AutoDock-GIST: Incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking

Molecules

Volumn 21, Issue 11, 2016, Pages

  Uehara, Shota ^a   Tanaka, Shigenori ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

AutoDock4; Binding site water; Grid inhomogeneous solvation theory (GIST); Protein ligand docking; Virtual screening

Indexed keywords

LIGAND; PROTEIN; PROTEIN BINDING; WATER;

BIOLOGY; CHEMISTRY; ENZYME ACTIVE SITE; METABOLISM; MOLECULAR DOCKING; MOLECULAR MODEL; PROCEDURES; THERMODYNAMICS;

CATALYTIC DOMAIN; COMPUTATIONAL BIOLOGY; LIGANDS; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEINS; THERMODYNAMICS; WATER;

EID: 84997817419     PISSN: None     EISSN: 14203049     Source Type: Journal    
DOI: 10.3390/molecules21111604     Document Type: Article

References (100)

1. Ladbury, J.E. Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem. Biol. 1996, 3, 973-980.
2. Poornima, C.S.; Dean, P.M. Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions. J. Comput. Aided Mol. Des. 1995, 9, 500-512.
3. Hummer, G. Molecular binding: Under water's influence. Nat. Chem. 2010, 2, 906-907.
4. Baron, R.; Setny, P.; McCammon, J.A. Water in Cavity - Ligand Recognition. J. Am. Chem. Soc. 2010, 132, 12091-12097.
5. Chung, E.; Henriques, D.; Renzoni, D.; Zvelebil, M.; Bradshaw, J.M.; Waksman, G.; Robinson, C.V.; Ladbury, J.E. Mass spectrometric and thermodynamic studies reveal the role of water molecules in complexes formed between SH2 domains and tyrosyl phosphopeptides. Structure 1998, 6, 1141-1151.
6. McPhalen, C.A.; James, M.N. Structural comparison of two serine proteinase-protein inhibitor complexes: Eglin-c-subtilisin Carlsberg and CI-2-subtilisin Novo. Biochemistry 1988, 27, 6582-6598.
7. Quiocho, F.A.; Wilson, D.K.; Vyas, N.K. Substrate specificity and affinity of a protein modulated by bound water molecules. Nature 1989, 340, 404-407.
8. Baron, R.; Setny, P.; Paesani, F. Water Structure, Dynamics, and Spectral Signatures: Changes upon model cavity-ligand recognition. J. Phys. Chem. B 2012, 116, 13774-13780.
9. Barillari, C.; Taylor, J.; Viner, R.; Essex, J.W. Classification of water molecules in protein binding sites. J. Am. Chem. Soc. 2007, 129, 2577-2587.
10. Michel, J.; Tirado-Rives, J.; Jorgensen, W.L. Prediction of the water content in protein binding sites. J. Phys. Chem. B 2009, 113, 13337-13346.
11. Raman, E.P.; Mackerell, A.D. Spatial analysis and quantification of the thermodynamic driving forces in protein-ligand binding: Binding site variability. J. Am. Chem. Soc. 2015, 137, 2608-2621.
12. Haider, K.; Huggins, D.J. Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules. J. Chem. Inf. Model. 2013, 53, 2571-2586.
13. Li, Z.; Lazaridis, T. Thermodynamics of buried water clusters at a protein-ligand binding interface. J. Phys. Chem. B 2006, 110, 1464-1475.
14. Chen, J.M.; Xu, S.L.; Wawrzak, Z.; Basarab, G.S.; Jordan, D.B. Structure-based design of potent inhibitors of scytalone dehydratase: Displacement of a water molecule from the active site. Biochemistry 1998, 37, 17735-17744.
15. Robinson, D.; Bertrand, T.; Carry, J.-C.; Halley, F.; Karlsson, A.; Mathieu, M.; Minoux, H.; Perrin, M.-A.; Robert, B.; Schio, L.; et al. Differential Water Thermodynamics Determine PI3K-Beta/Delta Selectivity for Solvent-Exposed Ligand Modifications. J. Chem. Inf. Model. 2016, 56, 886-894.
16. Ladbury, J.E.; Klebe, G.; Freire, E. Adding calorimetric data to decision making in lead discovery: A hot tip. Nat. Rev. Drug Discov. 2010, 9, 23-27.
17. Barandun, L.J.; Ehrmann, F.R.; Zimmerli, D.; Immekus, F.; Giroud, M.; Grunenfelder, C.; Schweizer, W.B.; Bernet, B.; Betz, M.; Heine, A.; et al. Replacement of water molecules in a phosphate binding site by furanoside-appended lin-benzoguanine ligands of tRNA-guanine transglycosylase (TGT). Chem. A Eur. J. 2015, 21, 126-135.
18. Biela, A.; Nasief, N.N.; Betz, M.; Heine, A.; Hangauer, D.; Klebe, G. Dissecting the hydrophobic effect on the molecular level: The role of water, enthalpy, and entropy in ligand binding to thermolysin. Angew. Chem. Int. Ed. 2013, 52, 1822-1828.
19. Betz, M.; Wulsdorf, T.; Krimmer, S.G.; Klebe, G. Impact of surface water layers on protein-ligand binding: How well are experimental data reproduced by molecular dynamics simulations in a thermolysin test case? J. Chem. Inf. Model. 2016, 56, 223-233.
20. Pearlstein, R.A.; Hu, Q.Y.; Zhou, J.; Yowe, D.; Levell, J.; Dale, B.; Kaushik, V.K.; Daniels, D.; Hanrahan, S.; Sherman, W.; et al. New hypotheses about the structure-function of proprotein convertase subtilisin/kexin type 9: Analysis of the epidermal growth factor-like repeat a docking site using watermap. Proteins Struct. Funct. Bioinform. 2010, 78, 2571-2586.
21. Pearlstein, R.A.; Sherman, W.; Abel, R. Contributions of water transfer energy to protein-ligand association and dissociation barriers: Watermap analysis of a series of p38α MAP kinase inhibitors. Proteins Struct. Funct. Bioinform. 2013, 81, 1509-1526.
22. Bortolato, A.; Tehan, B.G.; Bodnarchuk, M.S.; Essex, J.W.; Mason, J.S. Water network perturbation in ligand binding: Adenosine A 2A antagonists as a case study. J. Chem. Inf. Model. 2013, 53, 1700-1713.
23. Barillari, C.; Duncan, A.L.; Westwood, I.M.; Blagg, J.; van Montfort, R.L.M. Analysis of water patterns in protein kinase binding sites. Proteins Struct. Funct. Bioinform. 2011, 79, 2109-2121.
24. Imai, T.; Hiraoka, R.; Kovalenko, A.; Hirata, F. Locating missing water molecules in protein cavities by the three-dimensional reference interaction site model theory of molecular solvation. Proteins Struct. Funct. Bioinform. 2006, 66, 804-813.
25. Bayden, A.S.; Moustakas, D.T.; Joseph-McCarthy, D.; Lamb, M.L. Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP. J. Chem. Inf. Model. 2015, 55, 1552-1565.
26. Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-857.
27. Goodfellow, J.M.; Vovelle, F. Biomolecular energy calculations using transputer technology. Eur. Biophys. J. 1989, 17, 167-172.
28. Pitt, W.R.; Murray-Rust, J.; Goodfellow, J.M. AQUARIUS2: Knowledge-based modeling of solvent sites around proteins. J. Comput. Chem. 1993, 14, 1007-1018.
29. Verdonk, M.L.; Cole, J.C.; Taylor, R. SuperStar: A knowledge-based approach for identifying interaction sites in proteins. J. Mol. Biol. 1999, 289, 1093-1108.
30. 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.
31. Li, Z.; Lazaridis, T. Computing the Thermodynamic Contributions of Interfacial Water. Methods Mol. Biol. 2012, 819, 393-404.
32. Hu, B.; Lill, M.A. WATsite: Hydration site prediction program with PyMOL interface. J. Comput. Chem. 2014, 35, 1255-1260.
33. Czapiewski, D.; Zielkiewicz, J. Structural properties of hydration shell around various conformations of simple polypeptides. J. Phys. Chem. B 2010, 114, 4536-4550.
34. Henchman, R.H.; McCammon, J.A. Extracting hydration sites around proteins from explicit water simulations. J. Comput. Chem. 2002, 23, 861-869.
35. Lazaridis, T. Inhomogeneous fluid approach to solvation thermodynamics. 1. Theory. J. Phys. Chem. B 1998, 102, 3531-3541.
36. Lazaridis, T. Inhomogeneous fluid approach to solvation thermodynamics. 2. Applications to Simple Fluids. J. Phys. Chem. B 1998, 102, 3542-3550.
37. Nguyen, C.N.; Young, T.K.; Gilson, M.K. Grid inhomogeneous solvation theory: Hydration structure and thermodynamics of the miniature receptor Cucurbit [7] uril. J. Chem. Phys. 2012, 137, 149901.
38. Nguyen, C.N.; Cruz, A.; Gilson, M.K.; Kurtzman, T. Thermodynamics of water in an enzyme active site: Grid-based hydration analysis of coagulation factor xa. J. Chem. Theory Comput. 2014, 10, 2769-2780.
39. 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 Discov. 2004, 3, 935-949.
40. Meng, X.-Y.; Zhang, H.-X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des. 2011, 7, 146-157.
41. Yuriev, E.; Agostino, M.; Ramsland, P.A. Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit. 2011, 24, 149-164.
42. Okimoto, N.; Futatsugi, N.; Fuji, H.; Suenaga, A.; Morimoto, G.; Yanai, R.; Ohno, Y.; Narumi, T.; Taiji, M. High-performance drug discovery: Computational screening by combining docking and molecular dynamics simulations. PLoS Comput. Biol. 2009, 5, e1000528.
43. Graaf, D.C.; Pospisil, P.; Pos, W.; Folkers, G.; Vermeulen, N.P.E. Binding mode prediction of cytochrome P450 and thymidine kinase protein-ligand complexes by consideration of water and rescoring in automated docking. J. Med. Chem. 2005, 48, 2308-2318.
44. Huang, S.; Zou, X. Inclusion of solvation and entropy in the knowledge-based scoring function for protein - Ligand interactions. J. Chem. Inf. Model. 2010, 50, 262-273.
45. Kumar, A.; Zhang, K.Y.J. Investigation on the effect of key water molecules on docking performance in CSARdock exercise. J. Chem. Inf. Model. 2013, 53, 1880-1892.
46. Huang, N.; Shoichet, B.K. Exploiting ordered waters in molecular docking. J. Med. Chem. 2008, 51, 4862-4865.
47. Verdonk, M.L.; Chessari, G.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Nissink, J.W.M.; Taylor, R.D.; Taylor, R. Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem. 2005, 48, 6504-6515.
48. Forli, S.; Olson, A.J. A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. J. Med. Chem. 2012, 55, 623-638.
49. Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem. 2007, 28, 1145-1152.
50. Mysinger, M.M.; Shoichet, B.K. Rapid context-dependent ligand desolvation in molecular docking. J. Chem. Inf. Model. 2010, 50, 1561-1573.
51. Shoichet, B.K.; Leach, A.R.; Kuntz, I.D. Ligand solvation in molecular docking. Proteins Struct. Funct. Genet. 1999, 34, 4-16.
52. Alvarez-Garcia, D.; Barril, X. Molecular simulations with solvent competition quantify water displaceability and provide accurate interaction maps of protein binding sites. J. Med. Chem. 2014, 57, 8530-8539.
53. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 30, 2785-2791.
54. Goodsell, D.S.; Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins Struct. Funct. Genet. 1990, 8, 195-202.
55. Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 2012, 55, 6582-6594.
56. Young, T.; Hua, L.; Huang, X.; Abel, R.; Friesner, R.; Berne, B.J. Dewetting transitions in protein cavities. Proteins Struct. Funct. Bioinform. 2010, 78, 1856-1869.
57. Humphrey, W.; Dalke, A.; Schulten, K. VDM: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33-38.
58. Uehara, S.; Fujimoto, K.J.; Tanaka, S. Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli. Phys. Chem. Chem. Phys. 2015, 17, 16412-16417.
59. Chen, H.-M.; Liu, B.-F.; Huang, H.-L.; Hwang, S.-F.; Ho, S.-Y. SODOCK: Swarm optimization for highly flexible protein-ligand docking. J. Comput. Chem. 2007, 28, 612-623.
60. Santos-Martins, D.; Forli, S.; Ramos, M.J.; Olson, A.J. AutoDock4Zn: An improved AutoDock force field for small-molecule docking to zinc metalloproteins. J. Chem. Inf. Model. 2014, 54, 2371-2379.
61. Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 1998, 19, 1639-1662.
62. Weiner, S.J.; Kollman, P.A.; Case, D.A.; Singh, U.C.; Ghio, C.; Alagona, G.; Profeta, S.; Weinerl, P. A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc. 1984, 106, 765-784.
63. Huey, R.; Goodsell, D.; Morris, G.; Olson, A. Grid-based hydrogen bond potentials with improved directionality. Lett. Drug Des. Discov. 2004, 1, 178-183.
64. Mehler, E.L.; Solmajer, T. Electrostatic effects in proteins: Comparison of dielectric and charge models. Protein Eng. Des. Sel. 1991, 4, 903-910.
65. Stouten, P.F.W.; Frömmel, C.; Nakamura, H.; Sander, C. An Effective Solvation Term Based on Atomic Occupancies for Use in Protein Simulations. Mol. Simul. 1993, 10, 97-120.
66. Sgobba, M.; Caporuscio, F.; Anighoro, A.; Portioli, C.; Rastelli, G. Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur. J. Med. Chem. 2012, 58, 431-440.
67. Thompson, D.C.; Humblet, C.; Joseph-McCarthy, D. Investigation of MM-PBSA rescoring of docking poses. J. Chem. Inf. Model. 2008, 48, 1081-1091.
68. Sun, H.; Zhao, L.; Peng, S.; Huang, N. Incorporating replacement free energy of binding-site waters in molecular docking. Proteins Struct. Funct. Bioinform. 2014, 82, 1765-1776.
69. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242.
70. Adler, M.; Davey, D.D.; Phillips, G.B.; Kim, S.H.; Jancarik, J.; Rumennik, G.; Light, D.R.; Whitlow, M. Preparation, characterization, and the crystal structure of the inhibitor ZK-807834 (CI-1031) complexed with factor Xa. Biochemistry 2000, 39, 12534-12542.
71. Word, J.M.; Lovell, S.C.; Richardson, J.S.; Richardson, D.C. Asparagine and glutamine: Using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol. 1999, 285, 1735-1747.
72. Case, D.A.; Babin, V.; Berryman, J.T.; Betz, R.M.; Cai, Q.; Cerutti, D.S.; Cheatham, T.E., III; Darden, T.A.; Duke, R.E.; Gohlke, H.; et al. AMBER 14; University of California: San Francisco, CA, USA, 2014.
73. Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins Struct. Funct. Bioinform. 2006, 65, 712-725.
74. Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926-935.
75. Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J. Chem. Theory Comput. 2013, 9, 3878-3888.
76. Ryckaert, J.P.; Ciccotti, G.; Berendsen, H.J.C. Numerical integration of the Cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327-341.
77. Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577-8593.
78. Roe, D.R.; Cheatham, T.E., III. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular synamics trajectory data. J. Chem. Theory Comput. 2013, 9, 3084-3095.
79. Ramsey, S.; Nguyen, C.; Salomon-Ferrer, R.; Walker, R.C.; Gilson, M.K.; Kurtzman, T. Solvation thermodynamic mapping of molecular surfaces in AmberTools: GIST. J. Comput. Chem. 2016, 37, 2029-2037.
80. Liu, Z.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.; Nie, W.; Liu, Y.; Wang, R. PDB-wide collection of binding data: Current status of the PDBbind database. Bioinformatics 2014, 31, 405-412.
81. Pan, Y.; Huang, N.; Cho, S.; MacKerell, A.D. Consideration of molecular weight during compound selection in virtual target-based database screening. J. Chem. Inf. Comput. Sci. 2003, 43, 267-272.
82. Kim, R.; Skolnick, J. Assessment of programs for ligand binding affinity prediction. J. Comput. Chem. 2008, 29, 1316-1331.
83. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc.: Montreal, QC, Canada, 2016.
84. Triballeau, N.; Acher, F.; Brabet, I.; Pin, J.P.; Bertrand, H.O. 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. 2005, 48, 2534-2547.
85. Bender, A.; Glen, R.C. A discussion of measures of enrichment in virtual screening: Comparing the information content of descriptors with increasing levels of sophistication. J. Chem. Inf. Model. 2005, 45, 1369-1375.
86. Perzborn, E.; Roehrig, S.; Straub, A.; Kubitza, D.; Misselwitz, F. The discovery and development of rivaroxaban, an oral, direct factor Xa inhibitor. Nat. Rev. Drug Discov. 2011, 10, 61-75.
87. Rai, R.; Sprengeler, P.; Elrod, K.; Young, W. Perspectives on Factor Xa Inhibition. Curr. Med. Chem. 2001, 8, 101-119.
88. Lumry, R.; Rajender, S. Enthalpy-entropy compensation phenomena in water solutions of proteins and small molecules: A ubiquitous properly of water. Biopolymers 1970, 9, 1125-1227.
89. Meloun, M.; Ferenčíková, Z. Enthalpy-entropy compensation for some drugs dissociation in aqueous solutions. Fluid Phase Equilib. 2012, 328, 31-41.
90. Ahmad, M.; Helms, V.; Lengauer, T.; Kalinina, O.V. Enthalpy-entropy compensation upon molecular conformational changes. J. Chem. Theory Comput. 2015, 11, 1410-1418.
91. Plewczynski, D.; Łaźniewski, M.; Augustyniak, R.; Ginalski, K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem. 2011, 32, 742-755.
92. Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des. 2002, 16, 11-26.
93. 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.
94. Kadirvelraj, R.; Foley, B.L.; Dyekjær, J.D.; Woods, R.J. Involvement of water in carbohydrate-protein binding: concanavalin a revisited. J. Am. Chem. Soc. 2008, 130, 16933-16942.
95. Li, Z.; Lazaridis, T. Thermodynamic contributions of the ordered water molecule in HIV-1 protease. J. Am. Chem. Soc. 2003, 125, 6636-6637.
96. Michel, J.; Tirado-Rives, J.; Jorgensen, W.L. Energetics of displacing water molecules from protein binding sites: Consequences for ligand optimization. J. Am. Chem. Soc. 2009, 131, 15403-15411.
97. Baldwin, E.T.; Bhat, T.N.; Gulnik, S.; Liu, B.; Topol, I.A.; Kiso, Y.; Mimoto, T.; Mitsuya, H.; Erickson, J.W. Structure of HIV-1 protease with KNI-272, a tight-binding transition-state analog containing allophenylnorstatine. Structure 1995, 3, 581-590.
98. Blum, A.P.; Lester, H.A.; Dougherty, D.A. Nicotinic pharmacophore: The pyridine N of nicotine and carbonyl of acetylcholine hydrogen bond across a subunit interface to a backbone NH. Proc. Natl. Acad. Sci. USA 2010, 107, 13206-13211.
99. Adachi, M.; Ohhara, T.; Kurihara, K.; Tamada, T.; Honjo, E.; Okazaki, N.; Arai, S.; Shoyama, Y.; Kimura, K.; Matsumura, H.; et al. Structure of HIV-1 protease in complex with potent inhibitor KNI-272 determined by high-resolution X-ray and neutron crystallography. Proc. Natl. Acad. Sci. USA 2009, 106, 4641-4646.
100. Murphy, R.B.; Repasky, M.P.; Greenwood, J.R.; Tubert-Brohman, I.; Jerome, S.; Annabhimoju, R.; Boyles, N.A.; Schmitz, C.D.; Abel, R.; Farid, R.; et al. WScore: A flexible and accurate treatment of explicit water molecules in ligand-receptor docking. J. Med. Chem. 2016, 59, 4364-4384.