(How to) profit from molecular dynamics-based ensemble docking

Application of Computational Techniques in Pharmacy and Medicine

Volumn 17, Issue , 2014, Pages 501-538

  von Grafenstein, Susanne ^a   Fuchs, Julian E ^a   Liedl, Klaus R ^a  

^a UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG PRODUCTS; MOLECULAR MODELING; PROTEINS;

COMPUTATIONAL TECHNIQUE; COMPUTER AIDED DRUG DESIGN; CONFORMATIONAL SPACE; MOLECULAR DOCKING; MOLECULAR DYNAMICS SIMULATIONS; MULTIPLE RECEPTORS; PROTEIN STRUCTURES; VIRTUAL SCREENING;

MOLECULAR DYNAMICS;

EID: 85009950121     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-94-017-9257-8_15     Document Type: Chapter

References (132)

1. Boehr DD, McElheny D, Dyson HJ, Wright PE (2006) The dynamic energy landscape of di-hydrofolate reductase catalysis. Science 313(5793):1638-1642. doi:10.1126/science.1130258
2. Fischer E (1894) Einfluss der Configuration auf die Wirkung der Enzyme. Ber Dtsch Chem Ges 27
3. Koshland DE (1958) Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci U S A 44 98-104
4. Tsai CJ, Ma BY, Nussinov R (1999) Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci U S A 96(18):9970-9972. doi:10.1073/pnas.96.18.9970
5. Tsai CJ, Kumar S, Ma BY, Nussinov R (1999) Folding funnels, binding funnels, and protein function. Protein Sci 8(6):1181-1190
6. Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5(11):789-796. doi:10.1038/nchembio.232
7. Durrant JD, McCammon JA (2010) Computer-aided drug-discovery techniques that account for receptor flexibility. Curr Opin Pharmacol 10(6):770-774. doi:10.1016/j.coph.2010.09.001
8. Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18(2):178-184. doi:10.1016/j.sbi.2008.01.004
9. Carlson HA, McCammon JA (2000) Accommodating protein flexibility in computational drug design. Mol Pharmacol 57(2):213-218
10. Carlson HA (2002) Protein flexibility is an important component of structure-based drug discovery. Curr Pharm Des 8(17):1571-1578. doi:10.2174/1381612023394232
11. Chaudhury S, Gray JJ (2008) Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles. J Mol Biol 381(4):1068-1087. doi:10.1016/j.jmb.2008.05.042
12. Henzler-Wildman KA, Lei M, Thai V, Kerns SJ, Karplus M, Kern D (2007) A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature 450(7171):913-U27. doi:10.1038/nature06407
13. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Str, Funct, Bioinform 78(8):1950-1958. doi:10.1002/prot.22711
14. Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19(2):120-127. doi:10.1016/j.sbi.2009.03.004
15. Goetz AW, Williamson MJ, Xu D, Poole D, Le Grand S, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J Chem Theory Comput 8(5):1542-1555. doi:10.1021/ct200909j
16. Luque I, Freire E (2000) Structural stability of binding sites: Consequences for binding affinity and allosteric effects. Proteins: Str Funct Genet 41(S4):63-71. doi:10.1002/1097-0134 (2000) 41:4+<63::AID-PROT60>3.0.CO;2-6
17. Kroemer RT (2007) Structure-based drug design: docking and scoring. Curr Protein Pept Sci 8(4):312-328
18. B Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7-8):394-400. doi:10.1016/j.drudis.2009.01.003
19. Jiang F, Kim SH (1991) Soft docking-matching of molecular-surface cubes. J Mol Biol 219(1):79-102. doi:10.1016/0022-2836(91)90859-5
20. Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308(2):377-395. doi:10.1006/jmbi.2001.4551
21. Flick J, Tristram F, Wenzel W (2012) Modeling loop backbone flexibility in receptor-ligand docking simulations. J Comput Chem 33(31):2504-2515. doi:10.1002/jcc.23087
22. Cozzini P, Kellogg GE, Spyrakis F, Abraham DJ, Costantino G, Emerson A, Fanelli F, Gohlke H, Kuhn LA, Morris GM, Orozco M, Pertinhez TA, Rizzi M, Sotriffer CA (2008) Target flexibility: an emerging consideration in drug discovery and design. J Med Chem 51(20):6237-6255. doi:10.1021/jm800562d
23. Gamblin SJ, Skehel JJ (2010) influenza hemagglutinin and neuraminidase membrane glyco-proteins. J Biol Chem 285(37):28403-28409. doi:10.1074/jbc.R110.129809
24. Lill MA (2011) Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 50(28):6157-6169. doi:10.1021/bi2004558
25. Russell RJ, Haire LF, Stevens DJ, Collins PJ, Lin YP, Blackburn GM, Hay AJ, Gamblin SJ, Skehel JJ (2006) The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design. Nature 443(7107):45-49. doi:10.1038/nature05114
26. Cheng LS, Amaro RE, Xu D, Li WW, Arzberger PW, McCammon JA (2008) Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuramini-dase. J Med Chem 51(13):3878-3894. doi:10.1021/jm8001197
27. Grienke U, Schmidtke M, Kirchmair J, Pfarr K, Wutzler P, Durrwald R, Wolber G, Liedl KR, Stuppner H, Rollinger JM (2010) Antiviral Potential and Molecular Insight into Neuramini-dase Inhibiting Diarylheptanoids from Alpinia katsumadai. J Med Chem 53(2):778-786. doi:10.1021/jm901440f
28. Vavricka CJ, Li Q, Wu Y, Qi JX, Wang MY, Liu Y, Gao F, Liu J, Feng EG, He JH, Wang JF, Liu H, Jiang HL, Gao GF (2011) Structural and functional analysis of laninamivir and its oc-tanoate prodrug reveals group specific mechanisms for influenza NA inhibition. PloS Pathog 7(10):e1002249. doi:10.1371/journal.ppat.1002249
29. Li Q, Qi JX, Zhang W, Vavricka CJ, Shi Y, Wei JH, Feng EG, Shen JS, Chen JL, Liu D, He JH, Yan JH, Liu H, Jiang HL, Teng MK, Li XB, Gao GF (2010) The 2009 pandemic H1N1 neuraminidase N1 lacks the 150-cavity in its active site. Nat Struct Mol Biol 17(10):1266-1268. doi:10.1038/nsmb.1909
30. van der Vries E, Collins PJ, Vachieri SG, Xiong XL, Liu JF, Walker PA, Haire LF, Hay AJ, Schutten M, Osterhaus A, Martin SR, Boucher CAB, Skehel JJ, Gamblin SJ (2012) H1N1 2009 Pandemic influenza virus: Resistance of the I223R neuraminidase mutant explained by kinetic and structural analysis. PloS Pathog 8(9):e1002914. doi:10.1371/journal. ppat.1002914
31. Rudrawar S, Dyason JC, Rameix-Welti MA, Rose FJ, Kerry PS, Russell RJ, van der Werf S, Thomson RJ, Naffakh N, von Itzstein M (2010) Novel sialic acid derivatives lock open the 150-loop of an influenza A virus group-1 sialidase. Nat Comm 1 113. doi:10.1038/ ncomms1114
32. Amaro RE, Swift RV, Votapka L, Li WW, Walker RC, Bush RM (2011) Mechanism of 150-cavity formation in influenza neuraminidase. Nat Comm 2388. doi:10.1038/ncomms1390
33. Wallnoefer HG, Lingott T, Gutierrez JM, Merfort I, Liedl KR (2010) Backbone flexibility controls the activity and specificity of a protein-protein interface: specificity in snake venom metalloproteases. J Am Chem Soc 132(30):10330-10337. doi:10.1021/ja909908y
34. Case DA, Darden TA, Cheatham III TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Hayik S, Roitberg A, Seabra G, Swails J, Goetz AW, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wolf RM, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh M-J, Cui G, Roe DR, Mathews DH, Seetin MG, Salomon-Ferrer R, Sagui C, Babin V, Luchko T, Gusarov S, Kovalenko A, Kollman PA (2012) AMBER12. University of California, San Francisco
35. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084-3095. doi:10.1021/ct400341p
36. von Grafenstein S, Wallnoefer HG, Kirchmair J, Fuchs JE, Huber RG, Spitzer GM, Schmidtke M, Sauerbrei A, Rollinger JM, Liedl KR (2013) Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity. J Biomol Struct Dyn. Published online doi:10.1080/07391102.2013.855142
37. McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267(5612):585-590. doi:10.1038/267585a0
38. Xu M, Lill MA (2011) Significant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inf Model 51(3):693-706. doi:10.1021/ci100457t
39. Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157-1174
40. Fuchs JE, Huber RG, Von Grafenstein S, Wallnoefer HG, Spitzer GM, Fuchs D, Liedl KR (2012) Dynamic regulation of phenylalanine hydroxylase by simulated redox manipulation. PloS One 7(12):e53005. doi:10.1371/journal.pone.0053005
41. Lin J-H, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124(20):5632-5633. doi:10.1021/ja0260162
42. Seeliger D, de Groot BL (2010) Conformational transitions upon ligand binding: holo-struc-ture prediction from apo conformations. PloS Comp Biol 6(1):e1000634. doi:10.1371/jour-nal.pcbi.1000634
43. Nichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 51(6):1439-1446. doi:10.1021/ci200117n
44. McGovern SL, Shoichet BK (2003) Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 46(14):2895-2907. doi:10.1021/jm0300330
45. Mackey MD, Melville JL (2009) Better than random? The chemotype enrichment problem. J Chem Inf Model 49(5):1154-1162. doi:10.1021/ci8003978
46. Bolstad ESD, Anderson AC (2009) In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking. Proteins: Str, Funct, Bioinform 75(1):62-74. doi:10.1002/prot.22214
47. Armen RS, Chen J, Brooks CL III (2009) An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J Chem Theory Comput 5(10):2909-2923. doi:10.1021/ct900262t
48. Marelius J, Ljungberg KB, Aqvist J (2001) Sensitivity of an empirical affinity scoring function to changes in receptor-ligand complex conformations. Eur J Pharm Sci 14(1):87-95. doi:10.1016/s0928-0987(01)00162-2
49. Kirchmair J, Spitzer GM, Liedl KR (2011) Consideration of water and solvation effects in virtual screening. In: Sotriffer C (ed) Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim, pp 263-289
50. Cole JC, Korb O, Olsson TSG, Liebeschuetz J (2011) The basis for target-based virtual screening: protein structures. In: Sotriffer C (ed) Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim, pp 87-114
51. Borjesson U, Hunenberger PH (2001) Explicit-solvent molecular dynamics simulation at constant pH: methodology and application to small amines. J Chem Phys 114(22):9706-9719
52. Merski M, Shoichet BK (2013) The impact of introducing a histidine into an apolar cavity site on docking and ligand recognition. J Med Chem 56(7):2874-2884. doi:10.1021/jm301823g
53. Kim MO, Nichols SE, Wang Y, McCammon JA (2013) Effects of histidine protonation and rotameric states on virtual screening of M-tuberculosis RmlC. J Comput-Aided Mol Des 27(3):235-246. doi:10.1007/s10822-013-9643-9
54. Labute P (2009) Protonate3D: assignment of ionization states and hydrogen coordinates to macromolecular structures. Proteins: Str, Funct, Bioinform 75(1):187-205. doi:10.1002/prot.22234
55. Halperin I, Ma BY, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins: Str, Funct, Genet 47(4):409-443. doi:10.1002/prot.10115
56. Sinko W, Lindert S, McCammon JA (2013) Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des 81(1):41-49. doi:10.1111/cbdd.12051
57. Osguthorpe DJ, Sherman W, Hagler AT (2012) Exploring protein flexibility: incorporating structural ensembles from crystal structures and simulation into virtual screening protocols. J Phys Chem B 116(23):6952-6959. doi:10.1021/jp3003992
58. Fulle S, Gohlke H (2010) Molecular recognition of RNA: challenges for modelling interactions and plasticity. J Mol Recognit 23(2):220-231. doi:10.1002/jmr.1000
59. Bruno A, Amori L, Costantino G (2011) Addressing the conformational flexibility of serine racemase by combining targeted molecular dynamics, conformational sampling and docking studies. Mol Inform 30(4):317-328. doi:10.1002/minf.201000162
60. Abagyan R, Totrov M, Kuznetsov D (1994) ICM-a new method for protein modeling and design-applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15(5):488-506. doi:10.1002/jcc.540150503
61. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1-2):141-151. doi:10.1016/s0009-2614(99)01123-9
62. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087-1092. doi:10.1063/1.1699114
63. Gervasio FL, Laio A, Parrinello M (2005) Flexible docking in solution using metadynamics. J Am Chem Soc 127(8):2600-2607. doi:10.1021/ja0445950
64. Zacharias M (2004) Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP. Proteins: Str, Funct, Bioinform 54(4):759-767. doi:10.1002/prot.10637
65. Hinsen K (1998) Analysis of domain motions by approximate normal mode calculations. Proteins: Str, Funct, Genet33(3):417-429. doi:10.1002/(sici)1097-0134(19981115)33:3<417::aid-prot10>3.0.co;2-8
66. May A, Zacharias M (2008) Protein-ligand docking accounting for receptor side chain and global flexibility in normal modes: evaluation on kinase inhibitor cross docking. J Med Chem 51(12):3499-3506. doi:10.1021/jm800071v
67. Leis S, Zacharias M (2011) Efficient inclusion of receptor flexibility in grid-based proteinligand docking. J Comput Chem 32(16):3433-3439. doi:10.1002/jcc.21923
68. Cavasotto CN, Kovacs JA, Abagyan RA (2005) Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 127(26):9632-9640. doi:10.1021/ja042260c
69. Rueda M, Bottegoni G, Abagyan R (2009) Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes. J Chem Inf Model 49(3):716-725. doi:10.1021/ci8003732
70. Tran HT, Zhang S (2011) Accurate prediction of the bound form of the akt pleckstrin homol-ogy domain using normal mode analysis to explore structural flexibility. J Chem Inf Model 51(9):2352-2360. doi:10.1021/ci2001742
71. Cavasotto CN, Abagyan RA (2004) Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 337(1):209-225. doi:10.1016/j.jmb.2004.01.003
72. Davis IW, Baker D (2009) ROSETTALIGAND docking with full ligand and receptor flexibility. J Mol Biol 385(2):381-392. doi:10.1016/j.jmb.2008.11.010
73. Meiler J, Baker D (2006) ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins: Str, Funct, Bioinform 65(3):538-548. doi:10.1002/prot.21086
74. Alonso H, Bliznyuk AA, Gready JE (2006) Combining docking and molecular dynamic simulations in drug design. Med Res Rev 26(5):531-568. doi:10.1002/med.20067
75. Knegtel RMA, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266(2):424-440. doi:10.1006/jmbi.1996.0776
76. Polgar T, Keseru GM (2006) Ensemble docking into flexible active sites. Critical evaluation of flexE against JNK-3 and beta-secretase. J Chem Inf Model 46(4):1795-1805. doi:10.1021/ ci050412x
77. Osterberg F, Morris GM, Sanner MF, Olson AJ, Goodsell DS (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins: Str, Funct, Genet 46(1):34-40. doi:10.1002/prot.10028
78. Broughton HB (2000) A method for including protein flexibility in protein-ligand docking: improving tools for database mining and virtual screening. J Mol Graphics Modell 18(3):247-257. doi:10.1016/s1093-3263(00)00036-x
79. Cosconati S, Marinelli L, Di Leva FS, La Pietra V, De Simone A, Mancini F, Andrisano V, Novellino E, Goodsell DS, Olson AJ (2012) Protein flexibility in virtual screening: the BACE-1 case study. J Chem Inf Model 52(10):2697-2704. doi:10.1021/ci300390h
80. Lin JH, Perryman AL, Schames JR, McCammon JA (2003) The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68(1):47-62. doi:10.1002/bip.10218
81. Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni HH, McCammon JA (2004) Discovery of a novel binding trench in HIV integrase. J Med Chem 47(8):1879-1881. doi:10.1021/jm0341913
82. Perryman AL, Lin J-H, McCammon JA (2006) Optimization and computational evaluation of a series of potential active site inhibitors of the V82F/I84V drug-resistant mutant of HIV-1 protease: an application of the relaxed complex method of structure-based drug design. Chem Biol Drug Des 67(5):336-345. doi:10.1111/j.1747-0285.2006.00382.x
83. Trott O, Olson AJ (2010) Software news and update autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455-461. doi:10.1002/jcc.21334
84. Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput-Aided Mol Des 22(9):693-705. doi:10.1007/s10822-007-9159-2
85. Sivanesan D, Rajnarayanan RV, Doherty J, Pattabiraman N (2005) In-silico screening using flexible ligand binding pockets: a molecular dynamics-based approach. J Comput-Aided Mol Des 19(4):213-228. doi:10.1007/s10822-005-4788-9
86. Paulsen JL, Anderson AC (2009) Scoring ensembles of docked protein: ligand interactions for virtual lead optimization. J Chem Inf Model 49(12):2813-2819. doi:10.1021/ci9003078
87. Korb O, Olsson TSG, Bowden SJ, Hall RJ, Verdonk ML, Liebeschuetz JW, Cole JC (2012) Potential and limitations of ensemble docking. J Chem Inf Model 52(5):1262-1274. doi:10.1021/ci2005934
88. Nichols SE, Swift RV, Amaro RE (2012) Rational prediction with molecular dynamics for hit identification. Curr Top Med Chem 12(18):2002-2012
89. Shao JY, Tanner SW, Thompson N, Cheatham TE (2007) Clustering molecular dynamics trajectories: 1. characterizing the performance of different clustering algorithms. J Chem Theory Comput 3(6):2312-2334. doi:10.1021/ct700119m
90. Daura X, van Gunsteren WF, Mark AE (1999) Folding-unfolding thermodynamics of a be-ta-heptapeptide from equilibrium simulations. Proteins: Str, Funct, Genet 34 (3):269-280. doi:10.1002/(sici)1097-0134(19990215)34:3<269::aid-prot1>3.0.co;2-3
91. Durrant JD, Hall L, Swift RV, Landon M, Schnaufer A, Amaro RE (2010) Novel naphthalene-based inhibitors of trypanosoma brucei RNA editing ligase 1. PloS Neglect Trop D 4(8):e803 doi:10.1371/journal.pntd.0000803
92. Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA (2013) CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model. doi:10.1021/ci400025f
93. Ben Nasr N, Guillemain H, Lagarde N, Zagury J-F, Montes M (2013) Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query. J Chem Inf Model 53(2):293-311. doi:10.1021/ci3004557
94. Sotriffer CA, Dramburg I (2005) "In situ cross-docking" to simultaneously address multiple targets. J Med Chem 48(9):3122-3125. doi:10.1021/jm050075j
95. Huang S-Y, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins: Str, Funct, Bioinform 66(2):399-421. doi:10.1002/prot.21214
96. Bottegoni G, Kufareva I, Totrov M, Abagyan R (2009) Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52(2):397-406. doi:10.1021/jm8009958
97. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161(2):269-288. doi:10.1016/0022-2836(82)90153-x
98. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Rev Drug Discov 3(11):935-949. doi:10.1038/nrd1549
99. Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303(5665):1813-1818. doi:10.1126/science.1096361
100. Rognan D (2011) Docking methods for virtual screening: principles and recent advances. In: Sotriffer C (ed) Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim, pp 153-176
101. Henzler AM, Rarey M (2011) Protein flexibility in structure-based virtual screening: from models to algorithms. In: Sotriffer C (ed) Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim, pp 223-244
102. Carlson HA, Dunbar JB Jr (2011) A call to arms: what you can do for computational drug discovery. J Chem Inf Model 51(9):2025-2026. doi:10.1021/ci200398g
103. Warren GL, Andrews CW, Capelli A-M, 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(20):5912-5931. doi:10.1021/jm050362n
104. Perola E, Charifson PS (2004) Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem 47(10):2499-2510. doi:10.1021/jm030563w
105. Gohlke H, Hendlich M, Klebe G (2000) Predicting binding modes, binding affinities and 'hot spots' for protein-ligand complexes using a knowledge-based scoring function. Perspec Drug Discov Des 20(1):115-144. doi:10.1023/a:1008781006867
106. Velec HFG, Gohlke H, Klebe G (2005) DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J Med Chem 48(20):6296-6303. doi:10.1021/jm050436v
107. Kuhn B, Fuchs JE, Reutlinger M, Stahl M, Taylor NR (2011) Rationalizing tight ligand binding through cooperative interaction networks. J Chem Inf Model 51(12):3180-3198. doi:10.1021/ci200319e
108. Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J Comput-Aided Mol Des 26(6):787-799. doi:10.1007/ s10822-012-9575-9
109. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817-2831. doi:10.1021/ja0771033
110. Cummings MD, Arnoult É, Buyck C, Tresadern G, Vos AM, Wegner JK (2011) Preparing and filtering compound databases for virtual and experimental screening. In: Sotriffer C (ed) Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim, pp 35-59
111. Mason JS, Bortolato A, Congreve M, Marshall FH (2012) New insights from structural biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33(5):249-260. doi:10.1016/j.tips.2012.02.005
112. Smith RD, Dunbar JB Jr, Ung PM-U, Esposito EX, Yang C-Y, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115-2131. doi:10.1021/ci200269q
113. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: 1. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput-Aided Mol Des 11(5):425-445. doi:10.1023/a:1007996124545
114. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A (2011) Systematic exploitation of multiple receptor conformations for virtual ligand screening. PloS One 6(5):e18845. doi:10.1371/journal.pone.0018845
115. Xu M, Lill MA (2012) Utilizing experimental data for reducing ensemble size in flexible-protein docking. J Chem Inf Model 52(1):187-198. doi:10.1021/ci200428t
116. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337(5):1161-1182. doi:10.1016/j.jmb.2004.02.015
117. Eriksson AE, Baase WA, Zhang XJ, Heinz DW, Blaber M, Baldwin EP, Matthews BW (1992) Response of a protein-structure to cavity-creating mutations and its relation to the hydrophobic effect. Science 255(5041):178-183. doi:10.1126/science.1553543
118. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432-4443. doi:10.1021/jm048972v
119. Minh DDL (2012) Implicit ligand theory: rigorous binding free energies and thermodynamic expectations from molecular docking. J Chem Phys 137(10). doi:10.1063/1.4751284
120. Feng JA, Marshall GR (2010) SKATE: a docking program that decouples systematic sampling from scoring. J Comput Chem 31(14):2540-2554. doi:10.1002/jcc.21545
121. Korb O, McCabe P, Cole J (2011) The ensemble performance index: an improved measure for assessing ensemble pose prediction performance. J Chem Inf Model 51(11):2915-2919. doi:10.1021/ci2002796
122. Nicholls A (2008) What do we know and when do we know it?. J Comput-Aided Mol Des 22(3-4):239-255. doi:10.1007/s10822-008-9170-2
123. Peterson W, Birdsall T, Fox W (1954) The theory of signal detectability. Trans IRE Prof Group Inform Theory 4(4):171-212
124. Okimoto N, Futatsugi N, Fuji H, Suenaga A, Morimoto G, Yanai R, Ohno Y, Narumi T, Taiji M (2009) High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations. PloS Comp Biol 5(10):e1000528. doi:10.1371/ journal.pcbi.1000528
125. Amaro R, Cheng L, McCammon JA, Li WW, Arzberber PW (2009) Ensemble-based virtual screening reveals novel antiviral compounds for avian influenza neuraminidase. US PatentWO2009128964 22 Jan 2009
126. Proctor EA, Yin S, Tropsha A, Dokholyan NV (2012) Discrete molecular dynamics distinguishes native like binding poses from decoys in difficult targets. Biophys J 102(1):144-151. doi:10.1016/j.bpj.2011.11.4008
127. Wallnoefer H, Fox T, Liedl K (2010) Challenges for computer simulations in drug design. In: Paneth P, Dybala-Defratyka A (eds) Kinetics and dynamics, challenges and advances in computational chemistry and physics. Springer Netherlands, Dordrecht, pp 431-463
128. Honig B, Nicholls A (1995) Classical electrostatics in biology and chemistry. Science 268(5214):1144-1149. doi:10.1126/science.7761829
129. Still WC, Tempczyk A, Hawley RC, Hendrickson T (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112(16):6127-6129. doi:10.1021/ja00172a038
130. Negri M, Recanatini M, Hartmann RW (2011) Computational investigation of the binding mode of bis(hydroxylphenyl)arenes in 17 beta-HSD1: molecular dynamics simulations, MM-PBSA free energy calculations, and molecular electrostatic potential maps. J Comput-Aided Mol Des 25(9):795-811. doi:10.1007/s10822-011-9464-7
131. Aqvist J, Medina C, Samuelsson JE (1994) New method for predicting binding-affinity in computer-aided drug design. Protein Eng 7(3):385-391. doi:10.1093/protein/7.3.385
132. Stjernschantz E, Oostenbrink C (2010) Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 98(11):2682-2691. doi:10.1016/j.bpj.2010.02.034