Abstract
Fragment-based drug discovery approaches allow for a greater coverage of chemical space and generally produce high efficiency ligands. As such, virtual and experimental fragment screening are increasingly being coupled in an effort to identify new leads for specific therapeutic targets. Fragment docking is employed to create target-focussed subset of compounds for testing along side generic fragment libraries. The utility of the program Glide with various scoring schemes for fragment docking is discussed. Fragment docking results for two test cases, prostaglandin D2 synthase and DNA ligase, are presented and compared to experimental screening data. Self-docking, cross-docking, and enrichment studies are performed. For the enrichment runs, experimental data exists indicating that the docking decoys in fact do not inhibit the corresponding enzyme being examined. Results indicate that even for difficult test cases fragment docking can yield enrichments significantly better than random.
Similar content being viewed by others
References
Howard S, Berdini V, Boulstridge JA, Carr MG, Cross DM, Curry J, Devine LA, Early TR, Fazal L, Gill AL, Heathcote M, Maman S, Matthews JE, McMenamin RL, Navarro EF, O’Brien MA, O’Reilly M, Rees DC, Reule M, Tisi D, Williams G, Vinkovic M, Wyatt PG (2009) Fragment-based discovery of the pyrazol-4-yl urea (AT9283), a multitargeted kinase inhibitor with potent aurora kinase activity. J Med Chem 52:379–388. doi:10.1021/jm800984v
Edwards PD, Albert JS, Sylvester M, Aharony D, Andisik D, Callaghan O, Campbell JB, Carr RA, Chessari G, Congreve M, Frederickson M, Folmer RH, Geschwindner S, Koether G, Kolmodin K, Krumrine J, Mauger RC, Murray CW, Olsson LL, Patel S, Spear N, Tian G (2007) Application of fragment-based lead generation to the discovery of novel, cyclic amidine beta-secretase inhibitors with nanomolar potency, cellular activity, and high ligand efficiency. J Med Chem 50:5912–5925. doi:10.1021/jm070829p
Geschwindner S, Olsson LL, Albert JS, Deinum J, Edwards PD, de Beer T, Folmer RH (2007) Discovery of a novel warhead against beta-secretase through fragment-based lead generation. J Med Chem 50:5903–5911. doi:10.1021/jm070825k
Albert JS, Blomberg N, Breeze AL, Brown AJ, Burrows JN, Edwards PD, Folmer RH, Geschwindner S, Griffen EJ, Kenny PW, Nowak T, Olsson LL, Sanganee H, Shapiro AB (2007) An integrated approach to fragment-based lead generation: philosophy, strategy and case studies from AstraZeneca’s drug discovery programmes. Curr Top Med Chem 7:1600–1629. doi:10.2174/156802607782341091
Erlanson DA, Wells JA, Braisted AC (2004) Tethering: fragment-based drug discovery. Annu Rev Biophys Biomol Struct 33:199–223. doi:10.1146/annurev.biophys.33.110502.140409
Hohwy M, Spadola L, Lundquist B, Hawtin P, Dahmen J, Groth-Clausen I, Nilsson E, Persdotter S, von Wachenfeldt K, Folmer RH, Edman K (2008) Novel prostaglandin D synthase inhibitors generated by fragment-based drug design. J Med Chem 51:2178–2186. doi:10.1021/jm701509k
Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6:29–40. doi:10.1038/nrd2201
Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431. doi:10.1016/S1359-6446(04)03069-7
Kuntz ID, Chen K, Sharp KA, Kollman PA (1999) The maximal affinity of ligands. Proc Natl Acad Sci USA 96:9997–10002. doi:10.1073/pnas.96.18.9997
Abad-Zapatero C, Metz JT (2005) Ligand efficiency indices as guideposts for drug discovery. Drug Discov Today 10:464–469. doi:10.1016/S1359-6446(05)03386-6
Carr RAE, Congreve M, Murray CW, Rees DC (2005) Fragment-based lead discovery: leads by design. Drug Discov Today 10:987–992. doi:10.1016/S1359-6446(05)03511-7
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. doi:10.1016/S0169-409X(96)00423-1
Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14:251–264. doi:10.1023/A:1008130001697
Oprea TI, Davis AM, Teague SJ, Leeson PD (2001) Is there a difference between leads and drugs? A historical perspective. J Chem Inf Comput Sci 41:1308–1315. doi:10.1021/ci010366a
Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623. doi:10.1021/jm020017n
Congreve M, Carr R, Murray C, Jhoti H (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877. doi:10.1016/S1359-6446(03)02831-9
Joseph-McCarthy D, Baber JC, Feyfant E, Thompson DC, Humblet C (2007) Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Dev 10:264–274
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430
Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759. doi:10.1021/jm030644s
Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J Med Chem 49:6177–6196. doi:10.1021/jm051256o
Hohwy M, Spadola L, Lundquist B, Hawtin P, Dahmén J, Groth-Clausen I, Nilsson E, Persdotter S, von Wachenfeldt K, Folmer R, Edman K (2008) Novel prostaglandin D synthase inhibitors generated by fragment-based drug design. J Med Chem 51:2178–2186. doi:10.1021/jm701509k
Aritake K, Kado Y, Inoue T, Miyano M, Urade Y (2006) Structural and functional characterization of HQL-79, an orally selective inhibitor of human hematopoietic prostaglandin D synthase. J Biol Chem 281:15277–15286. doi:10.1074/jbc.M506431200
Engler MJ, Richardson CC (1982) DNA ligases. In: Boyer PD (ed) The enzymes. Academic Press, Inc., New York, NY, pp 3–29
Kenny PW (2009) J Comput Aided Mol Des In (this issue)
Weininger D (1988) SMILES 1. Introduction and encoding rules. J Chem Inf Comput 28:31–36
Kenny PW, Sadowski J (2004) Structure modification in chemical databases. In: Opera TI (ed) Chemoinformatics in drug discovery. Wiley, Weinheim, pp 271–285
Lyne PD, Lamb ML, Saeh JC (2006) Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 49:4805–4808. doi:10.1021/jm060522a
Chen HM, Lyne PD, Giordanetto F, Lovell T, Li J (2006) Evaluating molecular-docking methods for pose prediction and enrichment factors. J Chem Inf Model 46:401–415. doi:10.1021/ci0503255
Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547. doi:10.1021/jm049092j
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940–3941. doi:10.1093/bioinformatics/bti623
Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553. doi:10.1021/jm050540c
Verdonk ML, Mortenson PN, Hall RJ, Hartshorn MJ, Murray CW (2008) Protein-ligand docking against non-native protein conformers. J Chem Inf Model 48:2214–2225. doi:10.1021/ci8002254
Graves AP, Shivakumar DM, Boyce SE, Jacobson MP, Case DA, Shoichet BK (2008) Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol 377:914–934. doi:10.1016/j.jmb.2008.01.049
Thompson DC, Humblet C, Joseph-McCarthy D (2008) Investigation of MM-PBSA rescoring of docking poses. J Chem Inf Model 48:1081–1091. doi:10.1021/ci700470c
Cummings MD, DesJarlais RL, Gibbs AC, Mohan V, Jaeger EP (2005) Comparison of automated docking programs as virtual screening tools. J Med Chem 48:962–976. doi:10.1021/jm049798d
Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931. doi:10.1021/jm050362n
Acknowledgments
We thank Joann Prescott-Roy, Rutger Folmer, Loredana Spadola, and Peter Kenny for their help in locating and curating data sets and Adam Shapiro for providing experimental data on ligase in advance of publication.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Kawatkar, S., Wang, H., Czerminski, R. et al. Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using Glide. J Comput Aided Mol Des 23, 527–539 (2009). https://doi.org/10.1007/s10822-009-9281-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-009-9281-4