JL et al (2010) Solution structure and function of YndB, an AHSA1 protein from Bacillus subtilis. Proteins 78(16):3328–3340CrossRefGoogle Scholar
  113. 113.
    Merlitz H, Herges T, Wenzel W (2004) Fluctuation analysis and accuracy of a large-scale in silico screen. J Comput Chem 25(13):1568–1575CrossRefGoogle Scholar
  114. 114.
    Tirado-Rives J, Jorgensen WL (2006) Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J Med Chem 49(20):5880–5884CrossRefGoogle Scholar
  115. 115.
    Charifson PS et al (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42(25):5100–5109CrossRefGoogle Scholar
  116. 116.
    Peng JW (2009) Communication breakdown: protein dynamics and drug design. Structure 17(3):319–320CrossRefGoogle Scholar
  117. 117.
    Hayward S, de Groot BL (2008) Normal modes and essential dynamics. Methods Mol Biol 443:89–106 (Molecular Modeling of Proteins)Google Scholar
  118. 118.
    Huang SY, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 66(2):399–421CrossRefGoogle Scholar
  119. 119.
    Erickson JA et al (2004) Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J Med Chem 47(1):45–55CrossRefGoogle Scholar
  120. 120.
    Sherman W et al (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553CrossRefGoogle Scholar
  121. 121.
    Stark J, Powers R (2008) Rapid protein-ligand costructures using chemical shift perturbations. J Am Chem Soc 130(2):535–545CrossRefGoogle Scholar
  122. 122.
    B-Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7–8):394–400CrossRefGoogle Scholar
  123. 123.
    Jiang F, Kim SH (1991) “Soft docking”: matching of molecular surface cubes. J Mol Biol 219(1):79–102CrossRefGoogle Scholar
  124. 124.
    Claussen H et al (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308(2):377–395CrossRefGoogle Scholar
  125. 125.
    Alberts IL, Todorov NP, Dean PM (2005) Receptor flexibility in de novo ligand design and docking. J Med Chem 48(21):6585–6596CrossRefGoogle Scholar
  126. 126.
    Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443CrossRefGoogle Scholar
  127. 127.
    Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11(13–14):580–594CrossRefGoogle Scholar
  128. 128.
    Schneider G, Bohm HJ (2002) Virtual screening and fast automated docking methods. Drug Discov Today 7(1):64–70CrossRefGoogle Scholar
  129. 129.
    Chen H et al (2006) On evaluating molecular-docking methods for pose prediction and enrichment factors. J Chem Inf Model 46(1):401–415CrossRefGoogle Scholar
  130. 130.
    Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47(3):558–565CrossRefGoogle Scholar
  131. 131.
    Kirchmair J et al (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection–what can we learn from earlier mistakes? J Comput Aided Mol Des 22(3–4):213–228CrossRefGoogle Scholar
  132. 132.
    Bender A, Glen RC (2005) A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model 45(5):1369–1375CrossRefGoogle Scholar
  133. 133.
    Truchon J-F, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47(2):488–508CrossRefGoogle Scholar
  134. 134.
    Scsibrany H et al (2003) Clustering and similarity of chemical structures represented by binary substructure descriptors. Chemom Intell Lab Syst 67(2):95–108CrossRefGoogle Scholar
  135. 135.
    Davis AM et al (2005) Components of successful lead generation. Curr Top Med Chem 5(4):421–439CrossRefGoogle Scholar
  136. 136.
    Sams-Dodd F (2006) Drug discovery: selecting the optimal approach. Drug Discov Today 11(9–10):465–472CrossRefGoogle Scholar
  137. 137.
    Fink T, Reymond JL (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J Chem Inf Model 47(2):342–353CrossRefGoogle Scholar
  138. 138.
    Lahana R (1999) How many leads from HTS? Drug Discov Today 4(10):447–448CrossRefGoogle Scholar
  139. 139.
    Goode DR et al (2008) Identification of promiscuous small molecule activators in high-throughput enzyme activation screens. J Med Chem 51(8):2346–2349CrossRefGoogle Scholar
  140. 140.
    Foloppe N et al (2006) Identification of chemically diverse Chk1 inhibitors by receptor-based virtual screening. Bioorg Med Chem 14(14):4792–4802CrossRefGoogle Scholar
  141. 141.
    Richardson CM et al (2007) Discovery of a potent CDK2 inhibitor with a novel binding mode, using virtual screening and initial, structure-guided lead scoping. Bioorg Med Chem Lett 17(14):3880–3885CrossRefGoogle Scholar
  142. 142.
    Pellecchia M et al (2004) NMR-based techniques in the hit identification and optimisation processes. Expert Opin Ther Targets 8(6):597–611CrossRefGoogle Scholar
  143. 143.
    Galperin MY, Koonin EV (2010) From complete genome sequence to ‘complete’ understanding? Trends Biotechnol 28(8):398–406CrossRefGoogle Scholar
  144. 144.
    Tucker CL (2002) High-throughput cell-based assays in yeast. Drug Discov Today 7(18 Suppl):S125–S130CrossRefGoogle Scholar
  145. 145.
    Lee YH et al (2005) Gene knockdown by large circular antisense for high-throughput functional genomics. Nat Biotechnol 23(5):591–599CrossRefGoogle Scholar
  146. 146.
    Joshi T et al (2004) Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae. OMICS 8(4):322–333CrossRefGoogle Scholar
  147. 147.
    del Val C et al (2004) High-throughput protein analysis integrating bioinformatics and experimental assays. Nucleic Acids Res 32(2):742–748CrossRefGoogle Scholar
  148. 148.
    Laurie AT, Jackson RM (2006) Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr Protein Pept Sci 7(5):395–406CrossRefGoogle Scholar
  149. 149.
    Blundell TL et al (2006) Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Philos Trans R Soc Lond B Biol Sci 361(1467):413–423CrossRefGoogle Scholar
  150. 150.
    Vajda S, Guarnieri F (2006) Characterization of protein-ligand interaction sites using experimental and computational methods. Curr Opin Drug Discov Devel 9(3):354–362Google Scholar
  151. 151.
    Mercier KA, Germer K, Powers R (2006) Design and characterization of a functional library for NMR screening against novel protein targets. Comb Chem High Throughput Screen 9(7):515–534CrossRefGoogle Scholar
  152. 152.
    Mercier KA, Shortridge MD, Powers R (2009) A multi-step NMR screen for the identification and evaluation of chemical leads for drug discovery. Comb Chem High Throughput Screen 12(3):285–295CrossRefGoogle Scholar
  153. 153.
    Powers R et al (2006) Comparison of protein active site structures for functional annotation of proteins and drug design. Proteins 65(1):124–135CrossRefGoogle Scholar
  154. 154.
    Park K, Kim D (2008) Binding similarity network of ligand. Proteins 71(2):960–971CrossRefGoogle Scholar
  155. 155.
    Mercier KA et al (2009) Structure and function of Pseudomonas aeruginosa protein PA1324 (21-170). Protein Sci 18(3):606–618Google Scholar
  156. 156.
    Shortridge MD, Powers R (2009) Structural and functional similarity between the bacterial type III secretion system needle protein PrgI and the eukaryotic apoptosis Bcl-2 proteins. PLoS One 4(10):e7442CrossRefGoogle Scholar
  157. 157.
    McCoy MA, Wyss DF (2000) Alignment of weakly interacting molecules to protein surfaces using simulations of chemical shift perturbations. J Biomol NMR 18(3):189–198CrossRefGoogle Scholar
  158. 158.
    Schieborr U et al (2005) How much NMR data is required to determine a protein-ligand complex structure? Chembiochem 6(10):1891–1898CrossRefGoogle Scholar
  159. 159.
    Wang B, Westerhoff LM, Merz KM Jr (2007) A critical assessment of the performance of protein-ligand scoring functions based on NMR chemical shift perturbations. J Med Chem 50(21):5128–5134CrossRefGoogle Scholar
  160. 160.
    Gonzalez-Ruiz D, Gohlke H (2009) Steering protein-ligand docking with quantitative NMR chemical shift perturbations. J Chem Inf Model 49(10):2260–2271CrossRefGoogle Scholar
  161. 161.
    Xu X-P, Case DA (2001) Automated prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database. J Biomol NMR 21(4):321–333CrossRefGoogle Scholar
  162. 162.
    Nilges M (1995) Calculation of protein structures with ambiguous distance restraints. Automated assignment of ambiguous NOE crosspeaks and disulphide connectivities. J Mol Biol 245(5):645–660CrossRefGoogle Scholar
  163. 163.
    Guntert P, Wuthrich K (2001) Sampling of conformation space in torsion angle dynamics calculations. Comput Phys Commun 138(2):155–169CrossRefGoogle Scholar
  164. 164.
    Gohlke H, Hendlich M, Klebe G (2000) Predicting binding modes, binding affinities and “hot spots” for protein-ligand complexes using a knowledge-based scoring function. Perspect Drug Discov Des 20:115–144CrossRefGoogle Scholar
  165. 165.
    Osapay K, Case DA (1991) A new analysis of proton chemical shifts in proteins. J Am Chem Soc 113(25):9436–9444CrossRefGoogle Scholar
  166. 166.
    Branson KM et al (2009) Discovery of inhibitors of lupin diadenosine 5′,5′′′-P(1), P(4)-tetraphosphate hydrolase by virtual screening. Biochemistry 48(32):7614–7620CrossRefGoogle Scholar
  167. 167.
    Jacobsson M et al (2008) Identification of Plasmodium falciparum spermidine synthase active site binders through structure-based virtual screening. J Med Chem 51(9):2777–2786CrossRefGoogle Scholar
  168. 168.
    Lee Y et al (2009) Identification of compounds exhibiting inhibitory activity toward the Pseudomonas tolaasii toxin tolaasin I using in silico docking calculations, NMR binding assays, and in vitro hemolytic activity assays. Bioorg Med Chem Lett 19(15):4321–4324CrossRefGoogle Scholar
  169. 169.
    Veldkamp CT et al (2010) Targeting SDF-1/CXCL12 with a ligand that prevents activation of CXCR4 through structure-based drug design. J Am Chem Soc 132(21):7242–7243CrossRefGoogle Scholar
  170. 170.
    Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182CrossRefGoogle Scholar
  171. 171.
    Lorber DM, Shoichet BK (2005) Hierarchical docking of databases of multiple ligand conformations. Curr Top Med Chem 5(8):739–749CrossRefGoogle Scholar
  172. 172.
    Bembenek SD, Tounge BA, Reynolds CH (2009) Ligand efficiency and fragment-based drug discovery. Drug Discov Today 14(5–6):278–283CrossRefGoogle Scholar
  173. 173.
    Reynolds CH, Tounge BA, Bembenek SD (2008) Ligand binding efficiency: trends, physical basis, and implications. J Med Chem 51(8):2432–2438CrossRefGoogle Scholar
  174. 174.
    Barelier S et al (2010) Discovery of fragment molecules that bind the human peroxiredoxin 5 active site. PLoS One 5(3):e9744CrossRefGoogle Scholar
  175. 175.
    Declercq JP et al (2001) Crystal structure of human peroxiredoxin 5, a novel type of mammalian peroxiredoxin at 1.5 A resolution. J Mol Biol 311(4):751–759CrossRefGoogle Scholar
  176. 176.
    Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48(7):2518–2525CrossRefGoogle Scholar
  177. 177.
    Hajduk PJ, Huth JR, Tse C (2005) Predicting protein druggability. Drug Discov Today 10(23–24):1675–1682CrossRefGoogle Scholar
  178. 178.
    Peng JW et al (2001) Nuclear magnetic resonance-based approaches for lead generation in drug discovery. Methods Enzymol 338:202–230CrossRefGoogle Scholar
  179. 179.
    Morris GM et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662CrossRefGoogle Scholar
  180. 180.
    Wang JF et al (1992) Solution studies of staphylococcal nuclease H124L. 2. 1H, 13C, and 15N chemical shift assignments for the unligated enzyme and analysis of chemical shift changes that accompany formation of the nuclease-thymidine 3′,5′-bisphosphate-calcium ternary complex. Biochemistry 31(3):921–936CrossRefGoogle Scholar
  181. 181.
    Wang JF et al (1990) Two-dimensional NMR studies of staphylococcal nuclease. 2. Sequence-specific assignments of carbon-13 and nitrogen-15 signals from the nuclease H124L-thymidine 3′,5′-bisphosphate-Ca2+ ternary complex. Biochemistry 29(1):102–113CrossRefGoogle Scholar
  182. 182.
    Wang JF, LeMaster DM, Markley JL (1990) Two-dimensional NMR studies of staphylococcal nuclease. 1. Sequence-specific assignments of hydrogen-1 signals and solution structure of the nuclease H124L-thymidine 3′,5′-bisphosphate-Ca2+ ternary complex. Biochemistry 29(1):88–101CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin-Heidelberg 2011

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of Nebraska – LincolnLincolnUSA

Personalised recommendations