Identification of pharmacological targets combining docking and molecular dynamics simulations

American Journal of Agricultural and Biological Science

Volumn 8, Issue 1, 2013, Pages 89-106

  Ian, Ilizaliturri Flores ^a   Luis, Rosas Trigueros Jorge ^a,c   Pablo, Carrillo Vazquez Jonathan ^a   Luis, Vique Sanchez Jose ^a   Normande, Carrillo Ibarra ^a   Beatriz, Zamora Lopez ^b   Sandino, Reyes Lopez Cesar Augusto ^a   Guadalupe, Benitez Cardoza Claudia ^a   Jose, Correa Basurto ^c,d   Absalom, Zamorano Carrillo ^a  

^a INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^c Interdisciplinary Group on Artificial Intelligence Applied to Protein Folding   (Mexico)

^d SEPI ESM IPN   (Mexico)

Author keywords

Docking; Drug design; In silico; MD simulations; Theoretical studies

Indexed keywords

EID: 84876230130     PISSN: 15574989     EISSN: 15574997     Source Type: Journal    
DOI: 10.3844/ajabssp.2013.89.106     Document Type: Review

References (168)

1. Abel, R., N.K. Salam, J. Shelley, R. Farid and R.A. Friesner et al., 2011. Contribution of explicit solvent effects to the binding affinity of small-molecule inhibitors in blood coagulation factor serine proteases. Chem. Med. Chem., 6: 1049-1066. DOI: 10.1002/cmdc.201000533
2. Almqvist, J., Y. Huang, A. Laaksonen, D.N. Wang and S. Hovmoller, 2007. Docking and homology modeling explain inhibition of the human vesicular glutamate transporters. Protein Sci., 16: 1819-1829. DOI: 10.1110/ps.072944707
3. Alonso, H., A.A. Bliznyuk and J.E. Gready, 2006. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev., 26: 531-568. DOI: 10.1002/med.20067
4. Anand, S. and D. Mohanty, 2012. Modeling holo- ACP:DH and holo-ACP: KR complexes of modular polyketide synthases: A docking and molecular dynamics study. BMC Struct. Biol., 12: 10-10. DOI: 10.1186/1472-6807-12-10
5. Andrusier, N., E. Mashiach, R. Nussinov and H.J. Wolfson, 2008. Principles of flexible protein-protein docking. Proteins, 73: 271-289. DOI: 10.1002/prot.22170
6. Andrusier, N., R. Nussinov and H.J. Wolfson, 2007. FireDock: Fast interaction refinement in molecular docking. Proteins: Struct. Function Bioinform., 69: 139-159. DOI: 10.1002/prot.21495
7. Aqvist, J. and O. Tapia, 1992. A molecular model for the retinol binding protein-transthyretin complex. J. Mol. Graph., 10: 120-123. DOI: 10.1016/0263-7855(92)80065-L
8. Aqvist, J., V.B. Luzhkov and B.O. Brandsdal, 2002. Ligand binding affinities from MD simulations. Accou. Chem. Res., 35: 358-365. DOI: 10.1021/ar010014p
9. Aradi, I. and P. Erdi, 2006. Computational neuropharmacology: Dynamical approaches in drug discovery. Trends Pharmacol. Sci., 27: 240-243. DOI: 10.1016/j.tips.2006.03.004
10. Bahar, I. and A.J. Rader, 2005. Coarse-grained normal mode analysis in structural biology. Curr. Opinion Struct. Biol., 15: 586-592. DOI: 10.1016/j.sbi.2005.08.007
11. Balabin, I.A., W. Yang and D.N. Beratan, 2009. Coarsegrained modeling of allosteric regulation in protein receptors. Proc. Natl. Acad. Sci., 106: 14253-14258. DOI: 10.1073/pnas.0901811106
12. Banner, D.W., A.C. Bloomer, G.A. Petsko, D.C. Phillips and C.I. Pogson et al., 1975. Structure of chicken muscle triose phosphate isomerase determined crystallographically at 2.5 angstrom resolution using amino acid sequence data. Nature, 255: 609-614. PMID: 1134550
13. Barducci, A., M. Bonomi and M. Parrinello, 2011. Metadynamics. Sci., 1: 826-843. DOI: 10.1002/wcms.31
14. Baud, M.G., T. Leiser, P. Haus, S. Samlal and A.C.C. Wong et al., 2012. Defining the mechanism of action and enzymatic selectivity of psammaplin A against its epigenetic targets. J. Med. Chem., 55: 1731-1750. DOI: 10.1021/jm2016182
15. Bauer, B.A., J.E. Davis, M. Taufer and S. Patel, 2011. Molecular dynamics simulations of aqueous ions at the liquid-vapor interface accelerated using graphics processors. J. Comput. Chem., 32: 375-385. DOI: 10.1002/jcc.21578
16. Berman, H.M., J. Westbrook, Z. Feng, G. Gilliland and T.N. Bhat et al., 2000. The protein data bank. Nucl. Acids Res., 28: 235-242. PMID: 10592235
17. Best, R.B., N.V.V. Buchete and G. Hummer, 2008. Are current molecular dynamics force fields too helical. Biophys. J., 95: L07-L09. DOI: 10.1529/biophysj.108.132696
18. Blacklow, S.C., R.T. Raines, W.A. Lim, P.D. Zamore and J.R. Knowles, 1998. Triosephosphate isomerase catalysis is diffusion controlled. Biochemistry, 27: 1158-1167. DOI: 10.1021/bi00404a013
19. Bohm, H.J., 1992. LUDI: Rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Design, 6: 593-606. DOI: 10.1007/BF00126217
20. Bohm, H.J., 1998. 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. Design, 12: 309-323. DOI: 10.1023/A:1007999920146
21. Braga, A., 2012. QSAR and QM/MM approaches applied to drug metabolism prediction. Mini Rev. Med. Chem., 12: 573-582. DOI: 10.2174/138955712800493807
22. Brooks, B.R., C.L. Brooks, A.D. Mackerell, L. Nilsson, R.J. Petrella and B. Roux et al., 2009. CHARMM: The biomolecular simulation program. J. Computat. Chem., 30: 1545-1614. DOI: 10.1002/jcc.21287
23. Brooks, B.R., R.E. Bruccoleri, B.D. Olafson, D.J. States and S. Swaminathan et al., 1983. CHARMM: A program for macromolecular energy, minimization and dynamics calculations. J. Computat. Chem., 4: 187-217. DOI: 10.1002/jcc.540040211
24. Carlson, H.A. and W.L. Jorgensen, 1995. An extended linear response method for determining free energies of hydration. J. Physical Chem., 99: 10667-10673. DOI: 10.1021/j100026a034
25. Cavasotto, C.N. and R.A. Abagyan, 2004. Protein flexibility in ligand docking and virtual screening to protein kinases. J. Mol. Biol., 337: 209-225. DOI: 10.1016/j.jmb.2004.01.003
26. Cerqueira, N.M.F.S.A., S.F. Sousa, P.A. Fernandes and M.J. Ramos, 2009. Virtual screening of compound libraries ligand-macromolecular interactions in drug discovery. Methods Mol. Biol., 572: 57-70. DOI: 10.1007/978-1-60761-244-5_4
27. Charifson, P.S., J.J. Corkery, M.A. Murcko and W.P. Walters, 1999. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 42: 5100-5109. DOI: 10.1021/jm990352k
28. Chaudhury, S. and J.J. Gray, 2008. Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles. J. Mol. Biol., 381: 1068-1087. DOI: 10.1016/j.jmb.2008.05.042
29. Chepelev, L.L. and M. Dumontier, 2009. Target Profiling of Small Molecules. In: Protein Targeting with Small Molecules: Chemical Biology Techniques and Applications, Osada, H. (Ed.), John Wiley and Sons, Inc., Hoboken, NJ, USA., ISBN- 10: 0470495006.
30. Chopra, G., C.M. Summa and M. Levitt, 2008. Solvent dramatically affects protein structure refinement. Proc. Natl. Acad. Sci. USA., 105: 20239-20244. DOI: 10.1073/pnas.0810818105
31. Colizzi, F., R. Perozzo, L. Scapozza, M. Recanatini and A. Cavalli, 2010. Single-molecule pulling simulations can discern active from inactive enzyme inhibitors. J. Am. Chem. Soc., 132: 7361-7371. DOI: 10.1021/ja100259r
32. Cornell, W.D., P. Cieplak, C.I. Bayly, I.R. Gould and K.M. Merz et al., 1995. A second generation force field for the simulation of proteins, nucleic acids and organic molecules. J. Am. Chem. Soc., 117: 5179-5197. DOI: 10.1021/ja00124a002
33. Coupez, B. and R.A. Lewis, 2006. Docking and scoringtheoretically easy, practically impossible. Curr. Med. Chem., 13: 2995-3003. DOI: 10.2174/092986706778521797
34. Cuendet, M.A., 2006. The Jarzynski identity derived from general Hamiltonian or non-Hamiltonian dynamics reproducing NVT or NPT ensembles. J. Chem. Phys., 125: 144109-144120. DOI: 10.1063/1.2338535
35. D'Abramo, M., O. Rabal, J. Oyarzabal and F.L. Gervasio, 2012. Conformational selection versus induced fit in kinases: The case of PI3K PI3K-?. Angewandte Chemie Int. Edn., 51: 642-646. DOI: 10.1002/anie.201103264
36. Daura, X., A. Glattli, P. Gee, C. Peter and W.F. van Gunsteren, 2002. Unfolded state of peptides. Adv. Protein Chem., 62: 341-360. DOI: 10.1016/S0065-3233(02)62013-3
37. DesJarlais, R.L., R.P. Sheridan, J.S. Dixon, I.D. Kuntz and R. Venkataraghavan, 1986. Docking flexible ligands to macromolecular receptors by molecular shape. J. Med. Chem., 29: 2149-2153. DOI: 10.1021/jm00161a004
38. Dodson, G.G., D.P. Lane and C.S. Verma, 2008. Molecular simulations of protein dynamics: New windows on mechanisms in biology. EMBO Reports, 9: 144-150. DOI: 10.1038/sj.embor.7401160
39. Dominguez, C., R. Boelens and A.M. Bonvin, 2003. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc., 125: 1731-1737. DOI: 10.1021/ja026939x
40. Durrant, J.D. and J.A. McCammon, 2011. Molecular dynamics simulations and drug discovery. BMC Biol., 9: 71-71. DOI: 10.1186/1741-7007-9-71
41. Eisen, M.B., D.C. Wiley, M. Karplus and R.E. Hubbard, 1994. HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins, 19: 199-221. DOI: 10.1002/prot.340190305
42. Enriquez-Flores, S., A. Rodriguez-Romero, G. Hernandez-Alcantara, P. Gutierrez-Castrellon and K. Carvajal et al., 2008. Species-specific inhibition of Giardia lamblia triosephosphate isomerase by localized perturbation of the homodimer. Mol. Biochem. Parasitol., 157: 179-186. DOI: 10.1016/j.molbiopara.2007.10.013
43. Espinoza-Fonseca, L.M. and J.G. Trujillo-Ferrara, 2004. Exploring the possible binding sites at the interface of triosephosphate isomerase dimer as a potential target for anti-tripanosomal drug design. Bioorganic Med. Chem. Lett., 14: 3151-3154. DOI: 10.1016/j.bmcl.2004.04.013
44. Espinoza-Fonseca, L.M. and J.G. Trujillo-Ferrara, 2005. Structural considerations for the rational design of selective anti-trypanosomal agents: The role of the aromatic clusters at the interface of triosephosphate isomerase dimer. Biochem. Biophysical Res. Commun., 328: 922-928. DOI: 10.1016/j.bbrc.2005.01.043
45. Espinoza-Fonseca, L.M. and J.G. Trujillo-Ferrara, 2006. Toward a rational design of selective multitrypanosomatid inhibitors: A computational docking study. Bioorganic Med. Chem. Lett., 16: 6288-6292. DOI: 10.1016/j.bmcl.2006.09.029
46. Farah, K., F. Muller-Plathe and M.C. Bohm, 2012. Classical reactive molecular dynamics implementations: State of the art. Chemphyschem, 13: 1127-1151. DOI: 10.1002/cphc.201100681
47. Feher, M. and C.I. Williams, 2012. Numerical errors and chaotic behavior in docking simulations. J. Chem. Inform. Model., 52: 724-738. DOI: 10.1021/ci200598m
48. Feher, M., 2006. Consensus scoring for protein-ligand interactions. Drug Disco. Today, 11: 421-428. DOI: 10.1016/j.drudis.2006.03.009
49. Feher, M., E. Deretey and S. Roy, 2003. BHB: A simple knowledge-based scoring function to improve the efficiency of database screening. J. Chem. Inform. Comput. Sci., 43: 1316-1327. DOI: 10.1021/ci030006i
50. Feig, M. and C.L. Brooks, 2004. Recent advances in the development and application of implicit solvent models in biomolecule simulations. Curr. Opinion Struct. Biol., 14: 217-224. DOI: 10.1016/j.sbi.2004.03.009
51. Fidelak, J., J. Juraszek, D. Branduardi, M. Bianciotto and F.L. Gervasio, 2010. Free-energy-based methods for binding profile determination in a congeneric series of CDK2 Inhibitors. J. Phys. Chem. B, 114: 9516-9524. DOI: 10.1021/jp911689r
52. Fischer, D., R. Norel, H. Wolfson and R. Nussinov, 1993. Surface motifs by a computer vision technique: Searches, detection and implications for protein-ligand recognition. Proteins, 16: 278-292. DOI: 10.1002/prot.340160306
53. Floquet, N., J.D. Marechal, M.A. Badet-Denisot, C.H. Robert and M. Dauchez et al., 2006. Normal mode analysis as a prerequisite for drug design: Application to matrix metalloproteinases inhibitors. FEBS Lett., 580: 5130-5136. DOI: 10.1016/j.febslet.2006.08.037
54. Freddolino, P.L., A.S. Arkhipov, S.B. Larson, A. McPherson and K. Schulten, 2006. Molecular dynamics simulations of the complete satellite tobacco mosaic virus. Structure, 14: 437-449. DOI: 10.1016/j.str.2005.11.014
55. Froimowitz, M., 1993. HyperChem: A software package for computational chemistry and molecular modeling. Biotechnology, 14: 1010-1013. DOI: 10.1016/j.jtbi.2011.01.010
56. Gabb, H.A., R.M. Jackson and M.J. Sternberg, 1997. Modelling protein docking using shape complementarity, electrostatics and biochemical information. J. Mol. Biol., 272: 106-120. DOI: 10.1006/jmbi.1997.1203
57. Garza-Ramos, G., N. Cabrera, E. Saavedra-Lira, M.T.D. Gomez-Puyou and P. Ostoa-Saloma et al., 1998. Sulfhydryl reagent susceptibility in proteins with high sequence similarity--triosephosphate isomerase from Trypanosoma brucei, Trypanosoma cruzi and Leishmania mexicana. Eur. J. Biochem., 253: 684-691. PMID: 9654066
58. Garza-Ramos, G., R. Perez-Montfort, A. Rojo- Dominguez, M.T.D. Gomez-Puyou and A. Gomez- Puyou, 1996. Species-specific inhibition of homologous enzymes by modification of nonconserved amino acids residues. The cysteine residues of triosephosphate isomerase. Eur. J. Biochem., 241: 114-120. DOI: 10.1111/j.1432-1033.1996.0114t.x
59. Gehlhaar, D.K., G.M. Verkhivker, P.A. Rejto, C.J. Sherman and D.B. Fogel et al., 1995. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: Conformationally flexible docking by evolutionary programming. Chem. Biol., 2: 317-324. DOI: 10.1016/1074-5521(95)90050-0
60. Ghorbani, M., M. Mehta, R. Bruinsma and A.J. Levine, 2012. Nonlinear-dynamics theory of up-down transitions in neocortical neural networks. Phys. Rev. E., 85: 021908. DOI: 10.1103/PhysRevE.85.021908
61. Goodsell, D.S., H. Lauble, C.D. Stout and A.J. Olson, 1993. Automated docking in crystallography: Analysis of the substrates of aconitase. Proteins, 17: 1-10. DOI: 10.1002/prot.340170104
62. Gorinski, N., N. Kowalsman, U. Renner, A. Wirth and M.T. Reinartz et al., 2012. Computational and experimental analysis of the TM4/TM5 dimerization interface of the serotonin 5-HT1A receptor. Mol. Pharmacol., 82: 448-463. DOI: 10.1124/mol.112.079137
63. Gorse, A.D. and J.E. Gready, 1997. Molecular dynamics simulations of the docking of substituted N5- deazapterins to dihydrofolate reductase. Protein Eng., 10: 23-30. DOI: 10.1093/protein/10.1.23
64. Gotz, A.W., M.J. Williamson, D. Xu, D. Poole and S.L. Grand et al., 2012. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput., 8: 1542-1555. DOI: 10.1021/ct200909j
65. Gschwend, D.A., A.C. Good and I.D. Kuntz, 1996. Molecular docking towards drug discovery. J. Mol. Recogn., 9: 175-186. DOI: 10.1002/(SICI)1099-1352(199603)9:2<175::AID-JMR260>3.0.CO;2-D
66. Guimaraes, C.R. and A.M. Mathiowetz, 2010. Addressing limitations with the MM-GB/SA scoring procedure using the WaterMap method and free energy perturbation calculations. J. Chem. Inform. Model., 50: 547-559. DOI: 10.1021/ci900497d
67. Hamelberg, D., J. Mongan and J.A. McCammon, 2004. Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J. Chem. Phys., 120: 11919-11929. DOI: 10.1063/1.1755656
68. Hammes, G.G., 2002. Multiple conformational changes in enzyme catalysis. Biochemistry, 41: 8221-8228. DOI: 10.1021/bi0260839
69. Harris, S.A., E. Gavathiotis, M.S. Searle, M. Orozcom and C.A. Laughton, 2001. Cooperativity in Drug- DNA recognition: A molecular dynamics study. J. Chem. Soc., 123: 12658-12663. DOI: 10.1021/ja016233n
70. Hart, T.N. and R.J. Read, 1992. A multiple-start monte carlo docking method. Proteins, 13: 206-222. DOI: 10.1002/prot.340130304
71. Harvey, M.J. and G.D. Fabritiis, 2012. High-throughput molecular dynamics: The powerful new tool for drug discovery. Drug Disco. Today. DOI: 10.1016/j.drudis.2012.03.017
72. Hildebrandt, A., R. Blossey, S. Rjasanow, O. Kohlbacher and H.P.P. Lenhof, 2007. Electrostatic potentials of proteins in water: A structured continuum approach. Bioinformation, 23: e99-e103. DOI: 10.1093/bioinformatics/btl312
73. Huang, S.Y.Y. and X. Zou, 2010. Advances and challenges in protein-ligand docking. Int. J. Sci., 11: 3016-3034. DOI: 10.3390/ijms11083016
74. Ishchenko, A.V. and E.I. Shakhnovich, 2002. SMall Molecule Growth 2001 (SMoG2001): An improved knowledge-based scoring function for protein-ligand interactions. J. Med. Chem., 45: 2770-2780. DOI: 10.1021/jm0105833
75. Isralewitz, B., M. Gao and K. Schulten, 2001. Steered molecular dynamics and mechanical functions of proteins. Curr. Opinion Struct. Biol., 11: 224-230. DOI: 10.1016/S0959-440X(00)00194-9
76. Ivetac, A. and J.A. McCammon, 2011. Molecular recognition in the case of flexible targets. Curr. Pharmaceutical Design, 17: 1663-1671. DOI: 10.2174/138161211796355056
77. Jain, A.N., 2003. Surflex: Fully automatic flexible molecular docking using a molecular similaritybased search engine. J. Med. Chem., 46: 499-511. DOI: 10.1021/jm020406h
78. Jiang, F. and S.H. Kim, 1991. Soft docking: Matching of molecular surface cubes. J. Mol. Biol., 219: 79-102. DOI: 10.1016/0022-2836(91)90859-5
79. Joannis, J.D., P.S. Coppock, F. Yin, M. Mori and A. Zamorano et al., 2011. Atomistic simulation of cholesterol effects on miscibility of saturated and unsaturated phospholipids: Implications for liquidordered/ liquid-disordered phase coexistence. J. Am. Chem. Soc., 133: 3625-3634. DOI: 10.1021/ja110425s
80. Jones, G., P. Willett, R.C. Glen, A.R. Leach and R. Taylor, 1997. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 267: 727-748. DOI: 10.1006/jmbi.1996.0897
81. Jorgensen, W.L. and J. Tirado-Rives, 1988. The OPLS optimized potentials for liquid simulations potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc., 110: 1657-1666. DOI: 10.1021/ja00214a001
82. Joubert, F., A.W. Neitz and A.I. Louw, 2001. Structurebased inhibitor screening: A family of sulfonated dye inhibitors for malaria parasite triosephosphate isomerase. Proteins, 45: 136-143. DOI: 10.1002/prot.1133
83. Kass, R.S., 2005. The channelopathies: Novel insights into molecular and genetic mechanisms of human disease. J. Clin. Investigat., 115: 1986-1989. DOI: 10.1172/JCI26011
84. Katritch, V., M. Rueda and R. Abagyan, 2012. Ligandguided receptor optimization. Methods Mol. Biol., 857: 189-205. DOI: 10.1007/978-1-61779-588-6_8
85. Kinch, L.N., S. Shi, H. Cheng, Q. Cong and J. Pei et al., 2011. CASP9 target classification. Proteins, 79 Suppl., 10: 21-36. DOI: 10.1002/prot.23190
86. Kitchen, D.B., H. Decornez, J.R. Furr and J. Bajorath, 2004. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Rev. Drug Disco., 3: 935-949. DOI: 10.1038/nrd1549
87. Klaic, L., R.I. Morimoto and R.B. Silverman, 2012. Celastrol analogues as inducers of the heat shock response. Design and synthesis of affinity probes for the identification of protein targets. ACS Chem. Biol., 7: 928-937. DOI: 10.1021/cb200539u
88. Knegtel, R.M., I.D. Kuntz and C.M. Oshiro, 1997. Molecular docking to ensembles of protein structures. J. Mol. Biol., 266: 424-440. DOI: 10.1006/jmbi.1996.0776
89. Kohlhoff, M., A. Dahm and R. Hensel, 1996. Tetrameric triosephosphate isomerase from hyperthermophilic Archaea. FEBS Lett., 383: 245-250. DOI: 10.1016/0014-5793(96)00249-9
90. Kohlmann, A., X. Zhu and D. Dalgarno, 2012. Application of MM-GB/SA and WATERMAP to SRC kinase inhibitor potency prediction. ACS Med. Chem. Lett., 3: 94-99. DOI: 10.1021/ml200222u
91. Kollman, P.A., I. Massova, C. Reyes, B. Kuhn and S. Huo et al., 2000. Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts Chem. Res., 33: 889-897. DOI: 10.1021/ar000033j
92. Koshland, D.E., 1958. Application of a theory of enzyme specificity to protein synthesis. Proc. Natl. Acad. Sci. USA., 44: 98-104. DOI: 10.1073/pnas.44.2.98
93. Koshland, D.E., 1963. Correlation of structure and function in enzyme action. Science, 142: 1533-1541. DOI: 10.1126/science.142.3599.1533
94. Krol, M., A.L. Tournier and P.A. Bates, 2007. Flexible relaxation of rigid-body docking solutions. Proteins, 68: 159-169. DOI: 10.1002/prot.21391
95. Kuntz, I.D., J.M. Blaney, S.J. Oatley, R. Langridge and T.E. Ferrin, 1982. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol., 161: 269-288. DOI: 10.1016/0022-2836(82)90153-X
96. Laio, A. and M. Parrinello, 2002. Escaping free-energy minima. Proc. Natl. Acad. Sci., 99: 12562-12566. DOI: 10.1073/pnas.202427399
97. Leach, A.R., 1994. Ligand docking to proteins with discrete side-chain flexibility. J. Mol. Biol., 235: 345-356. DOI: 10.1016/S0022-2836(05)80038-5.0
98. Leblanc, R., 2006. Molecular recognition at Langmuir monolayers. Curr. Opinion Chem. Biol., 10: 529-536. DOI: 10.1016/j.cbpa.2006.09.010
99. Lee, S.G., G.F. Brunello, S.S. Jang and D.G. Bucknall, 2009. Molecular dynamics simulation study of P (VP-co-HEMA) hydrogels: Effect of water content on equilibrium structures and mechanical properties. Biomaterials, 30: 6130-6141. DOI: 10.1016/j.biomaterials.2009.07.035
100. Lin, P.H., C.W. Tsai, J.W. Wu, R.C. Ruaan and W.Y. Chen, 2012. Molecular dynamics simulation of the induced-fit binding process of DNA aptamer and Largininamide. Biotechnol. J., 7: 1367-1375. DOI: 10.1002/biot.201200003
101. Lindahl, E., B. Hess and D. Van Der Spoel, 2001. GROMACS 3.0: A package for molecular simulation and trajectory analysis. J. Mol. Model., 7: 306-317. DOI: 10.1007/s008940100045
102. Lindorff-Larsen, K., P. Maragakis, S. Piana, M.P. Eastwood and R.O. Dror et al., 2012. Systematic validation of protein force fields against experimental data. PLoS ONE, 7: e32131. DOI: 10.1371/journal.pone.0032131
103. Liu, J., H. Zhang, Z. Xiao, F. Wang and X. Wang et al., 2011. Combined 3D-QSAR, molecular docking and molecular dynamics study on derivatives of peptide epoxyketone and tyropeptin-boronic acid as inhibitors against the β5 Subunit of human 20S proteasome. Int. J. Mol. Sci., 12: 1807-1835. DOI: 10.3390/ijms12031807
104. Lu, S.J. and F.C. Chong, 2012. Combining molecular docking and molecular dynamics to predict the binding modes of flavonoid derivatives with the neuraminidase of the 2009 H1N1 influenza a virus. Int. J. Mol. Sci., 13: 4496-4507. DOI: 10.3390/ijms13044496
105. Mackerell, A.D., 2004. Empirical force fields for biological macromolecules: Overview and issues. J. Comput. Chem., 25: 1584-1604. DOI: 10.1002/jcc.20082
106. MacKerell, A.D., D. Bashford, R.L. Dunbrack, J.D. Evanseck and M.J. Field et al., 1998. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Physical Chem. B, 102: 3586-3616. DOI: 10.1021/jp973084f
107. Maithal, K., G. Ravindra, H. Balaram and P. Balaram, 2002. Inhibition of plasmodium falciparum triosephosphate isomerase by chemical modification of an interface cysteine: Electrospray ionization mass spectrometric analysis of differential cysteine reactivities. J. Biol. Chem., 277: 25106-25114. DOI: 10.1074/jbc.M202419200
108. Mangoni, M., D. Roccatano and A. Di Nola, 1999. Docking of flexible ligands to flexible receptors in solution by molecular dynamics simulation. Proteins, 35: 153-162. DOI: 10.1002/(SICI)1097-0134(19990501)35:2<153::AIDPROT2> 3.0.CO;2-E
109. Mangoni, P.R.L. and J.A. McCammon, 2011. Studying functional dynamics in bio-molecules using accelerated molecular dynamics. Phys. Chem. Chem. Phys., 13: 20053-20065. DOI: 10.1039/C1CP22100K
110. Mazur, P., T. Magdziarz, A. Bak, Z. Chilmonczyk and T. Kasprzycka-Guttman et al., 2010. Does molecular docking reveal alternative chemopreventive mechanism of activation of oxidoreductase by sulforaphane isothiocyanates. J. Mol. Model., 16: 1205-1212. DOI: 10.1007/s00894-009-0628-5
111. Meng, X.Y.Y., H.X.X. Zhang, M. Mezei and M. Cui, 2011. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Design, 7: 146-157. DOI: 10.2174/157340911795677602
112. Miranker, A. and M. Karplus, 1991. Functionality maps of binding sites: A multiple copy simultaneous search method. Proteins, 11: 29-34. DOI: 10.1002/prot.340110104
113. Morris, G.M., D.S. Goodsell, R.S. Halliday, R. Huey and W.E. Hart et al., 1998. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 19: 1639-1662. DOI: 10.1002/(SICI)1096-987X(19981115)19:14<1639::AIDJCC10> 3.0.CO;2-B
114. Muegge, I. and Y.C. Martin, 1999. A general and fast scoring function for protein-ligand interactions: A simplified potential approach. J. Med. Chem., 42: 791-804. DOI: 10.1021/jm980536j
115. Nichols, S.E., R. Baron, A. Ivetac and J.A. McCammon, 2011. Predictive power of molecular dynamics receptor structures in virtual screening. J. Chem. Inform. Model., 51: 1439-1446. DOI: 10.1021/ci200117n
116. Norel, R., D. Fischer, H.J. Wolfson and R. Nussinov, 1994. Molecular surface recognition by a computer vision-based technique. Protein Eng., 7: 39-46. DOI: 10.1093/protein/7.1.39
117. Novak, W., H. Wang and G. Krilov, 2009. Role of protein flexibility in the design of Bcl-XL targeting agents: Insight from molecular dynamics. J. Comput. Aided Mol. Design, 23: 49-61. DOI: 10.1007/s10822-008-9237-0
118. Novere, N.L. and J.P. Changeux, 2001. LGICdb: The ligand-gated ion channel database. Nucl. Acids Res., 29: 294-295. DOI: 10.1093/nar/29.1.294
119. Nowacki, J., H.M. Osinga and K. Tsaneva-Atanasova, 2012. Dynamical systems analysis of spike-adding mechanisms in transient bursts. J. Math. Neurosci., 2: 7-7. DOI: 10.1186/2190-8567-2-7
120. Nurisso, A., A. Daina and R.C. Walker, 2012. A practical introduction to molecular dynamics simulations: Applications to homology modeling. Methods Mol. Biol., 857: 137-173. DOI: 10.1007/978-1-61779-588-6_6
121. Ogungbe, I.V. and W.N. Setzer, 2009. Comparative molecular docking of antitrypanosomal natural products into multiple Trypanosoma brucei drug targets. Molecules, 14: 1513-1536. DOI: 10.3390/molecules14041513
122. Okazaki, K.I. and S. Takada, 2008. Dynamic energy landscape view of coupled binding and protein conformational change: Induced-fit versus population-shift mechanisms. Proc. Natl. Acad. Sci. USA., 105: 11182-11187. DOI: 10.1073/pnas.0802524105
123. Olivares-Illana, V., R. Perez-Montfort, F. Lopez-Calahorra, M. Costas and A. Rodriguez-Romero et al., 2006. Structural differences in triosephosphate isomerase from different species and discovery of a multitrypanosomatid inhibitor. Biochemistry, 45: 2556-2560. DOI: 10.1021/bi0522293
124. Oshiro, C.M., I.D. Kuntz and J.S. Dixon, 1995. Flexible ligand docking using a genetic algorithm. J. Comput. Aided Mol. Design, 9: 113-130. DOI: 10.1007/BF00124402
125. Ou-Yang, S., J. Lu, X. Kong, Z. Liang and C. Luo et al., 2012. Computational drug discovery. Acta Pharmacol. Sinica, 33: 1131-1140. DOI: 10.1038/aps.2012.109
126. Park, I.H.H. and C. Li, 2010. Dynamic ligand-induced-fit simulation via enhanced conformational samplings and ensemble dockings: A survivin example. J. Phys. Chem. B, 114: 5144-5153. DOI: 10.1021/jp911085d
127. Patapati, K.K. and N.M. Glykos, 2011. Three force fields' views of the 3 (10) helix. Biophys. J., 101: 1766-1771. DOI: 10.1016/j.bpj.2011.08.044
128. Paton, R.S. and J.M. Goodman, 2009. Hydrogen bonding and pi-stacking: How reliable are force fields. A critical evaluation of force field descriptions of nonbonded interactions. J. Chem. Inform. Model., 49: 944-955. DOI: 10.1021/ci900009f.
129. Pedretti, A., L. Villa and G. Vistoli, 2004. VEGA-an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. J. Comput. Aided Mol. Design, 18: 167-173. DOI: 10.1023/B:JCAM.0000035186.90683.f2
130. Phillips, J.C., R. Braun, W. Wang, J. Gumbart and E. Tajkhorshid et al., 2005 Scalable molecular dynamics with NAMD. J. Computat. Chem., 26: 1781-1802. DOI: 10.1002/jcc.20289
131. Pool, R., J. Heringa, M. Hoefling, R. Schulz and J.C. Smith et al., 2012. Enabling grand-canonical Monte Carlo: Extending the flexibility of GROMACS through the GromPy python interface module. J. Comput. Chem., 33: 1207-1214. DOI: 10.1002/jcc.22947
132. Price, D.J. and C.L. Brooks, 2002. Modern protein force fields behave comparably in molecular dynamics simulations. J. Computat. Chem., 23: 1045-1057. DOI: 10.1002/jcc.10083
133. Rahman, M.Z.A., A.B. Salleh, R.N.A. Rahman, M.B.A. Rahman and M. Basri et al., 2012. Unlocking the mystery behind the activation phenomenon of T1 lipase: A molecular dynamics simulations approach. Protein Sci., 21: 1210-1221. DOI: 10.1002/pro.2108
134. Rarey, M., B. Kramer, T. Lengauer and G. Klebe, 1996. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 261: 470-489. DOI: 10.1006/jmbi.1996.0477
135. Razzaghi-Asl, N., A. Ebadi, N. Edraki, A. Mehdipour and S. Shahabipour et al., 2012. Response surface methodology in docking study of small molecule BACE-1 inhibitors. J. Mol. Model., 18: 1-10. DOI: 10.1007/s00894-012-1424-1
136. Rebek, J., 2009. Molecular recognition and selfassembly special feature: Introduction to the molecular recognition and self-assembly special feature. Proc. Natl. Acad. Sci. USA., 106: 10423-10424. DOI: 10.1073/pnas.0905341106
137. Rodriguez, F. and D. Rodrik, 2001. Trade policy and economic growth: A skeptic's guide to the crossnational evidence. NBER Mocroecon. Annual.
138. Rodriguez-Romero, A., A. Hernandez-Santoyo, L.D.P. Yauner, A. Kornhauser and D.A. Fernandez- Velasco, 2002. Structure and inactivation of triosephosphate isomerase from Entamoeba histolytica. J. Mol. Biol., 322: 669-675. DOI: 10.1016/S0022-2836(02)00809-4
139. Rosas-Trigueros, J.L., J. Correa-Basurto, C.G. Benitez- Cardoza and A. Zamorano-Carrillo, 2011. Insights into the structural stability of Bax from molecular dynamics simulations at high temperatures. Protein Sci., 20: 2035-2046. DOI: 10.1002/pro.740
140. Roux, B., 2010. Perspectives on: Molecular dynamics and computational methods. J. General Physiol., 135: 547-548. DOI: 10.1085/jgp.201010456
141. Roy, A., A. Kucukural and Y. Zhang, 2010. I-TASSER: A unified platform for automated protein structure and function prediction. Nature Protocols, 5: 725-738. DOI: 10.1038/nprot.2010.5
142. Roy, K. and A.S.S. Mandal, 2008. Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones. J. Enzyme Inhibition Med. Chem., 23: 980-995. DOI: 10.1080/14756360701811379
143. Schlick, T., 2010. Molecular Modeling and Simulation: An Interdisciplinary Guide. 2nd Edn., Springer, New York, ISBN-10: 9781441963505, pp: 723.
144. Schneider, G. and U. Fechner, 2005. Computer-based de novo design of drug-like molecules. Nature Rev. Drug Discovery, 4: 649-663. DOI: 10.1038/ nrd1799
145. Serletis, D., O.C. Zalay, T.A. Valiante, B.L. Bardakjian and P.L. Carlen, 2011. Complexity in neuronal noise depends on network interconnectivity. Annals Biomed. Eng., 39: 1768-1778. DOI: 10.1007/s10439-011-0281-x
146. Shaw, D.E., P. Maragakis, K. Lindorff-Larsen, S. Piana and R.O. Dror et al., 2010. Atomic-level characterization of the structural dynamics of proteins. Science, 330: 341-346. DOI: 10.1126/science.1187409
147. Smith, G.R., P.W. Fitzjohn, C.S. Page and P.A. Bates, 2005. Incorporation of flexibility into rigid-body docking: Applications in rounds 3-5 of CAPRI. Proteins, 60: 263-268. DOI: 10.1002/prot.20568
148. Srinivasan, J., T.E. Cheatham, P. Cieplak, P.A. Kollman and D.A. Case, 1998. Continuum solvent studies of the stability of DNA, RNA and phosphoramidate- DNA helices. J. Am. Chem. Soci., 120: 9401-9409. DOI: 10.1021/ja981844+
149. Still, W.C., A. Tempczyk, R.C. Hawley and T. Hendrickson, 1990. Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Soc., 112: 6127-6129. DOI: 10.1021/ja00172a038
150. Sugita, Y. and Y. Okamoto, 1999. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett., 314: 141-151. DOI: 10.1016/S0009-2614(99)01123-9
151. Takamori, S., 2006. VGLUTs: 'Exciting' times for glutamatergic research? Neurosci. Res., 55: 343-351. DOI: 10.1016/j.neures.2006.04.016
152. Tellez-Valencia, A., V. Olivares-Illana, A. Hernandez-Santoyo, R. Perez-Montfort and M. Costas et al., 2004. Inactivation of triosephosphate isomerase from trypanosoma cruzi by an agent that perturbs its dimer interface. J. Mol. Biol., 341: 1355-1365. DOI: 10.1016/j.jmb.2004.06.056
153. Tilocca, A., 2012. Molecular dynamics methods for modeling complex interactions in biomaterials. Methods Mol. Biol., 811: 285-301. DOI: 10.1007/978-1-61779-388-2_18
154. Trott, O. and A.J. Olson, 2010. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem., 31: 455-461. DOI: 10.1002/jcc.21334
155. Tsai, H.H.G.H., W.X.X. Lai, H.D.D. Lin, J.B.B. Lee and W.F.F. Juang et al., 2012. Molecular dynamics simulation of cation-phospholipid clustering in phospholipid bilayers: Possible role in stalk formation during membrane fusion. Biochim. Biophys. Acta-Biomembranes, 1818: 2742-2755. DOI: 10.1016/j.bbamem.2012.05.029
156. Verdonk, M.L., J.C. Cole, M.J. Hartshorn, C.W. Murray and R.D. Taylor, 2003. Improved protein-ligand docking using GOLD. Proteins, 52: 609-623. DOI: 10.1002/prot.1046
157. Verkhivker, G.M., D. Bouzida, D.K. Gehlhaar, P.A. Rejto and S. Arthurs et al., 2000. Deciphering common failures in molecular docking of ligandprotein complexes. J. Comput. Aided Mol. Design, 14: 731-751. DOI: 10.1023/A:1008158231558
158. Veselovsky, A.V. and A.S. Ivanov, 2003. Strategy of computer-aided drug design. Curr. Drug Targets Infect. Disorders, 3: 33-40. DOI: 10.2174/1568005033342145
159. Vitorovic-Todorovic, M.D., I.O. Juranic, L.M. Mandic and B.J. Drakulic, 2010. 4-Aryl-4-oxo-N-phenyl-2- aminylbutyramides as acetyl- and butyrylcholinesterase inhibitors. Preparation, anticholinesterase activity, docking study and 3D structure-activity relationship based on molecular interaction fields. Bioorganic Med. Chem., 18: 1181-1193. DOI: 10.1016/j.bmc.2009.12.042
160. Vries, S.J.D., A.D.V. Dijk, M. Krzeminski, M.V. Dijk and A. Thureau et al., 2007. HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets. Proteins, 69: 726-733. PMID: 17803234
161. Wang, J., P. Cieplak, J. Li, T. Hou and R. Luo et al., 2011. Development of polarizable models for molecular mechanical calculations I: Parameterization of atomic polarizability. J. Phys. Chem. B, 115: 3091-3099. DOI: 10.1021/jp112133g
162. Weiner, S.J., P.A. Kollman, D.A. Case, U.C. Singh and C. Ghio et al., 1984. A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc., 106: 765-784. DOI: 10.1021/ja00315a051
163. Wu, J.C., G. Chattree and P. Ren, 2012. Automation of AMOEBA polarizable force field parameterization for small molecules. Theoretical Chem. Accounts, 131: 1-11. DOI: 10.1007/s00214-012-1138-6
164. Yu, F.H., V. Yarov-Yarovoy, G.A. Gutman and W.A. Catterall, 2005. Overview of molecular relationships in the voltage-gated ion channel superfamily. Pharmacol. Rev., 57: 387-395. DOI: 10.1124/pr.57.4.13
165. Yue, S.Y., 1990. Distance-constrained molecular docking by simulated annealing. Protein Eng., 4: 177-184. DOI: 10.1093/protein/4.2.177
166. 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, 54: 759-767. DOI: 10.1002/prot.10637
167. Zare, B., A. Madadkar-Sobhani, S. Dastmalchi and M. Mahmoudian, 2011. Prediction of the human EP1 receptor binding site by homology modeling and molecular dynamics simulation. Sci. Pharm., 79: 793-816. DOI: 10.3797/scipharm.1106-24
168. Zhao, Y., A. De Nicola, T. Kawakatsu and G. Milano, 2012. Hybrid particle-field molecular dynamics simulations: Parallelization and benchmarks. J. Comput. Chem., 33: 868-880. DOI: 10.1063/1.3142103