Advertisement

Practices in Molecular Docking and Structure-Based Virtual Screening

  • Ricardo N. dos Santos
  • Leonardo G. Ferreira
  • Adriano D. Andricopulo
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)

Abstract

Drug discovery has evolved significantly over the past two decades. Progress in key areas such as molecular and structural biology has contributed to the elucidation of the three-dimensional structure and function of a wide range of biological molecules of therapeutic interest. In this context, the integration of experimental techniques, such as X-ray crystallography, and computational methods, such as molecular docking, has promoted the emergence of several areas in drug discovery, such as structure-based drug design (SBDD). SBDD strategies have been broadly used to identify, predict and optimize the activity of small molecules toward a molecular target and have contributed to major scientific breakthroughs in pharmaceutical R&D. This chapter outlines molecular docking and structure-based virtual screening (SBVS) protocols used to predict the interaction of small molecules with the phosphatidylinositol-bisphosphate-kinase PI3Kδ, which is a molecular target for hematological diseases. A detailed description of the molecular docking and SBVS procedures and an evaluation of the results are provided.

Key words

Autodock vina Drug discovery Molecular modeling Structure-based drug design X-ray crystallography 

Notes

Acknowledgments

We gratefully acknowledge financial support from the State of Sao Paulo Research Foundation (FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo), grants 2015/13667-9, 2013/25658-9, and 2013/07600-3.

References

  1. 1.
    Jin L, Wang W, Fang G (2014) Targeting protein-protein interaction by small molecules. Annu Rev Pharmacol Toxicol 54:435–456CrossRefPubMedGoogle Scholar
  2. 2.
    Blaney J (2012) A very short history of structure-based design: how did we get here and where do we need to go? J Comput Aided Mol Des 26:13–14CrossRefPubMedGoogle Scholar
  3. 3.
    Kinch MS, Hoyer DA (2015) History of drug development in four acts. Drug Discov Today 20:1163–1168CrossRefPubMedGoogle Scholar
  4. 4.
    Kalyaanamoorthy S, Chen YP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16:831–839CrossRefPubMedGoogle Scholar
  5. 5.
    Honarparvar B, Govender T, Maguire GE et al (2014) Integrated approach to structure-based enzymatic drug design: molecular modeling, spectroscopy, and experimental bioactivity. Chem Rev 114:493–537CrossRefPubMedGoogle Scholar
  6. 6.
    Eder J, Sedrani R, Wiesmann C (2014) The discovery of first-in-class drugs: origins and evolution. Nat Rev Drug Discov 13:577–587CrossRefPubMedGoogle Scholar
  7. 7.
    Shoichet BK, Kobilka BK (2012) Structure-based drug screening for G-protein-coupled receptors. Trends Pharmacol Sci 33:268–272CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Meng XY, Zhang HX, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kitchen DB, Decornez H, Furr JR et al (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949CrossRefPubMedGoogle Scholar
  10. 10.
    Ferreira LG, dos Santos RN, Oliva G et al (2015) Molecular docking and structure-based drug design strategies. Molecules 20:13384–13421CrossRefPubMedGoogle Scholar
  11. 11.
    Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24:149–164CrossRefPubMedGoogle Scholar
  12. 12.
    McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26:897–906CrossRefPubMedGoogle Scholar
  13. 13.
    Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428CrossRefPubMedGoogle Scholar
  14. 14.
    Gorelik B, Goldblum A (2008) High quality binding modes in docking ligands to proteins. Proteins 71:1373–1386CrossRefPubMedGoogle Scholar
  15. 15.
    Morris GM, Goodsell DS, Huey R, Olson AJ (1996) Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J Comput Aided Mol Des 10:293–304CrossRefPubMedGoogle Scholar
  16. 16.
    Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748CrossRefPubMedGoogle Scholar
  17. 17.
    Santos RN, Andricopulo AD (2013) Physics and its interfaces with medicinal chemistry and drug design. Braz J Phys 43:268–280CrossRefGoogle Scholar
  18. 18.
    Foloppe N, Hubbard R (2006) Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 13:3583–3608CrossRefPubMedGoogle Scholar
  19. 19.
    Huang SY, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions. Phys Chem Chem Phys 12:12899–12908CrossRefPubMedGoogle Scholar
  20. 20.
    Murray C, Auton TR, Eldridge MD (1998) Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. J Comput Aided Mol Des 12:503–519CrossRefPubMedGoogle Scholar
  21. 21.
    Huang SY, Zou X (2006) An iterative knowledge-based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27:1866–1875CrossRefPubMedGoogle Scholar
  22. 22.
    Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573CrossRefPubMedGoogle Scholar
  23. 23.
    Ruvinsky AM (2007) Role of binding entropy in the refinement of protein–ligand docking predictions: analysis based on the use of 11 scoring functions. J Comput Chem 28:1364–1372CrossRefPubMedGoogle Scholar
  24. 24.
    Lionta E, Spyrou G, Vassilatis DK, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14:1923–1938CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52:867–881CrossRefPubMedGoogle Scholar
  26. 26.
    Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Moura Barbosa AJ, Del Rio A (2012) Freely accessible databases of commercial compounds for high- throughput virtual screenings. Curr Top Med Chem 12:866–877CrossRefPubMedGoogle Scholar
  28. 28.
    Rose PW, Prlić A, Ali A et al (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D271–D281CrossRefPubMedGoogle Scholar
  29. 29.
    Valli M, dos Santos RN, Figueira LD et al (2013) Development of a natural products database from the biodiversity of Brazil. J Nat Prod 76:439–444CrossRefPubMedGoogle Scholar
  30. 30.
    Williams AJ (2008) Public chemical compound databases. Curr Opin Drug Discov Devel 11:393–404PubMedGoogle Scholar
  31. 31.
    Nicola G, Liu T, Gilson MK (2012) Public domain databases for medicinal chemistry. J Med Chem 55:6987–7002CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Williams A, Tkachenko V (2014) The Royal Society of Chemistry and the delivery of chemistry data repositories for the community. J Comput Aided Mol Des 28:1023–1030CrossRefPubMedGoogle Scholar
  33. 33.
    Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 31:455–461PubMedPubMedCentralGoogle Scholar
  35. 35.
    Pirhadib S, Sunseria J, Koes DR (2016) Open source molecular modeling. J Mol Graph Model 69:127–143CrossRefGoogle Scholar
  36. 36.
    O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera: a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefPubMedGoogle Scholar
  38. 38.
    Knight ZA, Gonzalez B, Feldman ME et al (2006) A pharmacological map of the PI3-K family defines a role for p110alpha in insulin signaling. Cell 125:733–747CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Wu P, Liu T, Hu Y (2009) PI3K inhibitors for cancer therapy: what has been achieved so far? Curr Med Chem 16:916–930CrossRefPubMedGoogle Scholar
  40. 40.
    Brana I, Siu LL (2012) Clinical development of phosphatidylinositol 3-kinase inhibitors for cancer treatment. BMC Med 10:161CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Wu M, Akinleye A, Zhu X (2013) Novel agents for chronic lymphocytic leukemia. J Hematol Oncol 6:36CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Graf SA, Gopal AK (2016) Idelalisib for the treatment of non-Hodgkin lymphoma. Expert Opin Pharmacother 17:265–274CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Greenwell BI, Flowers CR, Blum KA et al (2017) Clinical use of PI3K inhibitors in B-cell lymphoid malignancies: today and tomorrow. Expert Rev Anticancer Ther 17(3):271–279. https://doi.org/10.1080/14737140.2017.1285702 CrossRefPubMedGoogle Scholar
  44. 44.
    Somoza JR, Koditek D, Villaseñor AG et al (2015) Structural, biochemical, and biophysical characterization of idelalisib binding to phosphoinositide 3-kinase δ. J Biol Chem 290:8439–8446CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Davis AM, Teague SJ, Kleywegt GJ (2003) Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. Angew Chem Int Ed Engl 42:2718–2736CrossRefPubMedGoogle Scholar
  46. 46.
    Sastry GM, Adzhigirey M, Day T (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234CrossRefPubMedGoogle Scholar
  47. 47.
    Blundell TL, Jhoti H, Abell C (2002) High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1:45–54CrossRefPubMedGoogle Scholar
  48. 48.
    Moda TL, Torres LG, Carrara AE et al (2008) PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics 24:2270–2271CrossRefPubMedGoogle Scholar
  49. 49.
    Clark DE (2005) Computational prediction of ADMET properties: recent developments and future challenges. In: Dixon DA (ed) Annual reports in computational chemistry, vol 1. Elsevier, Amsterdam, pp 133–151CrossRefGoogle Scholar
  50. 50.
    Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204PubMedGoogle Scholar
  51. 51.
    Roberts BC, Mancera RL (2008) Ligand−protein docking with water molecules. J Chem Inf Model 48:397–408CrossRefPubMedGoogle Scholar
  52. 52.
    Kirchmair J, Spitzer GM, Liedl KR (2011) Consideration of water and solvation effects in virtual screening. In: Sotriffer C (ed) Virtual screening: principles, challenges, and practical guidelines. Wiley-VCH Verlag, WeinheimGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Físico-QuímicaUniversidade Estadual de Campinas (UNICAMP)CampinasBrazil
  2. 2.Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São CarlosUniversidade de São Paulo (USP)São CarlosBrazil

Personalised recommendations