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Application of Conformational Clustering in Protein–Ligand Docking

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Book cover Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 819))

Abstract

Protein–Ligand docking is a powerful technique routinely employed in structure-based drug design. Despite many reported success stories, docking is not always able to provide an accurate and easily interpretable prediction of the structure of the bound complex formed by a small organic molecule and a pharmacologically relevant target. Cluster analysis can represent a versatile and readily available postprocessing tool to be employed in combination with protein–ligand docking to simplify the evaluation of the results and help to overcome present limitations of docking protocols.

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References

  1. Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., and Ferrin, T. E. (1982) A geometric approach to macromolecule-ligand interactions, J Mol Biol 161, 269–288.

    Article  PubMed  CAS  Google Scholar 

  2. Halperin, I., Ma, B., Wolfson, H., and Nussinov, R. (2002) Principles of docking: An overview of search algorithms and a guide to scoring functions, Proteins 47, 409–443.

    Article  PubMed  CAS  Google Scholar 

  3. Kitchen, D. B., Decornez, H., Furr, J. R., and Bajorath, J. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications, Nat Rev Drug Discov 3, 935–949.

    Article  PubMed  CAS  Google Scholar 

  4. Taylor, R. D., Jewsbury, P. J., and Essex, J. W. (2002) A review of protein-small molecule docking methods, J Comput Aided Mol Des 16, 151–166.

    Article  PubMed  CAS  Google Scholar 

  5. Kontoyianni, M., McClellan, L. M., and Sokol, G. S. (2004) Evaluation of docking performance: comparative data on docking algorithms, J Med Chem 47, 558–565.

    Article  PubMed  CAS  Google Scholar 

  6. Carlson, H. A., and McCammon, J. A. (2000) Accommodating protein flexibility in computational drug design, Mol Pharmacol 57, 213–218.

    PubMed  CAS  Google Scholar 

  7. Welch, W., Ruppert, J., and Jain, A. N. (1996) Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites, Chemistry & Biology 3, 449–462.

    Article  CAS  Google Scholar 

  8. Moitessier, N., Englebienne, P., Lee, D., Lawandi, J., and Corbeil, C. R. (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go, Br J Pharmacol 153 Suppl 1, S7–26.

    PubMed  CAS  Google Scholar 

  9. Bursulaya, B. D., Totrov, M., Abagyan, R., and Brooks, C. L., 3rd. (2003) Comparative study of several algorithms for flexible ligand docking, J Comput Aided Mol Des 17, 755–763.

    Article  PubMed  CAS  Google Scholar 

  10. Cheng, T., Li, X., Li, Y., Liu, Z., and Wang, R. (2009) Comparative assessment of scoring functions on a diverse test set, J Chem Inf Model 49, 1079–1093.

    Article  PubMed  CAS  Google Scholar 

  11. Brooijmans, N., and Kuntz, I. D. (2003) Molecular recognition and docking algorithms, Annu Rev Biophys Biomol Struct 32, 335–373.

    Article  PubMed  CAS  Google Scholar 

  12. Yongye, A. B., Bender, A., and MartÃnez-Mayorga, K. (2010) Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble, Journal of Computer-Aided Molecular Design 24, 675–686.

    Article  PubMed  CAS  Google Scholar 

  13. Bottegoni, G., Cavalli, A., and Recanatini, M. (2006) A comparative study on the application of hierarchical-agglomerative clustering approaches to organize outputs of reiterated docking runs, Journal of Chemical Information and Modeling 46, 852–862.

    Article  PubMed  CAS  Google Scholar 

  14. Grazioso, G., Cavalli, A., De Amici, M., Recanatini, M., and De Micheli, C. (2008) Alpha7 nicotinic acetylcholine receptor agonists: Prediction of their binding affinity through a molecular mechanics poisson-boltzmann surface area approach, Journal of Computational Chemistry 29, 2593–2602.

    Article  PubMed  CAS  Google Scholar 

  15. Masetti, M., Cavalli, A., Recanatini, M., and Gervasio, F. L. (2009) Exploring complex protein-ligand recognition mechanisms with coarse metadynamics, J Phys Chem B 113, 4807–4816.

    Article  PubMed  CAS  Google Scholar 

  16. Colizzi, F., Perozzo, R., Scapozza, L., Recanatini, M., and Cavalli, A. (2010) Single-molecule pulling simulations can discern active from inactive enzyme inhibitors, Journal of the American Chemical Society 132, 7361–7371.

    Article  PubMed  CAS  Google Scholar 

  17. Piazzi, L., Cavalli, A., Belluti, F., Bisi, A., Gobbi, S., Rizzo, S., Bartolini, M., Andrisano, V., Recanatini, M., and Rampa, A. (2007) Extensive SAR and computational studies of 3-{4-[(benzylmethylamino)methyl] phenyl}-6,7-dimethoxy-2H-2-chromenone (AP2238) derivatives, Journal of Medicinal Chemistry 50, 4250–4254.

    Article  PubMed  CAS  Google Scholar 

  18. Tumiatti, V., Milelli, A., Minarini, A., Rosini, M., Bolognesi, M. L., Micco, M., Andrisano, V., Bartolini, M., Mancini, F., Recanatini, M., Cavalli, A., and Melchiorre, C. (2008) Structure-activity relationships of acetylcholinesterase noncovalent inhibitors based on a polyamine backbone. 4. Further investigation on the inner spacer, Journal of Medicinal Chemistry 51, 7308–7312.

    Article  PubMed  CAS  Google Scholar 

  19. Belluti, F., Piazzi, L., Bisi, A., Gobbi, S., Bartolini, M., Cavalli, A., Valenti, P., and Rampa, A. (2009) Design, synthesis, and evaluation of benzophenone derivatives as novel acetylcholinesterase inhibitors, European Journal of Medicinal Chemistry 44, 1341–1348.

    Article  PubMed  CAS  Google Scholar 

  20. Rivera-Becerril, E., Joseph-Nathan, P., Perez-Ãlvarez, V. M., and Morales-Rios, M. S. (2008) Synthesis and biological evaluation of (−)- and (+)-debromoflustramine B and its analogues as selective butyrylcholinesterase inhibitors, Journal of Medicinal Chemistry 51, 5271–5284.

    Article  PubMed  CAS  Google Scholar 

  21. Bolognesi, M. L., Banzi, R., Bartolini, M., Cavalli, A., Tarozzi, A., Andrisano, V., Minarini, A., Rosini, M., Tumiatti, V., Bergamini, C., Fato, R., Lenaz, G., Hrelia, P., Cattaneo, A., Recanatini, M., and Melchiorre, C. (2007) Novel class of quinone-bearing polyamines as multi-target-directed ligands to combat Alzheimer’s disease, Journal of Medicinal Chemistry 50, 4882–4897.

    Article  PubMed  CAS  Google Scholar 

  22. Bolognesi, M. L., Cavalli, A., Valgimigli, L., Bartolini, M., Rosini, M., Andrisano, V., Recanatini, M., and Melchiorre, C. (2007) Multi-target-directed drug design strategy: From a dual binding site acetylcholinesterase inhibitor to a trifunctional compound against Alzheimer’s disease, Journal of Medicinal Chemistry 50, 6446–6449.

    Article  PubMed  CAS  Google Scholar 

  23. Piazzi, L., Cavalli, A., Colizzi, F., Belluti, F., Bartolini, M., Mancini, F., Recanatini, M., Andrisano, V., and Rampa, A. (2008) Multi-target-directed coumarin derivatives: hAChE and BACE1 inhibitors as potential anti-Alzheimer compounds, Bioorganic and Medicinal Chemistry Letters 18, 423–426.

    Article  PubMed  CAS  Google Scholar 

  24. Rosini, M., Simoni, E., Bartolini, M., Cavalli, A., Ceccarini, L., Pascu, N., McClymont, D. W., Tarozzi, A., Bolognesi, M. L., Minarini, A., Tumiatti, V., Andrisano, V., Mellor, I. R., and Melchiorre, C. (2008) Inhibition of acetylcholinesterase, Î2-amyloid aggregation, and NMDA receptors in Alzheimer’s disease: A promising direction for the multi-target-directed ligands gold rush, Journal of Medicinal Chemistry 51, 4381–4384.

    Article  PubMed  CAS  Google Scholar 

  25. Rizzo, S., Bartolini, M., Ceccarini, L., Piazzi, L., Gobbi, S., Cavalli, A., Recanatini, M., Andrisano, V., and Rampa, A. (2010) Targeting Alzheimer’s disease: Novel indanone hybrids bearing a pharmacophoric fragment of AP2238, Bioorganic and Medicinal Chemistry 18, 1749–1760.

    Article  PubMed  CAS  Google Scholar 

  26. Hu, Q., Negri, M., Jahn-Hoffmann, K., Zhuang, Y., Olgen, S., Bartels, M., Muller-Vieira, U., Lauterbach, T., and Hartmann, R. W. (2008) Synthesis, biological evaluation, and molecular modeling studies of methylene imidazole substituted biaryls as inhibitors of human 17alpha-hydroxylase-17,20-lyase (CYP17)-Part II: Core rigidification and influence of substituents at the methylene bridge, Bioorganic and Medicinal Chemistry 16, 7715–7727.

    Article  PubMed  CAS  Google Scholar 

  27. Jagusch, C., Negri, M., Hille, U. E., Hu, Q., Bartels, M., Jahn-Hoffmann, K., Mendieta, M. A. E. P. B., Rodenwaldt, B., Müller-Vieira, U., Schmidt, D., Lauterbach, T., Recanatini, M., Cavalli, A., and Hartmann, R. W. (2008) Synthesis, biological evaluation and molecular modelling studies of methyleneimidazole substituted biaryls as inhibitors of human 17alpha-hydroxylase-17,20-lyase (CYP17). Part I: Heterocyclic modifications of the core structure, Bioorganic and Medicinal Chemistry 16, 1992–2010.

    Article  PubMed  CAS  Google Scholar 

  28. Hille, U. E., Hu, Q., Vock, C., Negri, M., Bartels, M., Muller-Vieira, U., Lauterbach, T., and Hartmann, R. W. (2009) Novel CYP17 inhibitors: Synthesis, biological evaluation, structure-activity relationships and modelling of methoxy- and hydroxy-substituted methyleneimidazolyl biphenyls, European Journal of Medicinal Chemistry 44, 2765–2775.

    Article  PubMed  CAS  Google Scholar 

  29. Hu, Q., Negri, M., Olgen, S., and Hartmann, R. W. (2010) The role of fluorine substitution in biphenyl methylene imidazole-type CYP17 inhibitors for the treatment of prostate carcinoma, ChemMedChem 5, 899–910.

    PubMed  CAS  Google Scholar 

  30. Gobbi, S., Cavalli, A., Negri, M., Schewe, K. E., Belluti, F., Piazzi, L., Hartmann, R. W., Recanatini, M., and Bisi, A. (2007) Imidazolylmethylbenzophenones as highly potent aromatase inhibitors, Journal of Medicinal Chemistry 50, 3420–3422.

    Article  PubMed  CAS  Google Scholar 

  31. Di Fenza, A., Rocchia, W., and Tozzini, V. (2009) Complexes of HIV-1 integrase with HAT proteins: Multiscale models, dynamics, and hypotheses on allosteric sites of inhibition, Proteins: Structure, Function and Bioformatics 76, 946–958.

    Article  Google Scholar 

  32. Tomlinson, S. M., Malmstrom, R. D., and Watowich, S. J. (2009) New approaches to structure-based discovery of Dengue protease inhibitors, Infectious Disorders - Drug Targets 9, 327–343.

    PubMed  CAS  Google Scholar 

  33. Totrov, M., and Abagyan, R. (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative, Curr Opin Struct Biol 18, 178–184.

    Article  PubMed  CAS  Google Scholar 

  34. Damm, K. L., and Carlson, H. A. (2007) Exploring experimental sources of multiple protein conformations in structure-based drug design, J Am Chem Soc 129, 8225–8235.

    Article  PubMed  CAS  Google Scholar 

  35. Barril, X., and Morley, S. D. (2005) Unveiling the Full Potential of Flexible Receptor Docking Using Multiple Crystallographic Structures, J. Med. Chem. 48, 4432–4443.

    Article  PubMed  CAS  Google Scholar 

  36. Bottegoni, G., Kufareva, I., Totrov, M., and Abagyan, R. (2009) Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking, J Med Chem 52, 397–406.

    Article  PubMed  CAS  Google Scholar 

  37. Rueda, M., Bottegoni, G., and Abagyan, R. (2010) Recipes for the selection of experimental protein conformations for virtual screening, J Chem Inf Model 50, 186–193.

    Article  PubMed  CAS  Google Scholar 

  38. Kiviranta, P. H., Salo, H. S., Leppanen, J., Rinne, V. M., Kyrylenko, S., Kuusisto, E., Suuronen, T., Salminen, A., Poso, A., Lahtela-Kakkonen, M., and Wallen, E. A. A. (2008) Characterization of the binding properties of SIRT2 inhibitors with a N-(3-phenylpropenoyl)-glycine tryptamide backbone, Bioorganic and Medicinal Chemistry 16, 8054–8062.

    Article  PubMed  CAS  Google Scholar 

  39. Kranjc, A., Bongarzone, S., Rossetti, G., Biarnes, X., Cavalli, A., Bolognesi, M. L., Roberti, M., Legname, G., and Carloni, P. (2009) Docking ligands on protein surfaces: The case study of prion protein, Journal of Chemical Theory and Computation 5, 2565–2573.

    Article  CAS  Google Scholar 

  40. Xiang, Z., Soto, C. S., and Honig, B. (2002) Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction, Proceedings of the National Academy of Sciences of the United States of America 99, 7432–7437.

    Article  PubMed  CAS  Google Scholar 

  41. Chang, M. W., Belew, R. K., Carroll, K. S., Olson, A. J., and Goodsell, D. S. (2008) Empirical entropic contributions in computational docking: Evaluation in APS reductase complexes, Journal of Computational Chemistry 29, 1753–1761.

    Article  PubMed  CAS  Google Scholar 

  42. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., and Olson, A. J. (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J Comput Chem 30, 2785–2791.

    Article  PubMed  CAS  Google Scholar 

  43. Jones, G., Willett, P., Glen, R. C., Leach, A. R., and Taylor, R. (1997) Development and validation of a genetic algorithm for flexible docking, Journal of Molecular Biology 267, 727–748.

    Article  PubMed  CAS  Google Scholar 

  44. Abagyan, R., Totrov, M., and Kuznetsov, D. (1994) Icm - a New Method for Protein Modeling and Design - Applications to Docking and Structure Prediction from the Distorted Native Conformation, Journal of Computational Chemistry 15, 488–506.

    Article  CAS  Google Scholar 

  45. Bottegoni, G., Rocchia, W., Recanatini, M., and Cavalli, A. (2006) AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets, Bioinformatics 22.

    Google Scholar 

  46. Lin, J. H., Perryman, A. L., Schames, J. R., and McCammon, J. A. (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme, J Am Chem Soc 124, 5632–5633.

    Article  PubMed  CAS  Google Scholar 

  47. Landon, M. R., Amaro, R. E., Baron, R., Ngan, C. H., Ozonoff, D., McCammon, J. A., and Vajda, S. (2008) Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble, Chem Biol Drug Des 71, 106–116.

    Article  PubMed  CAS  Google Scholar 

  48. Schames, J. R., Henchman, R. H., Siegel, J. S., Sotriffer, C. A., Ni, H., and McCammon, J. A. (2004) Discovery of a novel binding trench in HIV integrase, J Med Chem 47, 1879–1881.

    Article  PubMed  CAS  Google Scholar 

  49. Amaro, R. E., Baron, R., and McCammon, J. A. (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design, J Comput Aided Mol Des 22, 693–705.

    Article  PubMed  CAS  Google Scholar 

  50. Daura, X., Gademann, K., Jaun, B., Seebach, D., Van Gunsteren, W. F., and Mark, A. E. (1999) Peptide folding: When simulation meets experiment, Angewandte Chemie - International Edition 38, 236–240.

    Article  CAS  Google Scholar 

  51. Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., and Berendsen, H. J. C. (2005) GROMACS: Fast, flexible, and free, pp 1701-1718, Wiley Subscription Services, Inc., A Wiley Company.

    Google Scholar 

  52. de Hoon, M. J. L., Imoto, S., Nolan, J., and Miyano, S. (2004) Open source clustering software, Bioinformatics 20, 1453–1454.

    Article  PubMed  Google Scholar 

  53. Kaufman, L., and Rousseeuw, P. J. (1990) Finding Groups in Data: an Introduction to Cluster Analysis., Wiley, New York.

    Google Scholar 

  54. Hopkins, B. (1954) A new method for determining the type of distribution of plant individuals., Ann. Bot. 18, 213–227.

    Google Scholar 

  55. Kelley, L. A., Gardner, S. P., and Sutcliffe, M. J. (1997) An automated approach for defining core atoms and domains in an ensemble of NMR-derived protein structures, Protein Engineering 10, 737–741.

    Article  PubMed  CAS  Google Scholar 

  56. Cole, J. C., Murray, C. W., Nissink, J. W., Taylor, R. D., and Taylor, R. (2005) Comparing protein-ligand docking programs is difficult, Proteins 60, 325–332.

    Article  PubMed  CAS  Google Scholar 

  57. Hawkins, P. C., Warren, G. L., Skillman, A. G., and Nicholls, A. (2008) How to do an evaluation: pitfalls and traps, J Comput Aided Mol Des 22, 179–190.

    Article  PubMed  CAS  Google Scholar 

  58. Marcou, G., and Rognan, D. (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints, J Chem Inf Model 47, 195–207.

    Article  PubMed  CAS  Google Scholar 

  59. Abagyan, R., and Kufareva, I. (2009) The flexible pocketome engine for structural chemogenomics, Methods Mol Biol 575, 249–279.

    Article  PubMed  CAS  Google Scholar 

  60. Everitt, B. S., Landau, S., and Leese, M. (2001) Cluster analysis, Arnold, a member of the Hodder Headline Group, London.

    Google Scholar 

  61. Ward, J. H. J., and Hook, M. E. (1963) Application of a hierarchical grouping procedure to problem of grouping profiles, Educ. Psychol. Meas. 23, 69–92.

    Article  Google Scholar 

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Correspondence to Andrea Cavalli .

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Bottegoni, G., Rocchia, W., Cavalli, A. (2012). Application of Conformational Clustering in Protein–Ligand Docking. In: Baron, R. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 819. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-465-0_12

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