Abstract
The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.
Similar content being viewed by others
References
Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Nat Rev Drug Discov 3(11):935
Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) J Med Chem 47(7):1750
Schneider G, Böhm H-J (2002) Drug Discov Today 7(1):64
Hawkins PCD, Skillman AG, Nicholls A (2007) J Med Chem 50(1):74
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK (2004) J Med Chem 47(7):1739
Brown N, Jacoby E (2006) Mini Rev Med Chem 6(11):1217
Mauser H, Guba W (2008) Curr Top Med Chem 11(3):365
Wang L, Deng Y, Wu Y, Kim B, LeBard DN, Wandschneider D, Beachy M, Friesner RA, Abel R (2016) J Chem Theory Comput 13(1):42
Hu Y, Stumpfe D, Bajorath Jr (2017) J Med Chem 60(4):1238
Jasial S, Hu Y, Bajorath J (2016) J Chem Inf Model 56(2):300
Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL (2015) J Chem Theory Comput 12(1):281
Vanommeslaeghe K, Raman EP, MacKerell AD Jr (2012) J Chem Inf Model 52(12):3155
Sherborne B, Shanmugasundaram V, Cheng AC, Christ CD, DesJarlais RL, Duca JS, Lewis RA, Loughney DA, Manas ES, McGaughey GB (2016) J Comp-Aided Mol Design 30(12):1139
Hu Y, Sherborne B, Lee T-S, Case DA, York DM, Guo Z (2016) J Comp-Aided Mol Design 30(7):533
Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Curr Opin Struct Biol 21(2):150
Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J (2015) J Am Chem Soc 137(7):2695
Wan S, Knapp B, Wright DW, Deane CM, Coveney PV (2015) J Chem Theory Comput 11(7):3346
Loeffler HH, Michel J, Woods C (2015) J Chem Inf Model 2485
Gapsys V, Michielssens S, Seeliger D, de Groot BL (2015) J Comput Chem 36(5):348
Homeyer N, Gohlke H (2013) J Comput Chem 34(11):965
Lee T, Hu Y, Sherborne B, Guo Z, York DM (2017) J Chem Theory Comput 13(7):3077
Crespo A, Rodriguez-Granillo A, Lim VT (2017) Curr Top Med Chem 17(23):2663
Huang M, Giese TJ, York DM (2015) J Comput Chem 36(18):1370
Giese TJ, Huang M, Chen H, York DM (2014) Acc Chem Res 47(9):2812
Richter HGF, Benson GM, Blum D, Chaput E, Feng S, Gardes C, Grether U, Hartman P, Kuhn B, Martin RE (2011) Bioorg Med Chem Lett 21(1):191
Richter HGF, Benson GM, Bleicher KH, Blum D, Chaput E, Clemann N, Feng S, Gardes C, Grether U, Hartman P (2011) Bioorg Med Chem Lett 21(4):1134
Feng S, Yang M, Zhang Z, Wang Z, Hong D, Richter H, Benson GM, Bleicher K, Grether U, Martin RE (2009) Bioorg Med Chem Lett 19(9):2595
Halgren TA (1999) J Comput Chem 20(7):720
Schrödinger (2014) Release 2014-1: MacroModel. Schrödinger, LLC, New York
Fradera X, Verras A, Hu Y, Wang D, Wang H, Fells J, Armacost K, Crespo A, Sherborne B, Wang H, Peng Z, Gao Y-D (2017) J Comp-Aided Mol Design. doi:10.1007/s10822-017-0053-2
Molecular Operating Environment (MOE). Chemical Computing Group Inc., Sherbooke St. West, Suite #910. Montreal
OpenEye Scientific Software, Inc. Fe Santa (2015) NM, http://www.eyesopen.com
Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) J Mol Biol 267(3):727
Wang R, Lai L, Wang S (2002) J Comp-Aided Mol Design 16(1):11
Wang R, Lu Y, Wang S (2003) J Med Chem 46(12):2287
Liu J, Wang R (2015) J Chem Inf Model 55(3):475
Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z, Wang R (2014) J Chem Inf Model 54(6):1700
POSIT 3.1.0.5: OpenEye Scientific Software, Santa Fe, NM, http://www.eyesopen.com
OMEGA 2.5.1.4: OpenEye Scientific Software, Santa Fe, NM, http://www.eyesopen.com. Hawkins, P.C.D.; Skillman, A.G.; Warren, G.L.; Ellingson, B.A.; Stahl, M.T.
ROCS 3.2.1.4: OpenEye Scientific Software, Santa Fe, NM, http://www.eyesopen.com
Schrödinger Release 2016-3: Jaguar, version 8.6, Schrödinger. LLC, New York, 2016
Crespo A, Scherlis DA, Marti MA, Ordejon P, Roitberg AE, Estrin DA (2003) J Phys Chem B 107(49):13728
Warshel A, Levitt M (1976) J Mol Biol 103(2):227
Tannor DJ, Marten B, Murphy R, Friesner RA, Sitkoff D, Nicholls A, Ringnalda M, Goddard WA, Honig B (1994) J Am Chem Soc 116(26):11875
Marten B, Kim K, Cortis C, Friesner RA, Murphy RB, Ringnalda MN, Sitkoff D, Honig B (1996) J Phys Chem 100(28):11775
Kojetin DJ, Burris TP (2013) Mol Pharmacol 83(1):1
Nettles KW, Bruning JB, Gil G, O’Neill EE, Nowak J, Hughs A, Kim Y, DeSombre ER, Dilis R, Hanson RN (2007) EMBO Rep 8(6):563
Jasial S, Hu Y, Bajorath Jr (2014.; 2016) Small-molecule drug discovery suite 2014-4: QSite, version 6.5, Schrödinger. LLC, New York
Murphy RB, Philipp DM, Friesner RA (2000) J Comput Chem 21(16):1442
Philipp DM, Friesner RA (1999) J Comput Chem 20(14):1468
Becke AD (1993) J Chem Phys 98(2):1372
Johnson BG, Gill PMW, Pople JA (1993) J Chem Phys 98(7):5612
Lee CT, Yang WT, Parr RG (1988) Phys Rev B 37(2):785
Banks JL, Beard HS, Cao YX, Cho AE, Damm W, Farid R, Felts AK, Halgren TA, Mainz DT, Maple JR, Murphy R, Philipp DM, Repasky MP, Zhang LY, Berne BJ, Friesner RA, Gallicchio E, Levy RM (2005) J Comput Chem 26(16):1752
Bochevarov AD, Harder E, Hughes TF, Greenwood JR, Braden DA, Philipp DM, Rinaldo D, Halls MD, Zhang J, Friesner RA (2013) Int J Quantum Chem 113(18):2110
Jakalian A, Bush BL, Jack DB, Bayly CI (2000) J Comput Chem 21(2):132
Jakalian A, Jack DB, Bayly CI (2002) J Comput Chem 23(16):1623
Miller MD, Kearsley SK, Underwood DJ, Sheridan RP (1994) J Comp-Aided Mol Design 8(2):153
Biggadike K, Bledsoe RK, Coe DM, Cooper TWJ, House D, Iannone MA, Macdonald SJF, Madauss KP, McLay IM, Shipley TJ (2009) Proc Natl Acad Sci USA 106(43):18114
Luchko T, Gusarov S, Roe DR, Simmerling C, Case DA, Tuszynski J, Kovalenko A (2010) J Chem Theory Comput 6(3):607
Kovalenko A, Hirata F (1999) J Chem Phys 110(20):10095
Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) J Am Chem Soc 130(9):2817
Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Proc Natl Acad Sci USA 104(3):808
Mi L-Z, Devarakonda S, Harp JM, Han Q, Pellicciari R, Willson TM, Khorasanizadeh S, Rastinejad F (2003) Mol Cell 11(4):1093
Acknowledgements
The authors would like to thank the following people for efforts, expertise and helpful discussions: Symon Gathiaka and Robert P. Sheridan. We are grateful to Merck & Co., Inc., Kenilworth, NJ USA Postdoctoral Research Fellows Program for financial support to Y. H. and the technical support from the High Performance Computing (HPC) group at Merck & Co., Inc., Kenilworth, NJ USA.
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Gao, YD., Hu, Y., Crespo, A. et al. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort. J Comput Aided Mol Des 32, 129–142 (2018). https://doi.org/10.1007/s10822-017-0072-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-017-0072-z