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.
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References
** L, Wang W, Fang G (2014) Targeting protein-protein interaction by small molecules. Annu Rev Pharmacol Toxicol 54:435–456
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 26:13–14
Kinch MS, Hoyer DA (2015) History of drug development in four acts. Drug Discov Today 20:1163–1168
Kalyaanamoorthy S, Chen YP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16:831–839
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 114:493–537
Eder J, Sedrani R, Wiesmann C (2014) The discovery of first-in-class drugs: origins and evolution. Nat Rev Drug Discov 13:577–587
Shoichet BK, Kobilka BK (2012) Structure-based drug screening for G-protein-coupled receptors. Trends Pharmacol Sci 33:268–272
Meng XY, Zhang HX, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157
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 3:935–949
Ferreira LG, dos Santos RN, Oliva G et al (2015) Molecular docking and structure-based drug design strategies. Molecules 20:13384–13421
Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24:149–164
McGann M (2012) FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des 26:897–906
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 15:411–428
Gorelik B, Goldblum A (2008) High quality binding modes in docking ligands to proteins. Proteins 71:1373–1386
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 10:293–304
Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748
Santos RN, Andricopulo AD (2013) Physics and its interfaces with medicinal chemistry and drug design. Braz J Phys 43:268–280
Foloppe N, Hubbard R (2006) Towards predictive ligand design with free-energy based computational methods? Curr Med Chem 13:3583–3608
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 12:12899–12908
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 12:503–519
Huang SY, Zou X (2006) An iterative knowledge-based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27:1866–1875
Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573
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 28:1364–1372
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 14:1923–1938
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 52:867–881
Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139
Moura Barbosa AJ, Del Rio A (2012) Freely accessible databases of commercial compounds for high- throughput virtual screenings. Curr Top Med Chem 12:866–877
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–D281
Valli M, dos Santos RN, Figueira LD et al (2013) Development of a natural products database from the biodiversity of Brazil. J Nat Prod 76:439–444
Williams AJ (2008) Public chemical compound databases. Curr Opin Drug Discov Devel 11:393–404
Nicola G, Liu T, Gilson MK (2012) Public domain databases for medicinal chemistry. J Med Chem 55:6987–7002
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 28:1023–1030
Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768
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 31:455–461
Pirhadib S, Sunseria J, Koes DR (2016) Open source molecular modeling. J Mol Graph Model 69:127–143
O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33
Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera: a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612
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 125:733–747
Wu P, Liu T, Hu Y (2009) PI3K inhibitors for cancer therapy: what has been achieved so far? Curr Med Chem 16:916–930
Brana I, Siu LL (2012) Clinical development of phosphatidylinositol 3-kinase inhibitors for cancer treatment. BMC Med 10:161
Wu M, Akinleye A, Zhu X (2013) Novel agents for chronic lymphocytic leukemia. J Hematol Oncol 6:36
Graf SA, Gopal AK (2016) Idelalisib for the treatment of non-Hodgkin lymphoma. Expert Opin Pharmacother 17:265–274
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
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 290:8439–8446
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 42:2718–2736
Sastry GM, Adzhigirey M, Day T (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234
Blundell TL, Jhoti H, Abell C (2002) High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1:45–54
Moda TL, Torres LG, Carrara AE et al (2008) PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics 24:2270–2271
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 133–151
Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204
Roberts BC, Mancera RL (2008) Ligand−protein docking with water molecules. J Chem Inf Model 48:397–408
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, Weinheim
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.
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dos Santos, R.N., Ferreira, L.G., Andricopulo, A.D. (2018). Practices in Molecular Docking and Structure-Based Virtual Screening. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_3
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DOI: https://doi.org/10.1007/978-1-4939-7756-7_3
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