Recent progress of molecular docking simulations applied to development of drugs

Current Bioinformatics

Volumn 7, Issue 4, 2012, Pages 352-365

  Azevedo, Linus Santana ^a   Moraes, Fernanda Pretto ^b   Xavier, Mariana Morrone ^b   Pantoja, Eduarda Ozório ^b   Villavicencio, Bianca ^b   Finck, Jana Aline ^b   Proença, Audrey Menegaz ^b   Rocha, Kelen Beiestorf ^b,c   de Azevedo, Walter Filgueira ^b  

^a Colégio Anchieta Associação Antônio Vieira ^*  (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

^c FEDERAL UNIVERSITY OF RIO GRANDE   (Brazil)

Author keywords

Differential evolution; Evolutionary algorithms; Molecular docking; Protein drug; Simulation; Structure based virtual screening

Indexed keywords

DRUG INTERACTIONS; ENZYMES; MOLECULAR MODELING; OPTIMIZATION;

DIFFERENTIAL EVOLUTION; MOLECULAR DOCKING; MOLECULAR DOCKING SIMULATIONS; PROTEIN A; PROTEIN DRUGS; PROTEIN TARGETS; SIMULATION; STRUCTURE-BASED; STRUCTURE-BASED VIRTUAL SCREENING; VIRTUAL SCREENING;

EVOLUTIONARY ALGORITHMS;

ACETYLCHOLINESTERASE; CYCLIN DEPENDENT KINASE 2; PHOSPHOTRANSFERASE; PURINE NUCLEOSIDE PHOSPHORYLASE; SHIKIMATE KINASE; UNCLASSIFIED DRUG;

ALGORITHM; DIFFERENTIAL EVOLUTION ALGORITHM; EVOLUTIONARY ALGORITHM; GUIDED DIFFERENTIAL EVOLUTION ALGORITHM; MACHINE LEARNING; MOLECULAR DOCKING; MOLEGRO VIRTUAL DOCKER; PRIORITY JOURNAL; REVIEW;

EID: 84874831415     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489312803901063     Document Type: Review

References (163)

1. Zacharias M. Accounting for conformational changes during protein-protein docking. Curr Opin Struct Biol 2010; 20(2): 180-186.
2. Vajda S, Kozakov D. Convergence and combination of methods in protein-protein docking. Curr Opin Struct Biol 2009; 19(2): 164-1670.
3. Andrusier N, Mashiach E, Nussinov R, et al. Principles of flexible protein-protein docking. Proteins 2008; 73(2): 271-89.
4. Gao M, Skolnick J. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions. PLoS Comput Biol 2009; 5(3): e1000341.
5. Klebe G, Böhm HJ. Energetic and entropic factors determining binding affinity in protein-ligand complexes. J Recept Signal Transduct Res 1997; 17(1-3): 459-473.
6. Böhm HJ. 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(3): 243-256.
7. Böhm HJ. Current computational tools for de novo ligand design. Curr Opin Biotechnol 1996; 7(4): 433-436.
8. Böhm HJ. 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(4): 309-3023.
9. Stahl M, Böhm HJ. Development of filter functions for protein-ligand docking. J Mol Graph Model 1998; 16(3): 121-132.
10. 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(1): 11-26.
11. De Azevedo Jr. WF, Dias R. Computational methods for calculation ligand-binding affinity. Curr Drug Targets 2008; 9:1031-1039.
12. Verkhivker G, Appelt K, Freer ST, et al. 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.
13. Huang S-Y, Zou X. Mean-force scoring functions for protein-ligand binding. Annu Rep Comput Chem 2010; 6: 281-296.
14. Koppensteiner WA, Sippl MJ. Knowledge-based potentials - Back to the roots. Biochemistry (Moscow) 1998; 63: 247-252.
15. Thomas PD, Dill KA. Statistical potentials extracted from protein structures: How accurate are they? J Mol Biol 1996; 257: 457-469.
16. Charifson PS, Corkery JJ, Murcko MA, et al. 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.
17. Wang R, Wang S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J Chem Inf Comput Sci 2001; 41: 1422-1426.
18. Clark RD, Strizhev A, Leonard JM, et al. Consensus scoring for ligand/protein interactions. J Mol Graph Model 2002; 20: 281-295.
19. Terp GE, Johansen BE, Christensen IT, et al. A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities. J Med Chem 2001; 44: 2333-2343.
20. Ruvinsky AM. Calculations of protein-ligand binding entropy of relative and overall molecular motions. J Comput.-Aided Mol Des 2007; 21: 361-370.
21. Chang MW, Belew RK, Carroll KS, et al. Empirical entropic contributions in computational docking: Evaluation in APS reductase complexes. J Comput Chem 2008; 29: 1753-1761.
22. Lee J, Seok C. A statistical rescoring scheme for protein-ligand docking: Consideration of entropic effect. Proteins 2008; 70: 1074-1083.
23. Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci 2010; 11(8): 3016-3034.
24. Dias R, De Azevedo Jr. WF. Molecular docking algorithms. Curr Drug Targets, 2008; 9:1040-1047.
25. Bellows ML, Floudas CA. Computational methods for de novo protein design and its applications to the human immunodeficiency virus 1, purine nucleoside phosphorylase, ubiquitin specific protease 7, and histone demethylases. Curr Drug Targets 2010; 11: 264-278.
26. De Azevedo Jr. WF. MolDock Applied to Structure-Based Virtual Screening. Curr Drug Targets 2010; 11: 327-334.
27. nd ed. Essex: Pearson Prentice Hall 2001.
28. Liu M, Wang S. MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J Comput Aided Mol Des 1999; 13: 435-451.
29. Rosenfeld RJ, Goodsell DS, Musah RA, et al. Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling. J Comput Aided Mol Des 2003; 17: 525-536.
30. Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 1996; 9: 1-5.
31. Kuntz ID, Blaney JM, Oatley SJ, et al. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982; 161: 269-288.
32. Rarey M, Kramer B, Lengauer T, et al. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261: 470-489.
33. Kramer B, Rarey M, Lengauer T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 1999; 37: 228-241.
34. Moré JJ, Wu Z. Distance geometry optimization for protein structures. J Global Optim 1999; 15: 219-234.
35. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983; 220: 671-680.
36. Goodsell DS, Olson AJ. Automated docking of substrates to proteins by simulated annealing. Proteins-Struct Funct Genet 1990; 8: 195-202.
37. Baxter CA, Murray CW, Clark DE, et al. Proteins, 1998; 33: 367-382.
38. Judson RS, Jaeger EP, Treasurywala AM. A genetic algorithm-based method for docking flexible molecules. J Mol Struc: Theochem 1994; 114: 191-206.
39. Jones G, Willett P, Glen RC. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 1995; 245(1): 43-53.
40. Jones G, Willett P, Glen RC. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 1995; 9(6): 532-549.
41. Jones G, Willett P, Glen RC, et al. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-748.
42. Oshiro CM, Kuntz ID, Dixon JS. Flexible ligand docking using a genetic algorithm. J Comput Aided Mol Des 1995; 9(2): 113-130.
43. Moitessier N, Englebienne P, Lee D, et al. 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-S26.
44. Gehlhaar DK, Verkhivker GM, Rejto PA, et al. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol?; 2(5): 317-324.
45. Feoktistov V. Differential evolution. In search of solutions. New York: Springer Science+Business Media LLC 2006.
46. Chakraborty UK, Ed. Advances in differential evolution. Heidelberg: Springer-Verlag 2008.
47. Storn R, Price K. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 1997; 11(4): 341-59.
48. Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking. J Med Chem 2006; 49: 3315-3321.
49. Darwin C. The origin of Species. London: John Murray 1859.
50. Heberlé G, De Azevedo Jr. WF. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 2011; 18(9): 1339-52.
51. Zaheer-ul-Haq, Khan W. Molecular and structural determinants of adamantyl susceptibility to HLA-DRs allelic variants: an in silico approach to understand the mechanism of MLEs. J Comput Aided Mol Des 2011; 25(1): 81-101.
52. Plazuk D, Zakrzewski J, Salmain M. Biotin as acylating agent in the Friedel-Crafts reaction. Avidin affinity of biotinyl derivatives of ferrocene, ruthenocene and pyrene and fluorescence properties of 1-biotinylpyrene. Org Biomol Chem 2011; 9: 408-417.
53. Seal A, Aykkal R, Babu RO, et al. Docking study of HIV-1 reverse transcriptase with phytochemicals. Bioinformation 2011; 5(10): 430-439.
54. Ferreira SB, Sodero ACR, Cardoso MFC, et al. Synthesis, Biological Activity, and Molecular Modeling Studies of 1H-1,2,3-Triazole Derivatives of Carbohydrates as -Glucosidases Inhibitors J Med Chem 2010; 53 (6): 2364-2375.
55. Araújo JQ, Lima JA, Pinto Ada C, et al. Docking of the alkaloid geissospermine into acetylcholinesterase: a natural scaffold targeting the treatment of Alzheimer's disease. J Mol Model 2011; 17(6): 1401-1412.
56. Cassidy CE, Setzer WN. Cancer-relevant biochemical targets of cytotoxic Lonchocarpus flavonoids: A molecular docking analysis. J Mol Model 2010; 16: 311-326.
57. Paul SB, Choudhury S. Computational analysis of the activity of pongachalcone I against highly resistant bacteria Pseudomonas putida. Bioinformation 2010; 4(10): 473-477.
58. Shityakov S, Dandekar T. Lead expansion and virtual screening of Indinavir derivate HIV-1 protease inhibitors using pharmacophoric-shape similarity scoring function. Bioinformation 2010; 4(7): 295-299.
59. Zeng ZS, Liang YH, Feng XQ, et al. Lead optimization of diarylpyrimidines as non-nucleoside inhibitors of HIV-1 reverse transcriptase. ChemMedChem 2010; 5(6): 837-840.
60. Boyapati S, Kulandaivelu U, Sangu S, et al. Synthesis, antimicrobial evaluation, and docking studies of novel 4-substituted quinazoline derivatives as DNA-gyrase inhibitors. Arch Pharm (Weinheim) 2010; 343(10): 570-576.
61. Yang L, Liu Y, Sternberg C, et al. Evaluation of enoyl-acyl carrier protein reductase inhibitors as Pseudomonas aeruginosa quorum-quenching reagents. Molecules 2010; 15(2): 780-792.
62. Ehrman TM, Barlow DJ, Hylands PJ. In silico search for multi-target anti-inflammatories in Chinese herbs and formulas. Bioorg Med Chem 2010; 18(6): 2204-2218.
63. Jimsheena VK, Gowda LR. Arachin derived peptides as selective angiotensin I-converting enzyme (ACE) inhibitors: structure-activity relationship. Peptides 2010; 31(6): 1165-1176.
64. Büchele B, Zugmaier W, Lunov O, et al. Surface Plasmon resonance analysis of nuclear factor-kappaB protein interactions with the sesquiterpene lactone helenalin. Anal Biochem 2010; 401(1): 30-37.
65. Ducati RG, Basso LA, Santos DS, et al. Crystallographic and docking studies of purine nucleoside phosphorylase from Mycobacterium tuberculosis. Bioorg Med Chem 2010; 18(13): 4769-4774.
66. Estrada AC, Syrovets T, Pitterle K, et al. Tirucallic acids are novel pleckstrin homology domain-dependent Akt inhibitors inducing apoptosis in prostate cancer cells. Mol Pharmacol 2010; 77(3): 378-387.
67. Thareja S, Aggarwal S, Bhardwaj TR, et al. Self-organizing molecular field analysis of 2,4-thiazolidinediones: A 3D-QSAR model for the development of human PTP1B inhibitors. Eur J Med Chem 2010; 45(6): 2537-2546.
68. Thareja S, Kokil GR, Aggarwal S, et al. Sulphonamides as Inhibitors of Protein Tyrosine Phosphatase 1B: A Three-Dimensional Quantitative Structure-Activity Relationship Study Using Self-Organizing Molecular Field Analysis Approach. Chem Pharm Bull (Tokyo) 2010; 58(4): 526-532
69. Jin Z, Yang L, Liu SJ, et al. Synthesis and biological evaluation of 3,6-diaryl-7H-thiazolo[3,2-b] [1,2,4]triazin-7-one derivatives as acetylcholinesterase inhibitors. Arch Pharm Res 2010; 33(10): 1641-1649.
70. Danalache BA, Gutkowska J, Slusarz MJ, et al. Oxytocin-Gly-Lys-Arg: a novel cardiomyogenic peptide. PLoS One 2010; 5(10): e13643.
71. Muftuoglu Y, Mustata G. Pharmacophore modeling strategies for the development of novel nonsteroidal inhibitors of human aromatase (CYP19). Bioorg Med Chem Lett 2010; 20(10): 3050-3064.
72. Ramalho TC, Tanos CC, Rennó MN, et al. Development of new acetylcholinesterase reactivators: molecular modeling versus in vitro data. Chem Biol Interact 2010; 187(1-3): 436-440.
73. Zeng ZS, He QQ, Liang YH, et al. Hybrid diarylbenzopyrimidine non-nucleoside reverse transcriptase inhibitors as promising new leads for improved anti-HIV-1 chemotherapy. Bioorg Med Chem 2010; 18(14): 5039-5047
74. Wang S, Yan J, Wang J, et al. Synthesis of some 5-phenylisoxazole-3-carboxylic acid derivatives as potent xanthine oxidase inhibitors. Eur J Med Chem 2010; 45(6): 2663-2670
75. Tan JJ, Cong XJ, Hu LM, et al. Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection. Drug Discov Today 2010; 15(5-6): 186-197.
76. Provazzi PJ, Arcuri HA, de Carvalho-Mello IM, et al. Structural studies of Helicase NS3 variants from Hepatitis C virus genotype 3 in virological sustained responder and non-responder patients. BMC Res Notes 2010; 3:196.
77. Sun Q, Hirasawa A, Hara T, et al. Structure-activity relationships of GPR120 agonists based on a docking simulation. Mol Pharmacol 2010; 78(5): 804-810.
78. Sapre NS, Gupta S, Pancholi N, et al. A group center overlap based approach for 3D QSAR studies on TIBO derivatives. J Comput Chem 2009; 30(6): 922-933.
79. Khan MT, Fuskevåg OM, Sylte I. Discovery of potent thermolysin inhibitors using structure based virtual screening and binding assays. J Med Chem 2009; 52(1): 48-61.
80. Pina AS, Roque AC. Studies on the molecular recognition between bioactive peptides and angiotensin-converting enzyme. J Mol Recognit 2009; 22(2): 162-168.
81. Honda T, Terao T, Aono H, et al. Synthesis of novel 1,4-benzoxazin-3-one derivatives as inhibitors against tyrosine kinases. Bioorg Med Chem 2009; 17(2): 699-708.
82. Ogungbe IV, Setzer WN. Comparative molecular docking of antitrypanosomal natural products into multiple Trypanosoma brucei drug targets. Molecules 2009; 14(4): 1513-1536.
83. Beausoleil E, Chauvignac C, Taverne T, et al. Structure-activity relationship of isoform selective inhibitors of Rac1/1b GTPase nucleotide binding. Bioorg Med Chem Lett 2009; 19(19): 5594-5598.
84. Sivaprakasam P, Tosso PN, Doerksen RJ. Structure-activity relationship and comparative docking studies for cycloguanil analogs as PfDHFR-TS inhibitors. J Chem Inf Model 2009; 49(7): 1787-1796.
85. Yang L, Rybtke MT, Jakobsen TH, et al. Computer-aided identification of recognized drugs as Pseudomonas aeruginosa quorum-sensing inhibitors. Antimicrob Agents Chemother 2009; 53(6): 2432-2443.
86. Bains J, Leon R, Boulanger MJ. Structural and biophysical characterization of BoxC from Burkholderia xenovorans LB400: a novel ring-cleaving enzyme in the crotonase superfamily. J Biol Chem 2009; 284(24): 16377-16385.
87. Platis D, Maltezos A, Ma JK, et al. Combinatorial de novo design and application of a biomimetic affinity ligand for the purification of human anti-HIV mAb 4E10 from transgenic tobacco. J Mol Recognit 2009; 22(6): 415-424.
88. Haigh JM, Hussain A, Mimmack ML, et al. Affinity ligands for immunoglobulins based on the multicomponent Ugi reaction. J Chromatogr B Analyt Technol Biomed Life Sci 2009; 877(14-15): 1440-1452.
89. Rudolf B, Salmain M, Martel A, et al. eta(1)-N-succinimidato complexes of iron, molybdenum and tungsten as reversible inhibitors of papain. J Inorg Biochem 2009; 103(8): 1162-1168.
90. Chauhan P, Shakya M. Model based design of inhibitors for c-jun. Bioinformation 2009; 4(6): 223-228.
91. Mitrasinovic PM. On the structure-based design of novel inhibitors of H5N1 influenza A virus neuraminidase (NA). Biophys Chem 2009; 140(1-3): 35-38.
92. Mitrasinovic PM. Advances in the structure-based design of the influenza A neuraminidase inhibitors. Curr Drug Targets 2010; 11(3): 315-26.
93. Ewing TJ, Makino S, Skillman AG, et al. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15: 411-428.
94. Yang JM Development and evaluation of a generic evolutionary method for protein-ligand docking. J Comput Chem 2004; 25: 843-857.
95. Yang JM, Chen CC. GEMDOCK: a generic evolutionary method for molecular docking. Proteins 2004; 55: 288-304.
96. Friesner RA, Banks JL, Murphy RB, et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem 2004; 47: 1739-1749.
97. Halgren TA, Murphy RB, Friesner RA, et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening. J Med Chem 2004; 47: 1750-1759.
98. Kirkpatrick P. Virtual screening: Gliding to success. Nat Rev Drug Discov 2004; 3: 299-309.
99. Krovat EM, Steindl T, Langer T. Recent Advances in Docking and Scoring. Curt Computer-Aided Drug Design 2005; 1: 93-102.
100. Friesner RA, Murphy RB, Repasky MP, et al. Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes. J Med Chem 2006; 49: 6177-96.
101. Verdonk ML, Cole JC, Hartshorn MJ, et al. Improved protein-ligand docking using GOLD. Proteins 2003; 52: 609-623.
102. Joy S, Nair PS, Hariharan R, et al. Detailed comparison of the protein-ligand docking efficiencies of GOLD, a commercial package and ArgusLab, a licensable freeware. In Silico Biol 2006; 6: 601-605.
103. Jain AN. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 2003; 46: 499-511.
104. Canduri F, De Azevedo Jr WF. Protein crystallography in drug discovery. Curr Drug Targets 2008; 9: 1048-1053.
105. Thilagavathi R, Mancera RL. Ligand-protein cross-docking with water molecules. J Chem Inf Model 2010; 50: 415-421.
106. Dias MV, Snee WC, Bromfield KM, et al. Structural investigation of inhibitor designs targeting 3-dehydroquinate dehydratase from the shikimate pathway of Mycobacterium tuberculosis. Biochem J 2011; 436(3): 729-739.
107. Nelder JA, Mead R. A simplex method for function minimization. Comput J 1965; 7(4): 308-313.
108. rd ed. New York: Cambridge University Press 2007.
109. nd ed. Cambridge: Cambridge University Press 2010.
110. Eiben AE, Smith SJ. Introduction to Evolutionary Computing. Berlin: Springer-Verlag 2007.
111. Coley DA. An Introduction to Genetic Algorithms for Scientist and Engineers. Singapore: World Scientific Publishing Co. Pte. Ltd. TohTuck Link 1999.
112. Goldberg DE. Genetic Algorithms in Search, Optimization, and Machine Learning. New York: Addilson-Wiley 1989.
113. Holland JH. Adaptation in Natural and Artificial Systems. Cambridge: MIR Press 1992.
114. Olariu S, Zomaya AY, Eds. Handbook of Bioinspired Algorithms and Applications. Boca Raton: Taylor & Francis Group, LLC 2006.
115. Shoemake K. In: Kirk D. Ed. Graphics Gems III. Boston: AP Professional (Academic Press) 1992; pp 124-132.
116. Korb O, Stutzle T, Exner TE. Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS. J Chem Inf Model 2009; 49: 84-96.
117. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem 2006; 49: 6789-6801.
118. Brooijmans N, Sharp KA, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 2003; 32: 335-373.
119. Miteva MA, Violas S, Montes M, et al. FAF-drugs: free adme/tox filtering of compound collections. Nucleic Acids Res 2006; 34: W738-W744.
120. Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Ver 1997; 23: 3-25.
121. Vianna CP, de Azevedo WF Jr. Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J Mol Model 2012; 18(2): 755-764.
122. Rudnitskaya A, Török B, Török M. Molecular docking of enzyme inhibitors: A computational tool for structure-based drug design. Biochem Mol Biol Educ 2010; 38(4): 261-265.
123. Bermúdez-Lugo JA, Rosales-Hernández MC, Deeb O, et al. In silico methods to assist drug developers in acetylcholinesterase inhibitor design. Curr Med Chem 2011; 18(8): 1122-1136.
124. Harel M, Schalk I, Ehret-Sabatier L, et al. Quaternary ligand binding to aromatic residues in the active-site gorge of acetylcholinesterase. Proc Natl Acad Sci USA 1993; 90: 9031-9035.
125. Rizzolio F, Tuccinardi T, Caligiuri, I, et al. CDK inhibitors: from the bench to clinical trials. Curr Drug Targets 2010; 11: 279-290.
126. Krystof V, Uldrijan S. Cyclin-dependent kinase inhibitors as anticancer drugs. Curr Drug Targets 2010; 11: 291-302.
127. Hernandes MZ, Cavalcanti SM, Moreira DR, et al. Halogen atoms in the modern medicinal chemistry: hints for the drug design. Curr Drug Targets 2010; 11: 303-314.
128. Canduri F, de Azevedo WF Jr. Structural basis for interaction of inhibitors with Cyclin-Dependent Kinase 2. Curr Computer-Aided Drug Design 2005; 1(1): 53-64.
129. Lawrie AM, Noble ME, Tunnah P, et al. Protein kinase inhibition by staurosporine revealed in details of the molecular interaction with CDK2. Nat Struct Biol 1997; 4: 796-801.
130. Scheer M, Grote A, Chang A, et al. BRENDA, the enzyme information system in 2011. Nucleic Acids Res 2011; 39(Database issue): D670-D676.
131. Otyepka M, Krystof V, Havlícek L, et al. Docking-based development of purine-like inhibitors of cyclin-dependent kinase-2. J Med Chem 2000; 43(13): 2506-2513.
132. De Azevedo Jr WF, Mueller-Dieckmann HJ, Schulze-Gahmen U, et al. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci USA 1996; 93: 2735-2740.
133. De Azevedo Jr WF, Leclerc S, Meijer L, et al. Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem 1997; 243: 518-526.
134. Krystof V, Cankar P, Frysová I, et al. 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J Med Chem 2006; 49(22): 6500-6509.
135. Murray AW, Elliott DC, Atkinson MR. Nucleotide biosynthesis from preformed purines in mammalian cells: regulatory mechanisms and biological significance. Prog Nucleic Acid Res Mol Biol 1970; 10: 87-119.
136. Murray AW. The biological significance of purine salvage. Annu Rev Biochem 1971; 40: 811-26.
137. Zhang Y, Ealick SE. In: Stroud RM, Finer-Moore J, Ed. Computational and structural approaches to drug discovery ligand-protein interactions. Cambridge: The Royal Soc Chem Pub 2008; pp. 49-72.
138. De Azevedo Jr WF, Canduri F, Santos DM, et al. Crystal structure of human purine nucleoside phosphorylase at 2.3 A resolution. Biochem Biophys Res Commun 2003; 308: 545-552.
139. Santos DM, Canduri F, Pereira JH, et al. Crystal structure of human purine nucleoside phosphorylase complexed with acyclovir. Biochem Biophys Res Commun 2003; 308: 553-559.
140. De Azevedo Jr WF, Canduri F, Dos Santos DM, et al. Structural basis for inhibition of human PNP by immucillin-H. Biochem Biophys Res Commun 2003; 309: 917-922.
141. De Azevedo Jr WF, Santos GC, Santos DM, et al. Docking and small angle X-ray scattering studies of purine nucleoside phosphorylase. Biochem Biophys Res Commun 2003; 309: 923-928.
142. De Azevedo Jr WF, Canduri F, dos Santos DM, et al. Crystal structure of human PNP complexed with guanine. Biochem Biophys Res Commun 2003; 312: 767-772.
143. Canduri F, dos Santos DM, Silva RG, et al. Structures of human Purine Nucleoside Phosphorylase complexed with Inosine and ddI. Biochem Biophys Res Commun 2004; 313: 907-914.
144. Da Silveira NJF, Uchoa HB, Canduri F, et al. Structural Bioinformatics Study of PNP from Schistosoma mansoni. Biochem Biophys Res Commun 2004; 322: 100-104.
145. Nolasco DO, Canduri F, Pereira JH, et al. Crystallographic structure of PNP from Mycobacterium tuberculosis at 1.9 A resolution. Biochem Biophys Res Commun 2004; 324: 789-794.
146. Canduri F, Fadel V, Dias MVB, et al. Crystal structure of human PNP complexed with hypoxanthine and sulfate ion. Biochem Biophys Res Commun 2005; 326: 335-338.
147. Canduri F, Fadel V, Basso LA, et al. New catalytic mechanism for human purine nucleoside phosphorylase. Biochem Biophys Res Commun 2005; 327: 646-649.
148. Canduri F, Silva RG, dos Santos DM, et al. Structure of human PNP complexed with ligands. Acta Crystallogr D Biol Crystallogr 2005; 61: 856-862.
149. Silva RG, Pereira JH, Canduri F, et al. Kinetics and crystal structure of human purine nucleoside phosphorylase in complex with 7-methyl-6-thio-guanosine. Arch Biochem Biophys 2005; 442: 49-58.
150. De Azevedo Jr WF, Canduri F, Basso LA et al. Determining the structural basis for specificity of ligands using crystallographic screening. Cell Biochem Biophys 2006; 44: 405-411.
151. Timmers LFSM, Caceres RA, Vivan AL, et al. Structural studies of human purine nucleoside phosphorylase: towards a new specific empirical scoring function. Arch Biochem Biophys 2008; 479: 28-38.
152. Caceres RA, Saraiva Timmers LF, Dias R, et al. Molecular modeling and dynamics simulations of PNP from Streptococcus agalactiae. Bioorg Med Chem 2008; 16: 4984-4993.
153. Timmers LFSM, Caceres RA, Basso LA, et al. Structural Bioinformatics Study of PNP from Listeria monocytogenes. Prot Pept Lett 2008; 15: 843-849.
154. Pauli I, Timmers LFSM, Caceres RA, et al. Molecular modeling and dynamics studies of Purine Nucleoside Phosphorylase from Bacteroides fragilis. J Mol Model 2009; 15: 913-922.
155. Timmers LFSM, Caceres RA, Dias R, et al. Molecular modeling, dynamics and docking studies of Purine Nucleoside Phosphorylase from Streptococcus pyogenes. Biophys Chem 2009; 142: 7-16.
156. Shi W, Basso LA, Santos DS, et al. Structures of purine nucleoside phosphorylase from Mycobacterium tuberculosis in complexes with immucillin-H and its pieces. Biochemistry 2001; 40: 8204-8215.
157. De Azevedo Jr WF, Canduri F, Simões de Oliveira J, et al. Molecular model of shikimate kinase from Mycobacterium tuberculosis. Biochem Biophys Res Commun 2002; 295: 142-8.
158. Krell T, Coggins JR, Lapthorn AJ. The three-dimensional structure of shikimate kinase. J Mol Biol 1998; 278: 983-997.
159. Pereira JH, Oliveira JS, Canduri F, et al. Acta Crystallogr D Biol Crystallogr 2004; 60:2310-2319.
160. Gu Y, Reshetnikova L, Li Y, et al. Crystal structure of shikimate kinase from Mycobacterium tuberculosis reveals the dynamic role of the LID domain in catalysis. J Mol Biol 2002; 319: 779-789.
161. Parish T, Stoker NG. The common aromatic amino acid biosynthesis pathway is essential in Mycobacterium tuberculosis. Microbiology 2002; 148: 3069-77.
162. Segura-Cabrera A, Rodriguez-Perez MA. Structure-based prediction of Mycobacterium tuberculosis shikimate kinase inhibitors by high-throughput virtual screening. Bioorg Med Chem Lett 2008; 18: 3152-3157.
163. Kumar M, Verma S, Sharma S, et al. Structure-based in silico design of a high-affinity dipeptide inhibitor for novel protein drug target Shikimate kinase of Mycobacterium tuberculosis. Chem Biol Drug Des 2010; 76: 277-284.