Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures

Journal of Chemical Information and Modeling

Volumn 53, Issue 10, 2013, Pages 2743-2756

  Tian, Sheng ^a   Sun, Huiyong ^a   Li, Youyong ^a   Pan, Peichen ^a   Li, Dan ^b   Hou, Tingjun ^a,b  

^a SOOCHOW UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; PHARMACODYNAMICS; PROTEINS; REGRESSION ANALYSIS;

BAYESIAN CLASSIFICATION; CRYSTALLOGRAPHIC STRUCTURE; POTENTIAL INHIBITORS; PROTEIN STRUCTURES; RECURSIVE PARTITIONING; TRADITIONAL CHINESE MEDICINE; VIRTUAL SCREENING; VIRTUAL SCREENING STRATEGIES;

MOLECULAR MODELING;

HERBACEOUS AGENT; LIGAND; PROTEIN KINASE INHIBITOR; RHO KINASE; ROCK1 PROTEIN, HUMAN;

ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BINDING SITE; CHEMISTRY; COMPUTER INTERFACE; DRUG ANTAGONISM; DRUG DEVELOPMENT; HIGH THROUGHPUT SCREENING; HUMAN; MOLECULAR DOCKING; PROTEIN BINDING; PROTEIN CONFORMATION; STRUCTURE ACTIVITY RELATION; X RAY CRYSTALLOGRAPHY;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BINDING SITES; CRYSTALLOGRAPHY, X-RAY; DRUG DISCOVERY; DRUGS, CHINESE HERBAL; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN KINASE INHIBITORS; RHO-ASSOCIATED KINASES; STRUCTURE-ACTIVITY RELATIONSHIP; USER-COMPUTER INTERFACE;

EID: 84887042090     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400382r     Document Type: Article

References (56)

1. Anson, B. D.; Ma, J.; He, J.-Q. Identifying Cardiotoxic Compounds Genet. Eng. Biotechnol. News 2009, 29, 34-35
2. Bajorath, F. Integration of virtual and high-throughput screening Nat Rev. Drug Discov. 2002, 1, 882-894
3. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations Drug Discov. Today 2006, 11, 580-594
4. Walters, W. P.; Stahl, M. T.; Murcko, M. A. Virtual screening-an overview Drug Discov. Today 1998, 3, 160-178
5. Hou, T. J.; Xu, X. J. Recent development and application of virtual screening in drug discovery: An overview Curr. Pharm. Des. 2004, 10, 1011-1033
6. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 1997, 23, 3-25
7. Meslamani, J.; Li, J.; Sutter, J.; Stevens, A.; Bertrand, H.-O.; Rognan, D. Protein-Ligand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling J. Chem. Inf. Model. 2012, 52, 943-955
8. Drwal, M. N.; Griffith, R. Combination of ligand-and structure-based methods in virtual screening Drug Discov. Today: Technol. 2013, 10, e395-e401
9. Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A critical assessment of docking programs and scoring functions J. Med. Chem. 2006, 49, 5912-5931
10. Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L. Assessing scoring functions for protein-ligand interactions J. Med. Chem. 2004, 47, 3032-3047
11. Wang, R. X.; Lu, Y. P.; Wang, S. M. Comparative evaluation of 11 scoring functions for molecular docking J. Med. Chem. 2003, 46, 2287-2303
12. Houston, D. R.; Walkinshaw, M. D. Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context J. Chem. Inf. Model. 2013, 53, 384-390
13. Teramoto, R.; Fukunishi, H. Supervised consensus scoring for docking and virtual screening J. Chem. Inf. Model. 2007, 47, 526-534
14. Sheridan, R. P.; Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discov. Today 2002, 7, 903-911
15. Tanrikulu, Y.; Kruger, B.; Proschak, E. The holistic integration of virtual screening in drug discovery Drug Discov. Today 2013, 18, 358-64
16. Sperandio, O.; Miteva, M. A.; Villoutreix, B. O. Combining ligand- and structure-based methods in drug design projects Curr. Comput.-Aided Drug Des. 2008, 4, 250-258
17. Wilson, G. L.; Lill, M. A. Integrating structure-based and ligand-based approaches for computational drug design Future Med. Chem. 2011, 3, 735-750
18. Holliday, J. D.; Kanoulas, E.; Malim, N.; Willett, P. Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision J. Cheminformatics 2011, 3 29
19. Krueger, D. M.; Evers, A. Comparison of Structure- and Ligand-Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors ChemMedChem 2010, 5, 148-158
20. Chen, Z.; Tian, G.; Wang, Z.; Jiang, H.; Shen, J.; Zhu, W. Multiple Pharmacophore Models Combined with Molecular Docking: A Reliable Way for Efficiently Identifying Novel PDE4 Inhibitors with High Structural Diversity J. Chem. Inf. Model. 2010, 50, 615-625
21. Feher, M. Consensus scoring for protein-ligand interactions Drug Discov. Today 2006, 11, 421-428
22. Cross, J. B.; Thompson, D. C.; Rai, B. K.; Baber, J. C.; Fan, K. Y.; Hu, Y.; Humblet, C. Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy J. Chem. Inf. Model. 2009, 49, 1455-1474
23. Bergner, A.; Parel, S. P. Hit Expansion Approaches Using Multiple Similarity Methods and Virtualized Query Structures J. Chem. Inf. Model. 2013, 53, 1057-1066
24. Willett, P. Combination of Similarity Rankings Using Data Fusion J. Chem. Inf. Model. 2013, 53, 1-10
25. Svensson, F.; Karlen, A.; Skold, C. Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods J. Chem. Inf. Model. 2012, 52, 225-232
26. Klon, A. E.; Glick, M.; Davies, J. W. Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results J. Med. Chem. 2004, 47, 4356-4359
27. Discovery Studio 3.1 Guide; Accelrys Inc.: San Diego, 2012; http://www.accelrys.com.
28. Carlson, H. A. Protein flexibility and drug design: how to hit a moving target Curr. Opin. Chem. Biol. 2002, 6, 447-452
29. Meagher, K. L.; Carlson, H. A. Incorporating protein flexibility in structure-based drug discovery: Using HIV-1 protease as a test case J. Am. Chem. Soc. 2004, 126, 13276-13281
30. Damm, K. L.; Carlson, H. A. Exploring experimental sources of multiple protein conformations in structure-based drug design J. Am. Chem. Soc. 2007, 129, 8225-8235
31. Cozzini, P.; Kellogg, G. E.; Spyrakis, F.; Abraham, D. J.; Costantino, G.; Emerson, A.; Fanelli, F.; Gohlke, H.; Kuhn, L. A.; Morris, G. M.; Orozco, M.; Pertinhez, T. A.; Rizzi, M.; Sotriffer, C. A. Target Flexibility: An Emerging Consideration in Drug Discovery and Design J. Med. Chem. 2008, 51, 6237-6255
32. B-Rao, C.; Subramanian, J.; Sharma, S. D. Managing protein flexibility in docking and its applications Drug Discov. Today 2009, 14, 394-400
33. Zhou, S.; Li, Y.; Hou, T. Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors J. Chem. Inf. Model. 2013, 53, 982-996
34. Ma, D.-L.; Chan, D. S.-H.; Leung, C.-H. Drug repositioning by structure-based virtual screening Chem. Soc. Rev. 2013, 42, 2130-2141
35. Totrov, M.; Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative Curr. Opin. Struct. Biol. 2008, 18, 178-184
36. Rueda, M.; Bottegoni, G.; Abagyan, R. Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes J. Chem. Inf. Model. 2009, 49, 716-725
37. Rueda, M.; Bottegoni, G.; Abagyan, R. Recipes for the Selection of Experimental Protein Conformations for Virtual Screening J. Chem. Inf. Model. 2010, 50, 186-193
38. Leung, T.; Manser, E.; Tan, L.; Lim, L. A novel serine/threonine kinase binding the Ras-related RhoA GTPase which translocates the kinase to peripheral membranes J. Biol. Chem. 1995, 270, 29051-29054
39. Dong, M.; Yan, B. P.; Liao, J. K.; Lam, Y.-Y.; Yip, G.; Yu, C.-M. Rho-kinase inhibition: a novel therapeutic target for the treatment of cardiovascular diseases Drug Discov. Today 2010, 15, 622
40. Liu, S.; Goldstein, R. H.; Scepansky, E. M.; Rosenblatt, M. Inhibition of rho-associated kinase signaling prevents breast cancer metastasis to human bone Cancer Res. 2009, 69, 8742-8751
41. Kubo, T.; Yamaguchi, A.; Iwata, N.; Yamashita, T. The therapeutic effects of Rho-ROCK inhibitors on CNS disorders Ther. Clin. Risk Manag. 2008, 4, 605
42. Mueller, B. K.; Mack, H.; Teusch, N. Rho kinase, a promising drug target for neurological disorders Nat Rev. Drug Discov. 2005, 4, 387-398
43. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank Nucleic Acids Res. 2000, 28, 235-242
44. Liu, T.; Lin, Y.; Wen, X.; Jorissen, R. N.; Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities Nucleic Acids Res. 2007, 35, D198-D201
45. Rogers, D.; Brown, R. D.; Hahn, M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up J. Biomol. Screen. 2005, 10, 682-686
46. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes J. Med. Chem. 2006, 49, 6177-6196
47. Kaminski, G. A.; Friesner, R. A.; Tirado-Rives, J.; Jorgensen, W. L. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides J. Phys. Chem. B 2001, 105, 6474-6487
48. Sutter, J.; Li, J.; Maynard, A. J.; Goupil, A.; Luu, T.; Nadassy, K. New Features that Improve the Pharmacophore Tools from Accelrys Curr. Comput.-Aided Drug Des. 2011, 7, 173-180
49. Rogers, D.; Hopfinger, A. J. Application of genetic function approximation to quantitative structure-activity-relationships and quantitative structure-property relationships J. Chem. Inf. Comput. Sci. 1994, 34, 854-866
50. Chen, L.; Li, Y.; Zhao, Q.; Peng, H.; Hou, T. ADME Evaluation in Drug Discovery. 10. Predictions of P-Glycoprotein Inhibitors Using Recursive Partitioning and Naive Bayesian Classification Techniques Mol. Pharmaceutics 2011, 8, 889-900
51. Tian, S.; Wang, J.; Li, Y.; Xu, X.; Hou, T. Drug-likeness Analysis of Traditional Chinese Medicines: Prediction of Drug-likeness Using Machine Learning Approaches Mol. Pharmaceutics 2012, 9, 2875-86
52. Wang, S.; Li, Y.; Wang, J.; Chen, L.; Zhang, L.; Yu, H.; Hou, T. ADMET Evaluation in Drug Discovery. 12. Development of Binary Classification Models for Prediction of hERG Potassium Channel Blockage Mol. Pharmaceutics 2012, 9, 996-1010
53. Hou, T.; Wang, J.; Zhang, W.; Xu, X. ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification J. Chem. Inf. Model. 2007, 47, 208-218
54. Shen, M.; Tian, S.; Li, Y.; Li, Q.; Xu, X.; Wang, J.; Hou, T. Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines J. Cheminformatics 2012, 4, 31
55. Qiao, X. B.; Hou, T. J.; Zhang, W.; Guo, S. L.; Xu, S. J. A 3D structure database of components from Chinese traditional medicinal herbs J. Chem. Inf. Comput. Sci. 2002, 42, 481-489
56. Bemis, G. W.; Murcko, M. A. The properties of known drugs 0.1. Molecular frameworks J. Med. Chem. 1996, 39, 2887-2893