Molecular docking: A powerful approach for structure-based drug discovery

Current Computer-Aided Drug Design

Volumn 7, Issue 2, 2011, Pages 146-157

  Meng, Xuan Yu ^a,b   Zhang, Hong Xing ^a   Mezei, Mihaly ^c   Cui, Meng ^b  

^a JILIN UNIVERSITY   (China)

^b VIRGINIA COMMONWEALTH UNIVERSITY   (United States)

^c MOUNT SINAI SCHOOL OF MEDICINE   (United States)

Author keywords

Backbone flexibility; Conformational sampling; Flexible protein ligand docking; Local move monte carlo sampling; Scoring

Indexed keywords

MOLECULAR MODELING;

BACKBONE FLEXIBILITY; CONFORMATIONAL SAMPLINGS; DRUG DISCOVERY; FLEXIBLE PROTEIN-LIGAND DOCKING; LOCAL MOVE MONTE CARLO; LOCAL MOVE MONTE CARLO SAMPLING; MOLECULAR DOCKING; MONTE CARLO SAMPLING; PROTEIN-LIGAND DOCKING; SCORING;

MONTE CARLO METHODS;

CIPROFLOXACIN; DNA TOPOISOMERASE (ATP HYDROLYSING); GYRASE INHIBITOR; LIGAND; NOVOBIOCIN; PROTEIN; RECEPTOR;

ALGORITHM; ARTICLE; BINDING AFFINITY; COMPUTATIONAL FLUID DYNAMICS; COMPUTER MODEL; CRYSTAL STRUCTURE; DATA ANALYSIS SOFTWARE; DRUG DESIGN; DRUG METABOLISM; ENTROPY; GENETIC ALGORITHM; HYDROPHOBICITY; LIPOPHILICITY; LOCAL MOVE MONTE CARLO; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MOLECULAR MECHANICS; MOLECULE; MONTE CARLO METHOD; PROTEIN CONFORMATION; PROTEIN TARGETING; SCORING SYSTEM;

EID: 79959224583     PISSN: 15734099     EISSN: 18756697     Source Type: Journal    
DOI: 10.2174/157340911795677602     Document Type: Article

References (138)

1. Jorgensen, W.L. The many roles of computation in drug discovery. Science, 2004, 303, 1813-1818.
2. Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov., 2002, 1, 882-894.
3. Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual screening-an overview. Drug Discov. Today, 1998, 3, 160-178
4. Langer, T.; Hoffmann, R.D. Virtual screening: an effective tool for lead structure discovery? Curr. Pharm. Des., 2001, 7, 509-527.
5. 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.
6. Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. Engl., 2002, 41, 2644-2676.
7. 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(Suppl 1), S7-26.
8. Shoichet, B.K.; McGovern, S.L.; Wei, B.; Irwin, J.J. In: Hits, leads and artifacts from virtual and high throughput screening., Molecular Informatics: Confronting Complexity, 2002.
9. Bailey, D.; Brown, D. High-throughput chemistry and structure-based design: survival of the smartest. Drug Discov. Today, 2001, 6, 57-59.
10. Kuntz, I.D.; Blaney, J.M.; Oatley, S.J.; Langridge, R.; Ferrin, T.E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol., 1982, 161, 269-288.
11. Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins, 2002, 47, 409-443.
12. Coupez, B.; Lewis, R.A. Docking and scoring--theoretically easy, practically impossible? Curr. Med. Chem., 2006, 13, 2995-3003.
13. Kontoyianni, M.; Madhav, P.; Suchanek, E.; Seibel, W. Theoretical and practical considerations in virtual screening: a beaten field? Curr. Med. Chem., 2008, 15, 107-116.
14. Brooijmans, N.; Kuntz, I.D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32, 335-373.
15. ten Brink, T.; Exner, T.E. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J. Chem. Inf. Model., 2009, 49, 1535-1546.
16. 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. Model., 2009, 49, 1455-1474.
17. Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J. Comput. Chem., 2010, 31, 2109-2125.
18. Plewczynski, D.; Lazniewski, M.; Augustyniak, R.; Ginalski, K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem., 2010 doi: 10.1002/jcc.21643.
19. McConkey, B.J.; Sobolev, V.; Edelman, M. The performance of current methods in ligand-protein docking. Curr. Sci., 2002, 83, 845-855.
20. Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28, 849-857.
21. Kastenholz, M.A.; Pastor, M.; Cruciani, G.; Haaksma, E.E.; Fox, T. GRID/CPCA: a new computational tool to design selective ligands. J. Med. Chem., 2000, 43, 3033-3044.
22. Levitt, D.G.; Banaszak, L.J. POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph., 1992, 10, 229-234.
23. Laskowski, R.A. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J. Mol. Graph., 1995, 13, 323-330, 307-328.
24. Glaser, F.; Morris, R.J.; Najmanovich, R.J.; Laskowski, R.A.; Thornton, J.M. A method for localizing ligand binding pockets in protein structures. Proteins, 2006, 62, 479-488.
25. Brady, G.P. Jr.; Stouten, P.F. Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des., 2000, 14, 383-401.
26. Mezei, M. A new method for mapping macromolecular topography. J. Mol. Graph. Model., 2003, 21, 463-472.
27. Fischer, E. Einfluss der configuration auf die wirkung derenzyme. Ber. Dt. Chem. Ges., 1894, 27, 2985-2993.
28. Koshland, D.E. Jr. Correlation of structure and function in enzyme action. Science, 1963, 142, 1533-1541.
29. Hammes, G.G. Multiple conformational changes in enzyme catalysis. Biochemistry, 2002, 41, 8221-8228.
30. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261, 470-489.
31. 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.
32. Jones, G.; Willett, P.; Glen, R.C.; Leach, A.R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 1997, 267, 727-748.
33. Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; 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.
34. McGann, M.R.; Almond, H.R.; Nicholls, A.; Grant, J.A.; Brown, F.K. Gaussian docking functions. Biopolymers, 2003, 68, 76-90.
35. Perola, E.; Walters, W.P.; Charifson, P.S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins, 2004, 56, 235-249.
36. 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.
37. Jiang, F.; Kim, S.H. "Soft docking": matching of molecular surface cubes. J. Mol. Biol., 1991, 219, 79-102.
38. Claussen, H.; Buning, C.; Rarey, M.; Lengauer, T. FlexE: efficient molecular docking considering protein structure variations. J. Mol. Biol., 2001, 308, 377-395.
39. Alonso, H.; Bliznyuk, A.A.; Gready, J.E. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev., 2006, 26, 531-568.
40. Sander, T.; Liljefors, T.; Balle, T. Prediction of the receptor conformation for iGluR2 agonist binding: QM/MM docking to an extensive conformational ensemble generated using normal mode analysis. J. Mol. Graph. Model., 2008, 26, 1259-1268.
41. Subramanian, J.; Sharma, S.; B-Rao, C. A novel computational analysis of ligand-induced conformational changes in the ATP binding sites of cyclin dependent kinases. J. Med. Chem., 2006, 49, 5434-5441.
42. Subramanian, J.; Sharma, S.; B-Rao, C. Modeling and selection of flexible proteins for structure-based drug design: backbone and side chain movements in p38 MAPK. Chem. Med. Chem., 2008, 3, 336-344.
43. Brint, A.T.; Willett, P. Algorithms for the identification of three-dimensional maximal common substructures. J. Chem. Inf. Comput. Sci., 1987, 27, 152-158.
44. Fischer, D.; Norel, R.; Wolfson, H.; Nussinov, R. Surface motifs by a computer vision technique: searches, detection, and implications for protein-ligand recognition. Proteins, 1993, 16, 278-292.
45. Norel, R.; Fischer, D.; Wolfson, H.J.; Nussinov, R. Molecular surface recognition by a computer vision-based technique. Protein Eng., 1994, 7, 39-46.
46. Miller, M.D.; Kearsley, S.K.; Underwood, D.J.; Sheridan, R.P. FLOG: a system to select 'quasi-flexible' ligands complementary to a receptor of known three-dimensional structure. J. Comput. Aided Mol. Des., 1994, 8, 153-174.
47. Diller, D.J.; Merz, K.M., Jr. High throughput docking for library design and library prioritization. Proteins, 2001, 43, 113-124.
48. Burkhard, P.; Taylor, P.; Walkinshaw, M.D. An example of a protein ligand found by database mining: description of the docking method and its verification by a 2.3 A X-ray structure of a thrombin-ligand complex. J. Mol. Biol., 1998, 277, 449-466.
49. DesJarlais, R.L.; Sheridan, R.P.; Dixon, J.S.; Kuntz, I.D.; Venkataraghavan, R. Docking flexible ligands to macromolecular receptors by molecular shape. J. Med. Chem., 1986, 29, 2149-2153.
50. Kuntz, I.D.; Leach, A.R. Conformational analysis of flexible ligands in macromolecular receptor sites. J. Comput. Chem., 1992, 13, 730-748
51. Ewing, T.J.; Makino, S.; Skillman, A.G.; Kuntz, I.D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des., 2001, 15, 411-428.
52. Welch, W.; Ruppert, J.; Jain, A.N. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem. Biol., 1996, 3, 449-462.
53. Schnecke, V.; Kuhn, L.A. Virtual Screening with solvation and ligand-induced complementarity. Persp. Drug Discov. Des., 2000, 20, 171-190.
54. Zsoldos, Z.; Reid, D.; Simon, A.; Sadjad, B.S.; Johnson, A.P. eHiTS: an innovative approach to the docking and scoring function problems. Curr. Protein Pept. Sci., 2006, 7, 421-435.
55. Miranker, A.; Karplus, M. Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins, 1991, 11, 29-34.
56. Eisen, M.B.; Wiley, D.C.; Karplus, M.; Hubbard, R.E. HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins, 1994, 19, 199-221.
57. Bohm, H.J. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des., 1992, 6, 593-606.
58. Goodsell, D.S.; Lauble, H.; Stout, C.D.; Olson, A.J. Automated docking in crystallography: analysis of the substrates of aconitase. Proteins, 1993, 17, 1-10.
59. Hart, T.N.; Read, R.J. A multiple-start Monte Carlo docking method. Proteins, 1992, 13, 206-222.
60. Goodsell, D.S.; Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins, 1990, 8, 195-202.
61. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM-A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem., 1994, 15, 488-506.
62. McMartin, C.; Bohacek, R.S. QXP: powerful, rapid computer algorithms for structure-based drug design. J. Comput. Aided Mol. Des., 1997, 11, 333-344.
63. Accelrys Inc., San Diego, CA, USA.
64. Oshiro, C.M.; Kuntz, I.D.; Dixon, J.S. Flexible ligand docking using a genetic algorithm. J. Comput. Aided Mol. Des., 1995, 9, 113-130.
65. Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins, 2003, 52, 609-623.
66. Clark, K.P.; Ajay, Flexible ligand docking without parameter adjustment across four ligand-receptor complexes. J. Comput. Chem., 1995, 16, 1210-1226.
67. Taylor, J.S.; Burnett, R.M. DARWIN: a program for docking flexible molecules. Proteins, 2000, 41, 173-191.
68. Cornell, W.D.; Cieplak, P.; Bayly, C.I.; Gould, I.R.; Merz, K.M.; Ferguson, D.M.; Spellmeyer, D.C.; Fox, T.; Caldwell, J.W.; Kollman, P.A. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc., 1995, 117, 5179-5197.
69. Weiner, S.J.; Kollman, P.A.; Case, D.A.; Singh, U.C.; Ghio, C.; Alagona, G.; Profeta, S. Jr.; Weiner, P. New force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc., 1984, 106, 765-784.
70. Brooks, B.R.; Bruccoleri, R.E.; Olafson, B.D.; States, D.J.; Swaminathan, S.; Karplus, M. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem., 1983, 4, 187-217.
71. Kollman, P.A. Free energy calculations: Applications to chemical and biochemical phenomena. Chem. Rev., 1993, 93 2395-2417.
72. Aqvist, J.; Luzhkov, V.B.; Brandsdal, B.O. Ligand binding affinities from MD simulations. Acc. Chem. Res., 2002, 35, 358-365.
73. Carlson, H.A.; Jorgensen, W.L. An extended linear response method for determining free energies of hydration. J. Phys. Chem., 1995 99, 10667-10673.
74. Shoichet, B.K.; Stroud, R.M.; Santi, D.V.; Kuntz, I.D.; Perry, K.M. Structure-based discovery of inhibitors of thymidylate synthase. Science, 1993, 259, 1445-1450.
75. Michel, J.; Verdonk, M.L.; Essex, J.W. Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? J. Med. Chem., 2006, 49, 7427-7439.
76. Briggs, J.M.; Marrone, T.J.; McCammon, J.A. Computational science new horizons and relevance to pharmaceutical design. Trends Cardiovasc. Med., 1996, 6, 198-206.
77. Bohm, H.J. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J. Comput. Aided Mol. Des., 1998, 12, 309-323.
78. Gehlhaar, D.K.; Verkhivker, G.M.; Rejto, P.A.; Sherman, C.J.; Fogel, D.B.; Fogel, L.J.; Freer, S.T. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem. Biol., 1995, 2, 317-324.
79. Verkhivker, G.M.; Bouzida, D.; Gehlhaar, D.K.; Rejto, P.A.; Arthurs, S.; Colson, A.B.; Freer, S.T.; Larson, V.; Luty, B.A.; Marrone, T.; Rose, P.W. Deciphering common failures in molecular docking of ligand-protein complexes. J. Comput. Aided Mol. Des., 2000, 14, 731-751.
80. Jain, A.N. Scoring non-covalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J. Comput. Aided Mol. Des., 1996, 10, 427-440.
81. Head, R.D.; Smythe, M.L.; Oprea, T.I.; Waller, C.L.; Green, S.M.; Marshall, G.R. VALIDATE: a new method for the receptor-based prediction of binding affinities of novel ligands. J. Am. Chem. Soc., 1996, 118, 3959-3969.
82. Gehlhaar, D.K.; Moerder, K.E.; Zichi, D.; Sherman, C.J.; Ogden, R.C.; Freer, S.T. De novo design of enzyme inhibitors by Monte Carlo ligand generation. J. Med. Chem., 1995, 38, 466-472.
83. Eldridge, M.D.; Murray, C.W.; Auton, T.R.; Paolini, G.V.; Mee, R.P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des., 1997, 11, 425-445.
84. Muegge, I.; Martin, Y.C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem., 1999, 42, 791-804.
85. Mitchell, J.B.O.; Laskowski, R.A.; Alex, A.; Thornton, J.M. Bleep-potential of mean force describing protein-ligand interactions: I. generating potential. J. Comput. Chem., 1999, 20, 1165-1176.
86. Ishchenko, A.V.; Shakhnovich, E.I. SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. J. Med. Chem., 2002, 45, 2770-2780.
87. Feher, M.; Deretey, E.; Roy, S. BHB: a simple knowledge-based scoring function to improve the efficiency of database screening. J. Chem. Inf. Comput. Sci., 2003, 43, 1316-1327
88. Verkhivker, G.; Appelt, K.; Freer, S.T.; Villafranca, J.E. Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. Protein Eng., 1995, 8, 677-691.
89. Wallqvist, A.; Jernigan, R.L.; Covell, D.G. A preference-based free-energy parameterization of enzyme-inhibitor binding. Applications to HIV-1-protease inhibitor design. Protein Sci., 1995, 4, 1881-1903.
90. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol., 2000, 295, 337-356.
91. DeWitte, R.S.; Shakhnovich, E.I., SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. methodology and supporting evidence. J. Am. Chem. Soc., 1996, 118, 11733-11744.
92. Charifson, P.S.; Corkery, J.J.; Murcko, M.A.; Walters, W.P. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 1999, 42, 5100-5109.
93. Feher, M. Consensus scoring for protein-ligand interactions. Drug Discov. Today, 2006, 11, 421-428.
94. Clark, R.D.; Strizhev, A.; Leonard, J.M.; Blake, J.F.; Matthew, J.B. Consensus scoring for ligand/protein interactions. J. Mol. Graph. Model., 2002, 20, 281-295.
95. Srinivasan, J.; Cheatham, T.E.; Cieplak, P.; Kollman, P.A.; Case, D.A. Continuum solvent studies of the stability of DNA, RNA, and phosphoramidateâˆ'DNA helices. J. Am. Chem. Soc., 1998, 120, 9401-9409.
96. Kollman, P.A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D.A.; Cheatham, T.E. 3rd. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res., 2000, 33, 889-897.
97. Still, W.C.; Tempczyk, A.; Hawley, R.C.; Hendrickson, T. Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc., 1990, 112, 6127-6129.
98. Guimaraes, C.R.; Mathiowetz, A.M. Addressing limitations with the MM-GB/SA scoring procedure using the WaterMap method and free energy perturbation calculations. J. Chem. Inf. Model., 50, 547-559.
99. Singh, N.; Warshel, A. Absolute binding free energy calculations: on the accuracy of computational scoring of protein-ligand interactions. Proteins, 2010, 78, 1705-1723.
100. Gabb, H.A.; Jackson, R.M.; Sternberg, M.J. Modelling protein docking using shape complementarity, electrostatics and biochemical information. J. Mol. Biol., 1997, 272, 106-120.
101. Bron, C.; Kerbosch, J. Algorithm 457: finding all cliques of an undirected graph. Commun. ACM, 1973, 16, 575-576.
102. Meng, E.C.; Shoichet, B.K.; Kuntz, I.D. Automated docking with grid-based energy evaluation. J. Comput. Chem., 1992, 13, 505-524.
103. Zou, X.Q.; Sun, Y.; Kuntz, I.D. Inclusion of solvation in ligand binding free energy calculations using the generalized-born model. J. Am. Chem. Soc., 1999, 121, 8033-8043.
104. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31, 455-461.
105. Bohm, H.J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des., 1994, 8, 243-256.
106. Teague, S.J. Implications of protein flexibility for drug discovery. Nat. Rev. Drug Discov., 2003, 2, 527-541.
107. Gschwend, D.A.; Good, A.C.; Kuntz, I.D. Molecular docking towards drug discovery. J. Mol. Recognit., 1996, 9, 175-186.
108. Totrov, M.; Abagyan, R. Protein-ligand docking as an energy optimization problem. In: Drug-receptor thermodynamics: Introduction and experimental applications; Raffa, R. B., Ed. John Wiley & Sons: New York, 2001; pp. 603-624.
109. Leach, A.R. Ligand docking to proteins with discrete side-chain flexibility. J. Mol. Biol., 1994, 235, 345-356.
110. Desmet, J.; De Maeyer, M.; Hazes, B.; Lasters, I. The dead-end elimination theorem and its use in protein sidechain positioning. Nature, 1992, 356, 539-542
111. Abagyan, R.; Totrov, M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol., 1994, 235, 983-1002.
112. 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.
113. Knegtel, R. M.; Kuntz, I. D.; Oshiro, C. M., Molecular docking to ensembles of protein structures. J. Mol. Biol., 1997, 266, 424-440.
114. Carlson, H.A.; Masukawa, K.M.; Rubins, K.; Bushman, F.D.; Jorgensen, W.L.; Lins, R.D.; Briggs, J.M.; McCammon, J. A., Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem., 2000, 43, 2100-2114.
115. Cavasotto, C.N.; Abagyan, R.A. Protein flexibility in ligand docking and virtual screening to protein kinases. J. Mol. Biol., 2004, 337, 209-225.
116. Derreumaux, P.; Schlick, T. The loop opening/closing motion of the enzyme triosephosphate isomerase. Biophys. J., 1998, 74, 72-81.
117. Zeng, L.; Zhou, M.M. Bromodomain: an acetyl-lysine binding domain. FEBS Lett., 2002, 513, 124-128.
118. Venkitakrishnan, R.P.; Zaborowski, E.; McElheny, D.; Benkovic, S.J.; Dyson, H.J.; Wright, P.E. Conformational changes in the active site loops of dihydrofolate reductase during the catalytic cycle. Biochemistry, 2004, 43, 16046-16055.
119. Go, N.; Scheraga, H.A. Ring closure and local conformational deformations of chain molecules. Macromolecules, 1970, 3, 178-187.
120. Dodd, L.R.; Boone, T.D.; Theodorou, D.N. A concerted rotation algorithm for atomistic Monte Carlo simulation of polymer melts and glasses. Mol. Phys., 1993, 78, 961-996.
121. Hoffmann, D.; Knapp, W. Polypeptide folding with off-lattice Monte Carlo dynamics: the method. Eur. Biophys. J., 1996, 111, 387-404.
122. Wu, M.G.; Deem, M.W. Analytical rebridging Monte Carlo: application to cis/trans isomerization in proline-containing, cyclic peptides. J. Chem. Phys., 1999, 14, 6625-6632.
123. Dinner, A.R. Local deformations of polymers with non-plannar rigid main chain internal coordinates. J. Comp. Chem., 2000, 21, 1132-1144.
124. Mezei, M. Efficient Monte Carlo sampling for long molecular chains using local moves, tested on a solvated lipid bilayer. J. Chem. Phys., 2003, 118, 3874-3879.
125. Cui, M.; Mezei, M.; Osman, R. Prediction of protein loop structures using a local move Monte Carlo approach and a grid-based force field. Protein Eng. Des. Sel., 2008, 21, 729-735.
126. Kubinyi, H. Computer Applications in Pharmaceutical Research and Development John Wiley: New York, 2006.
127. Kroemer, R.T. Structure-based drug design: docking and scoring. Curr. Protein Pept. Sci., 2007, 8, 312-328.
128. Venhorst, J.; ter Laak, A.M.; Commandeur, J.N.; Funae, Y.; Hiroi, T.; Vermeulen, N.P. Homology modeling of rat and human cytochrome P450 2D (CYP2D) isoforms and computational rationalization of experimental ligand-binding specificities. J. Med. Chem., 2003, 46, 74-86.
129. Williams, P.A.; Cosme, J.; Ward, A.; Angove, H.C.; Matak Vinkovic, D.; Jhoti, H. Crystal structure of human cytochrome P450 2C9 with bound warfarin. Nature, 2003, 424, 464-468.
130. Meng, X.Y.; Zheng, Q.C.; Zhang, H.X. A comparative analysis of binding sites between mouse CYP2C38 and CYP2C39 based on homology modeling, molecular dynamics simulation and docking studies. Biochim. Biophys. Acta, 2009, 1794, 1066-1072.
131. Boehm, H.J.; Boehringer, M.; Bur, D.; Gmuender, H.; Huber, W.; Klaus, W.; Kostrewa, D.; Kuehne, H.; Luebbers, T.; Meunier-Keller, N.; Mueller, F. Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J. Med. Chem., 2000, 43, 2664-2674.
132. Kirton, S.B.; Murray, C.W.; Verdonk, M.L.; Taylor, R.D. Prediction of binding modes for ligands in the cytochromes P450 and other heme-containing proteins. Proteins, 2005, 58, 836-844.
133. Doman, T.N.; McGovern, S.L.; Witherbee, B.J.; Kasten, T.P.; Kurumbail, R.; Stallings, W.C.; Connolly, D.T.; Shoichet, B.K., Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem., 2002, 45, 2213-2221.
134. Shoichet, B.K.; Leach, A.R.; Kuntz, I.D. Ligand solvation in molecular docking. Proteins, 1999, 34, 4-16.
135. Lorber, D.M.; Shoichet, B.K. Flexible ligand docking using conformational ensembles. Protein Sci., 1998, 7, 938-950.
136. Freymann, D.M.; Wenck, M.A.; Engel, J.C.; Feng, J.; Focia, P.J.; Eakin, A.E.; Craig, S.P. Efficient identification of inhibitors targeting the closed active site conformation of the HPRT from Trypanosoma cruzi. Chem. Biol., 2000, 7, 957-968.
137. Su, A.I.; Lorber, D.M.; Weston, G.S.; Baase, W.A.; Matthews, B.W.; Shoichet, B.K. Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation. Proteins, 2001, 42, 279-293.
138. Gschwend, D.A.; Kuntz, I.D. Orientational sampling and rigid-body minimization in molecular docking revisited: on-the-fly optimization and degeneracy removal. J. Comput. Aided Mol. Des., 1996, 10, 123-132.