Small Molecule Docking from Theoretical Structural Models

  • Eva Maria Novoa
  • Lluis Ribas de Pouplana
  • Modesto Orozco
Chapter
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

Abstract

Structural approaches to rational drug design rely on the basic assumption that pharmacological activity requires, as necessary but not sufficient condition, the binding of a drug to one or several cellular targets, proteins in most cases. The traditional paradigm assumes that drugs that interact only with a single cellular target are specific and accordingly have little secondary effects, while promiscuous molecules are more likely to generate undesirable side effects. However, current examples indicate that often efficient drugs are able to interact with several biological targets [1] and in fact some dirty drugs, such as chlorpromazine, dextromethorphan, and ibogaine exhibit desired pharmacological properties [2]. These considerations highlight the tremendous difficulty of designing small molecules that both have satisfactory ADME properties and the ability of interacting with a limited set of target proteins with a high affinity, avoiding at the same time undesirable interactions with other proteins. In this complex and challenging scenario, computer simulations emerge as the basic tool to guide medicinal chemists during the drug discovery process.

Keywords

Binding Mode Root Mean Square Deviation Homology Model Virtual Screening Docking Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Campbell, S.J., Gold, N.D., Jackson, R.M., Westhead, D.R.: Ligand binding: functional site location, similarity and docking. Curr. Opin. Struct. Biol. 13(3), 389–395 (2003)CrossRefGoogle Scholar
  2. 2.
    Keiser, M.J., et al.: Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25(2), 197–206 (2007)CrossRefGoogle Scholar
  3. 3.
    Fisher, E.: Einfluss der Konfiguration auf die Wirkung der Enzyme. Berichte der Deutschen Chemischen Gesellschaft. 27, 2985–2993 (1894)CrossRefGoogle Scholar
  4. 4.
    Kuntz, I.D., Blaney, J.M., Oatley, S.J., Langridge, R., Ferrin, T.E.: A geometric approach to macromolecule–ligand interactions. J. Mol. Biol. 161(2), 269–288 (1982)CrossRefGoogle Scholar
  5. 5.
    Butler, K.T., Luque, F.J., Barril, X.: Toward accurate relative energy predictions of the bioactive conformation of drugs. J. Comput. Chem. 30(4), 601–610 (2009)CrossRefGoogle Scholar
  6. 6.
    Perola, E., Charifson, P.S.: Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J. Med. Chem. 47(10), 2499–2510 (2004)CrossRefGoogle Scholar
  7. 7.
    Berman, H.M., et al.: The protein data bank. Nucleic. Acids. Res. 28(1), 235–242 (2000)ADSCrossRefGoogle Scholar
  8. 8.
    Cozzini, P., et al.: Target flexibility: an emerging consideration in drug discovery and design. J. Med. Chem. 51(20), 6237–6255 (2008)CrossRefGoogle Scholar
  9. 9.
    Merz, K.M.: Limits of free energy computation for protein–ligand interactions. J Chem. Theory. Comput. 6(4), 1018–1027 (2010)CrossRefGoogle Scholar
  10. 10.
    Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J.: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug. Discov. 3(11), 935–949 (2004)CrossRefGoogle Scholar
  11. 11.
    Pruitt, K.D., Tatusova, T., Maglott, D.R.: NCBI reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic. Acids. Res. 33(Database issue), D501–504 (2005)Google Scholar
  12. 12.
    Novoa, E.M., de Pouplana, L.R., Barril, X., Orozco, M.: Ensemble docking from homology models. J. Chem. Theory. Comput. 6(8), 2547–2557 (2010)CrossRefGoogle Scholar
  13. 13.
    Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins. 47(4), 409–443 (2002)CrossRefGoogle Scholar
  14. 14.
    Leach, A.R., Shoichet, B.K., Peishoff, C.E.: Prediction of protein–ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem. 49(20), 5851–5855 (2006)CrossRefGoogle Scholar
  15. 15.
    Shoichet, B.K., McGovern, S.L., Wei, B., Irwin, J.J.: Lead discovery using molecular docking. Curr. Opin. Chem. Biol. 6(4), 439–446 (2002)CrossRefGoogle Scholar
  16. 16.
    Sousa, S.F., Fernandes, P.A., Ramos, M.J.: Protein-ligand docking: current status and future challenges. Proteins. 65(1), 15–26 (2006)CrossRefGoogle Scholar
  17. 17.
    Shoichet, B.K., Bodian, D.L., Kuntz, I.D.: Molecular docking using shape descriptors. J. Comput. Chem. 13, 380–397 (1992)CrossRefGoogle Scholar
  18. 18.
    Gardiner, E.J., Willett, P., Artymiuk, P.J.: Graph-theoretic techniques for macromolecular docking. J. Chem. Inf. Comput. Sci. 40(2), 273–279 (2000)CrossRefGoogle Scholar
  19. 19.
    Ponder, J.W., Case, D.A.: Force fields for protein simulations. Adv. Protein. Chem. 66, 27–85 (2003)CrossRefGoogle Scholar
  20. 20.
    Morris, G.M. et al.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comput. Chem. 19, 1639–1662 (1998)CrossRefGoogle Scholar
  21. 21.
    Jones, G., Willett, P., Glen, R.C.: Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245(1), 43–53 (1995)CrossRefGoogle Scholar
  22. 22.
    Verdonk, M.L., Cole, J.C., Hartshorn, M.J., Murray, C.W., Taylor, R.D.: Improved protein-ligand docking using GOLD. Proteins. 52(4), 609–623 (2003)Google Scholar
  23. 23.
    Rarey, M., Kramer, B., Lengauer, T., Klebe, G.: A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261(3), 470–489 (1996)CrossRefGoogle Scholar
  24. 24.
    Jorgensen, W.L., Tirado-Rives, J.: The OPLS force field for proteins. Energy minimizations for crystals of cyclic peptides and Crambin. J. Am. Chem. Soc. 110, 1657–1666 (1988)Google Scholar
  25. 25.
    Abagyan, R., Totrov, M., Kuznetsov, D.: ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994)CrossRefGoogle Scholar
  26. 26.
    Friesner, R.A., et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47(7), 1739–1749 (2004)Google Scholar
  27. 27.
    Ding, F., Yin, S., Dokholyan, N.V.: Rapid flexible docking using a stochastic rotamer library of ligands. J. Chem. Inf. Model. 50(9), 1623–1632 (2010)CrossRefGoogle Scholar
  28. 28.
    Gohlke, H., Klebe, G.: Statistical potentials and scoring functions applied to protein-ligand binding. Curr. Opin. Struct. Biol. 11(2), 231–235 (2001)CrossRefGoogle Scholar
  29. 29.
    Dunbrack, R.L., Jr. Karplus, M.: Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230(2), 543–574 (1993)Google Scholar
  30. 30.
    Holm, L., Sander, C.: Fast and simple Monte Carlo algorithm for side chain optimization in proteins: application to model building by homology. Proteins. 14(2), 213–223 (1992)CrossRefGoogle Scholar
  31. 31.
    Brunger, A.T., Kuriyan, J., Karplus, M.: Crystallographic R factor refinement by molecular dynamics. Science. 235(4787), 458–460 (1987)ADSCrossRefGoogle Scholar
  32. 32.
    Armen, R.S., Chen, J., Brooks, C.L.: An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J. Chem. Theory. Comput. 5(10), 2909–2923 (2009)CrossRefGoogle Scholar
  33. 33.
    Paulsen, J.L., Anderson, A.C.: Scoring ensembles of docked protein:ligand interactions for virtual lead optimization. J. Chem. Inf. Model. 49(12), 2813–2819 (2009)CrossRefGoogle Scholar
  34. 34.
    Craig, I.R., Essex, J.W., Spiegel, K.: Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J. Chem. Inf. Model. 50(4), 511–524 (2010)CrossRefGoogle Scholar
  35. 35.
    Huang, S.Y., Zou, X.: Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins. 66(2), 399–421 (2007a)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Rao, S., et al.: Improving database enrichment through ensemble docking. J. Comput. Aided. Mol. Des. 22(9), 621–627 (2008)ADSCrossRefGoogle Scholar
  37. 37.
    Rueda, M., Bottegoni, G., Abagyan, R.: Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50(1), 186–193 (2010)CrossRefGoogle Scholar
  38. 38.
    Damm, K.L., Carlson, H.A.: Exploring experimental sources of multiple protein conformations in structure-based drug design. J. Am. Chem Soc. 129(26), 8225–8235 (2007)CrossRefGoogle Scholar
  39. 39.
    Huang, S.Y., Zou, X.: Efficient molecular docking of NMR structures: application to HIV-1 protease. Protein. Sci. 16(1), 43–51 (2007b)CrossRefGoogle Scholar
  40. 40.
    Hawkins, P.C., Warren, G.L., Skillman, A.G., Nicholls, A.: How to do an evaluation: pitfalls and traps. J. Comput. Aided. Mol. Des. 22(3–4), 179–190 (2008)ADSCrossRefGoogle Scholar
  41. 41.
    Warren, G.L., et al.: A critical assessment of docking programs and scoring functions. J. Med. Chem. 49(20), 5912–5931 (2006)CrossRefGoogle Scholar
  42. 42.
    Yang, L., et al.: Identifying unexpected therapeutic targets via chemical-protein interactome. PLoS ONE. 5(3), e9568 (2010)CrossRefGoogle Scholar
  43. 43.
    Petrelli, A., Giordano, S.: From single- to multi-target drugs in cancer theraphy: when aspecificity becomes an advantage. Curr. Med. Chem. 15, 422–432 (2008)CrossRefGoogle Scholar
  44. 44.
    Wermuth, C.G.: Multitarget drugs: the end of the ‘one-target-on-disease’ phylosophy? Drug. Discov. Today. 9, 826–827 (2004)CrossRefGoogle Scholar
  45. 45.
    Kirchmair, J., Markt, P., Distinto, S., Wolber, G., Langer, T.: 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), 213–228 (2008)ADSCrossRefGoogle Scholar
  46. 46.
    Langer T., Hoffmann RD.: Pharmacophores and Pharmacophore Searches. Wiley-VCH, Weinheim, Germany, pp. 338–343 (2006)Google Scholar
  47. 47.
    Truchon, J.F., Bayly, C.I.: Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model. 47(2), 488–508 (2007)CrossRefGoogle Scholar
  48. 48.
    Jain, A.N., Nicholls, A.: Recommendations for evaluation of computational methods. J. Comput. Aided. Mol. Des. 22(3–4), 133–139 (2008)ADSCrossRefGoogle Scholar
  49. 49.
    Nicholls, A.: What do we know and when do we know it? J. Comput. Aided. Mol. Des. 22(3–4), 239–255 (2008)ADSCrossRefGoogle Scholar
  50. 50.
    Witten, I.H., Frank, E.: Credibility: Evaluating what’s been learned. In: Data minings: Practical machine learning tools and techniques, 2nd ed; Morgan Kaufmann: San Francisco, CA, pp. 161–176 (2005)Google Scholar
  51. 51.
    Koehl, P., Levitt, M.: A brighter future for protein structure prediction. Nat. Struct. Biol. 6(2), 108–111 (1999)CrossRefGoogle Scholar
  52. 52.
    Marti-Renom, M.A., et al.: Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291–325 (2000)CrossRefGoogle Scholar
  53. 53.
    Arnold, K., Bordoli, L., Kopp, J., Schwede, T.: The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 22(2), 195–201 (2006)CrossRefGoogle Scholar
  54. 54.
    Bates, P.A., Kelley, L.A., MacCallum, R.M., Sternberg, M.J.: Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM. Proteins. Suppl. 5, 39–46 (2001)CrossRefGoogle Scholar
  55. 55.
    Eswar, N., et al.: Tools for comparative protein structure modeling and analysis. Nucleic. Acids. Res. 31(13), 3375–3380 (2003)CrossRefGoogle Scholar
  56. 56.
    Sali, A., Blundell, T.L.: Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234(3), 779–815 (1993)CrossRefGoogle Scholar
  57. 57.
    Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic. Acids. Res. 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
  58. 58.
    Wistrand, M., Sonnhammer, E.L.: Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER. BMC Bioinformatics. 6, 99 (2005)CrossRefGoogle Scholar
  59. 59.
    McGovern, S.L., Shoichet, B.K.: Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J. Med. Chem. 46(14), 2895–2907 (2003)CrossRefGoogle Scholar
  60. 60.
    Rockey, W.M., Elcock, A.H.: Structure selection for protein kinase docking and virtual screening: homology models or crystal structures? Curr. Protein. Pept. Sci. 7(5), 437–457 (2006)CrossRefGoogle Scholar
  61. 61.
    Tuccinardi, T., Botta, M., Giordano, A., Martinelli, A.: Protein kinases: docking and homology modeling reliability. J. Chem. Inf. Model. 50(8), 1432–1441 (2010)CrossRefGoogle Scholar
  62. 62.
    Blake, J.D., Cohen, F.E.: Pairwise sequence alignment below the twilight zone. J. Mol. Biol. 307(2), 721–735 (2001)CrossRefGoogle Scholar
  63. 63.
    Jennings, A.J., Edge, C.M., Sternberg, M.J.: An approach to improving multiple alignments of protein sequences using predicted secondary structure. Protein. Eng. 14(4), 227–231 (2001)CrossRefGoogle Scholar
  64. 64.
    Sanchez, R., Sali, A.: Advances in comparative protein-structure modelling. Curr. Opin. Struct. Biol. 7(2), 206–214 (1997)CrossRefGoogle Scholar
  65. 65.
    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(1), 243–257 (2001)CrossRefGoogle Scholar
  66. 66.
    Marti-Renom, M.A., Madhusudhan, M.S., Sali, A.: Alignment of protein sequences by their profiles. Protein. Sci. 13(4), 1071–1087 (2003)CrossRefGoogle Scholar
  67. 67.
    von Ohsen, N., Sommer, I., Zimmer, R.: Profile-profile alignment: a powerful tool for protein structure prediction. Pac. Symp. Biocomput. 252–263 (2003)Google Scholar
  68. 68.
    Jaroszewski, L., Rychlewski, L., Godzik, A.: Improving the quality of twilight-zone alignments. Protein Sci. 9(8), 1487–1496 (2000)CrossRefGoogle Scholar
  69. 69.
    Sauder, J.M., Arthur, J.W., Dunbrack, R.L., Jr: Large-scale comparison of protein sequence alignment algorithms with structure alignments. Proteins. 40(1), 6–22 (2000)CrossRefGoogle Scholar
  70. 70.
    Marti-Renom, M.A., Madhusudhan, M.S., Fiser, A., Rost, B., Sali, A.: Reliability of assessment of protein structure prediction methods. Structure. 10(3), 435–440 (2002)CrossRefGoogle Scholar
  71. 71.
    Eswar, N., Sali, A.: (2007) Comparative modeling of drug target proteins. In: Taylor J., Triggle D., Mason J.S., (eds.) Computer-Assisted Drug Design, Comprehensive Medicinal Chemistry II, vol. 4, pp. 215–236. Elsevier, Oxford, UKGoogle Scholar
  72. 72.
    Sanchez, R., et al.: Protein structure modeling for structural genomics. Nat. Struct. Biol. 7 Suppl. 986–990 (2000)Google Scholar
  73. 73.
    Eramian, D., Eswar, N., Shen, M.Y., Sali, A.: How well can the accuracy of comparative protein structure models be predicted? Protein. Sci. 17(11), 1881–1893 (2008)CrossRefGoogle Scholar
  74. 74.
    Baker, D., Sali, A.: Protein structure prediction and structural genomics. Science. 294(5540), 93–96 (2001)ADSCrossRefGoogle Scholar
  75. 75.
    Cavasotto, C.N., Phatak, S.S.: Homology modeling in drug discovery: current trends and applications. Drug. Discov. Today. 14(13–14), 676–683 (2009)CrossRefGoogle Scholar
  76. 76.
    Diller, D.J., Li, R.: Kinases, homology models, and high throughput docking. J. Med. Chem. 46(22), 4638–4647 (2003)CrossRefGoogle Scholar
  77. 77.
    Oshiro, C., et al.: Performance of 3D-database molecular docking studies into homology models. J. Med. Chem. 47(3), 764–767 (2004)CrossRefGoogle Scholar
  78. 78.
    Kairys, V., Fernandes, M.X., Gilson, M.K.: Screening drug-like compounds by docking to homology models: a systematic study. J. Chem. Inf. Model. 46(1), 365–379 (2006)CrossRefGoogle Scholar
  79. 79.
    Ferrara, P., Jacoby, E.: Evaluation of the utility of homology models in high throughput docking. J. Mol. Model. 13(8), 897–905 (2007)CrossRefGoogle Scholar
  80. 80.
    Fan, H., et al.: Molecular docking screens using comparative models of proteins. J. Chem. Inf. Model. 49(11), 2512–2527 (2009)CrossRefGoogle Scholar
  81. 81.
    Barril, X., Morley, S.D.: Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J. Med. Chem. 48(13), 4432–4443 (2005)CrossRefGoogle Scholar
  82. 82.
    Birch, L., Murray, C.W., Hartshorn, M.J., Tickle, I.J., Verdonk, M.L.: Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. J. Comput. Aided. Mol. Des. 16(12), 855–869 (2002)CrossRefGoogle Scholar
  83. 83.
    Hillisch, A., Pineda, L.F., Hilgenfeld, R.: Utility of homology models in the drug discovery process. Drug. Discov. Today. 9(15), 659–669 (2004)CrossRefGoogle Scholar
  84. 84.
    Wishart, D.S., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic. Acids. Res. 36(Database issue), D901–906 (2008)Google Scholar
  85. 85.
    Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5(4), 823–826 (1986)Google Scholar
  86. 86.
    O’Donovan, C., Apweiler, R., Bairoch, A.: The human proteomics initiative (HPI). Trends. Biotechnol. 19(5), 178–181 (2001)CrossRefGoogle Scholar
  87. 87.
    Park, S.J., Kufareva, I., Abagyan, R.: Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J. Comput. Aided. Mol. Des. 24(5), 459–471 (2010)ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Eva Maria Novoa
    • 1
    • 2
  • Lluis Ribas de Pouplana
    • 2
    • 3
  • Modesto Orozco
    • 1
    • 3
    • 4
  1. 1.Joint IRB-BSC Research Program in Computational Biology, Barcelona Supercomputing Center and Institute for Research in Biomedicine, IRBBarcelonaSpain
  2. 2.Cell and Developmental BiologyInstitute for Research in BiomedicineBarcelonaSpain
  3. 3.Institució Catalana per la Recerca i Estudis AvançatsBarcelonaSpain
  4. 4.Structural Bioinformatics Node Instituto Nacional de BioinformáticaInstitute of Research in BiomedicineBarcelonaSpain

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