Performance Evaluation of Docking Programs- Glide, GOLD, AutoDock & SurflexDock, Using Free Energy Perturbation Reference Data: A Case Study of Fructose-1, 6-bisphosphatase-AMP Analogs

Author(s): K. Kumar Reddy, R.S. Rathore, P. Srujana, R.R. Burri, C. Ravikumar Reddy, M. Sumakanth, Pallu Reddanna*, M. Rami Reddy

Journal Name: Mini-Reviews in Medicinal Chemistry

Volume 20 , Issue 12 , 2020

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Graphical Abstract:


Background: The accurate ranking of analogs of lead molecules with respect to their estimated binding free energies to drug targets remains highly challenging in molecular docking due to small relative differences in their free energy values.

Methods: Free energy perturbation (FEP) method, which provides the most accurate relative binding free energy values were earlier used to calculate free energies of many ligands for several important drug targets including Fructose-1,6-BisphosPhatase (FBPase). The availability of abundant structural and experimental binding affinity data for FBPase inhibitors provided an ideal system to evaluate four widely used docking programs, AutoDock, Glide, GOLD and SurflexDock, distinct from earlier comparative evaluation studies.

Results: The analyses suggested that, considering various parameters such as docking pose, scoring and ranking accuracy, sensitivity analysis and newly introduced relative ranking score, Glide provided reasonably consistent results in all respects for the system studied in the present work. Whereas GOLD and AutoDock also demonstrated better performance, AutoDock results were found to be significantly superior in terms of scoring accuracy compared to the rest.

Conclusion: Present analysis serves as a useful guide for researchers working in the field of lead optimization and for developers in upgradation of the docking programs.

Keywords: AMP analogs, AutoDock, FBPase, Free energy perturbation, Glide, GOLD, Molecular docking, SurflexDock.

Merz, K.M.; Ringe, D.; Reynolds, C.H. Drug design: Structure and ligand-based approaches; Cambridge University Press: Boston, MA, 2010.
Reddy, M.R.; Erion, M.D. Relative binding affinities of fructose-1,6-bisphosphatase inhibitors calculated using a quantum mechanics-based free energy perturbation method. J. Am. Chem. Soc., 2007, 129(30), 9296-9297.
Rathore, R.S.; Reddy, R.N.; Kondapi, A.K.; Reddanna, P.; Reddy, M.R. Use of quantum mechanics/molecular mechanics-based FEP method for calculating relative binding affinities of FBPase inhibitors for type-2 diabetes. Theor. Chem. Acc., 2012, 131(2), 1096-1106.
Reddy, M.R.; Reddy, C.R.; Rathore, R.S.; Erion, M.D.; Aparoy, P.; Reddy, R.N.; Reddanna, P. Free Energies calculations to estimate ligand-binding affinities in structure-based drug design. Curr. Pharm. Des., 2014, 20(20), 3323-3337.
Rathore, R.S.; Sumakanth, M.; Reddy, M.S.; Reddanna, P.; Rao, A.A.; Erion, M.D.; Reddy, M.R. Advances in binding free energies calculations: QM/MM-based free energy perturbation method for drug design. Curr. Pharm. Des., 2013, 19(26), 4674-4686.
Williams-Noonan, B.J.; Yuriev, E.; Chalmers, D.K. Free energy methods in drug design: Prospects of “Alchemical Perturbation” in medicinal chemistry. J. Med. Chem., 2018, 61, 638-649.
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49, 6177-6196.
Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47, 1750-1759.
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shaw, D.E.; Shelley, M.; Perry, J.K.; Francis, P.; Shenkin, P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47, 1739-1749.
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDock-Tools4: Automated docking with selective receptor flexibility. J. Comp. Chem., 2009, 30(16), 2785-2791.
Jones, G.; Willett, P.; Glen, R.C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol., 1995, 245(1), 43-53.
Jain, A.N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 2003, 46(4), 499-511.
Torres, P.H.M.; Sodero, A.C.R.; Jofily, P.; Silva-Jr, F.P. Key topics in molecular docking for drug design. Int. J. Mol. Sci., 2019, 20(18), 4574.
Fan, J.; Fu, A.; Zhang, L. Progress in molecular docking. Quant. Biol., 2019, 7(2), 83-89.
Pinzi, L.; Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 2019, 20(18), 4331.
Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C.R. Towards the development of universal, fast and highly accurate docking/scoring methods: A long way to go. Br. J. Pharmacol., 2008, 153, S7-S26.
Plewczynski, D.; Lazniewski, M.; Augustyniak, R.; Ginalski, K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comp. Chem., 2011, 32, 742-755.
Cross, J.B.; Thompson, D.C.; Rai, B.K.; Baber, J.C.; Fan, K.Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J. Chem. Inf. Mod., 2009, 49(6), 1455-1474.
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Dis., 2004, 3(11), 935-949.
Kellenberger, E.; Rodrigo, J.; Muller, P.; Didier, R. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins: Struct. Funct., Bioinf., 2004, 57(2), 225-242.
Warren, G.L.; Andrews, C.W.; Capelli, A.M.; Clarke, B.; LaLonde, J.; Lambert, M.H.; Lindvall, M.; Nevins, N.; Semus, S.F.; Senger, S.; Tedesco, G.; Wall, I.D.; Woolven, J.M.; Peishoff, C.E.; Head, M.S. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49(20), 5912-5931.
Cole, J.C.; Murray, C.W.; Nissink, J.W.; Taylor, R.D.; Taylor, R. Comparing protein-ligand docking programs is difficult. Proteins: Struct. Funct., Bioinf., 2005, 60(3), 325-332.
Kontoyianni, M.; McClellan, L.M.; Sokol, G.S. Evaluation of docking performance: comparative data on docking algorithms. J. Med. Chem., 2004, 47(3), 558-565.
Chen, H.; Lyne, P.D.; Giordanetto, F.; Lovell, T.; Li, J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model., 2006, 46(1), 401-415.
Kim, R.; Skolnick, J. Assessment of programs for ligand binding affinity prediction. J. Comp. Chem., 2008, 29(8), 1316-1331.
Rapp, C.; Kalyanaraman, C.; Schiffmiller, A.; Schoenbrun, E.L.; Jacobson, M.P. A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. J. Chem. Inf. Model., 2011, 51(9), 2082-2089.
Grinter, S.Z.; Yan, C.; Huang, S.Y.; Jiang, L.; Zou, X. Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark. J. Chem. Inf. Model., 2013, 53(8), 1905-1914.
Damm-Ganamet, K.L.; Smith, R.D.; Dunbar, J.B., Jr; Stuckey, J.A.; Carlson, H.A. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J. Chem. Inf. Model., 2013, 53(8), 1853-1870.
Kirchmair, J.; Markt, P.; Distinto, S.; Wolber, G.; Langer, T. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection-What can we learn from earlier mistakes? J. Comput. Aided Mol. Des., 2008, 22(3-4), 213-228.
Kroemer, R.T.; Vulpetti, A.; McDonald, J.J.; Rohrer, D.C.; Trosset, J.Y.; Giordanetto, F.; Cotesta, S.; McMartin, C.; Kihlén, M.; Stouten, P.F.W. Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations. J. Chem. Inf. Comput. Sci., 2004, 44(3), 871-881.
Spitzer, R.; Jain, A.N. Surflex-Dock: Docking benchmarks and real-world application. J. Comput. Aided Mol. Des., 2012, 26(6), 687-699.
Erion, M.D.; Kasibhatla, S.R.; Bookser, B.C.; Poelje, V.P.D.; Reddy, M.R.; Gruber, H.E.; Appleman, J.R. Discovery of AMP Mimetics that exhibit high inhibitory potency and specificity for AMP deaminase. J. Am. Chem. Soc., 1999, 121(2), 308-319.
Erion, M.D.; Poelje, V.P.D.; Reddy, M.R. Computer-Assisted scanning of ligand interactions: Analysis of the fructose 1,6-bisphosphatase-AMP complex using free energy calculations. J. Am. Chem. Soc., 2000, 122(25), 6114-6115.
Reddy, M.R.; Erion, M.D. Calculation of relative binding free energy differences for fructose 1,6-bisphosphatase inhibitors using the thermodynamic cycle perturbation approach. J. Am. Chem. Soc., 2001, 123(26), 6246-6252.
Reddy, M.R.; Singh, U.C.; Erion, M.D. Development of a quantum mechanics-based free-energy perturbation method: use in the calculation of relative solvation free energies. J. Am. Chem. Soc., 2004, 126(20), 6224-6225.
Reddy, M.R.; Erion, M.D. Relative binding affinities of fructose-1,6-bisphosphatase inhibitors calculated using a quantum mechanics-based free energy perturbation method. J. Am. Chem. Soc., 2007, 129(30), 9296-9297.
Erion, M.D.; Dang, Q.; Reddy, M.R.; Kasibhatla, S.R.; Huang, J.; Lipscomb, W.N.; Poelje, V.P.D. Structure-guided design of AMP mimics that inhibit fructose-1,6-bisphosphatase with high affinity and specificity. J. Am. Chem. Soc., 2007, 129(50), 15480-15490.
Reddy, M.R.; Singh, U.C.; Erion, M.D. Ab initio quantum mechanics-based free energy perturbation method for calculating relative solvation free energies. J. Comp. Chem., 2007, 28(2), 491-494.
Reddy, M.R.; Singh, U.C.; Erion, M.D. Use of a QM/MM-based FEP method to evaluate the anomalous hydration behavior of simple alkyl amines and amides: application to the design of FBPase inhibitors for the treatment of type-2 diabetes. J. Am. Chem. Soc., 2011, 133(21), 8059-8061.
Rathore, R.S.; Aparoy, P.; Reddanna, P.; Kondapi, A.K.; Reddy, M.R. Minimum MD simulation length required to achieve reliable results in free energy perturbation calculations: case study of relative binding free energies of fructose-1,6-bisphosphatase inhibitors. J. Comp. Chem., 2011, 32(10), 2097-2103.
Schrodinger Suite 2011: Glide version 5.7, MacroModel, version 9.9, and Maestro, version 9.2 Schrödinger, LLC, New York, NY. 2011.
Ke, H.M.; Zhang, Y.P.; Lipscomb, W.N. Crystal structure of fructose-1,6-bisphosphatase complexed with fructose 6-phosphate, AMP, and magnesium. Proc. Natl. Acad. Sci. USA, 1990, 87(14), 5243-5247.
Webb, B.; Sali, A. Comparative protein structure modeling using MODELLER Curr. Protoc. Bioinformat., 2016, 54, 5.6.1-5.6.37.
Laskowski, R.A.; Rullmannn, J.A.; MacArthur, M.W.; Kaptein, R.; Thornton, J.M. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR, 1996, 8(4), 477-486.
Grace software and document.
Cho, A.E.; Guallar, V.; Berne, B.; Friesner, R.A. Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach. J. Comput. Chem., 2005, 26, 915-931.
Abel, R.; Young, T.; Farid, R.; Berne, B.J.; Friesner, R.A. Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J. Am. Chem. Soc., 2008, 130, 2817-2831.
Sherman, W.; Day, T.; Jacobson, M.P.; Friesner, R.A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem., 2006, 49, 534-553.
Holt, P.A.; Chaires, J.B.; Trent, J.O. Molecular docking of intercalators and groove-binders to nucleic acids using Autodock and Surflex. J. Chem. Inf. Model., 2008, 48, 1602-1615.

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Year: 2020
Published on: 23 July, 2020
Page: [1179 - 1187]
Pages: 9
DOI: 10.2174/1389557520666200526183353
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