FINDSITEcomb: A threading/structure-based, proteomic-scale virtual ligand screening approach

Journal of Chemical Information and Modeling

Volumn 53, Issue 1, 2013, Pages 230-240

  Zhou, Hongyi ^a   Skolnick, Jeffrey ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; PROTEINS; WEB SERVICES;

COMPOUND LIBRARIES; DRUG DISCOVERY PROCESS; ENRICHMENT FACTORS; HOMOLOGOUS PROTEINS; TARGET STRUCTURE; TRADITIONAL STRUCTURES; VIRTUAL LIGAND SCREENING; VIRTUAL SCREENING;

LIGANDS;

G PROTEIN COUPLED RECEPTOR; LIGAND;

ALGORITHM; ARTICLE; BINDING SITE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER INTERFACE; DRUG SCREENING; HUMAN; METABOLISM; METHODOLOGY; MOLECULAR DOCKING; PROTEIN CONFORMATION; PROTEIN DATABASE; PROTEOMICS;

ALGORITHMS; BINDING SITES; DATABASES, PROTEIN; DRUG EVALUATION, PRECLINICAL; HUMANS; LIGANDS; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; PROTEIN CONFORMATION; PROTEOMICS; RECEPTORS, G-PROTEIN-COUPLED; USER-COMPUTER INTERFACE;

MLCS; MLOWN;

EID: 84873052265     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci300510n     Document Type: Article

References (53)

1. Reddy., A. S.; Pati, S. P.; Kumar, P. P.; Pradeep, H. N.; Sastry, G. N. Virtual Screening in Drug Discovery-A Computational Perspective Curr. Protein Pept. Sci. 2007, 8 (3) 331-353
2. Macarron, R.; Banks, M. N.; Bojanic, D.; Burns, D. J.; Cirovic, D. A.; Garyantes, T.; Green, D. V. S.; Hertzberg, R. P.; Janzen, W. P.; Paslay, J. W.; Schopfer, U.; Sittampalam, G. S. Impact of high-throughput screening in biomedical research Nat. Rev. Drug Discovery 2011, 10, 188-195
3. Glen, R. C.; Adams, S. E. Similarity Metrics and Descriptor Spaces-Which Combinations to Choose? QSAR Comb. Sci. 2006, 25 (12) 1133-1142
4. Flower, D. R. On the Properties of Bit String-Based Measures of Chemical Similarity J. Chem. Inf. Comput. Sci. 1998, 38 (3) 379-386
5. Nikolova, N.; Jaworska, J. Approaches to Measure Chemical Similarity-a Review QSAR Combin. Sci. 2003, 22 (9) 1006-1026
6. Tanimoto, T. T. An elementary mathematical theory of classification and prediction. IBM International Report; 1958, (Nov).
7. Kroemer, R. Structure-based drug design: docking and scoring Curr. Protein Pept. Sci. 2007, 8 (4) 312-328
8. Brylinski, M.; Skolnick, J. FINDSITE: A threading-based method for ligand-binding site prediction and functional annotation Proc. Natl. Acad. Sci. 2008, 105, 129-134
9. Brylinski, M.; Skolnick, J. Comprehensive Structural and Functional Characterization of the Human Kinome by Protein Structure Modeling and Ligand Virtual Screening J. Chem. Inf. Model. 2010, 50 (10) 1839-1854
10. KIN: A chemical Systems Biology Approach Mol. Pharmaceutics 2010, 7 (6) 2324-33
11. Brylinski, M.; Skolnick, J. Comparison of structure-based and threading-based approaches to protein functional annotation Proteins 2010, 78 (1) 118-34
12. Roy, A.; Xu, D.; Poisson, J.; Zhang, Y. A Protocol for Computer-Based Protein Structure and Function Prediction J. Visualized Exp. 2011, 57
13. Wass, M. N.; Kelly, L. A.; Sternberg, M. J. 3DLigandSite: predicting ligand-binding sites using similar structures Nucl. Acid. Res. 2010, 38 (suppl 2) W469-W473
14. LHM: a threading-based approach to ligand homology modeling PLoS Comput. Biol. 2009, 5 (6) e1000405
15. Skolnick, J.; Brylinski, M. Proceedings of the International Conference of the Quantum Bio-Informatics III Novel Comput. Approaches Drug Discovery 2009, 2009
16. Roy, A.; Zhang, Y. Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement Structure 2012, 20, 987-997
17. Roy, A.; Yang, J.; Zhang, Y. An accurate comparative algorithm for structure-based protein function annotation Nucleic Acids Res. 2012, 20, W471-W477
18. Bernstein, F. C.; Koetzle, T. F.; Williams, G. J. B.; Meyer, E. F.; M. D. B., Jr.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. The Protein Data Bank: A Computer-based Archival File for Macromolecular Structures J. Mol. Biol. 1977, 112, 535-542
19. Zhang, Y.; Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score Nucleic Acids Res. 2005, 33, 2302-2309
20. Pandit, S.; Skolnick, J. Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score BMC Bioinf. 2008, 9) 531
21. Brylinski, M.; Skolnick, J. Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints J. Comput. Chem. 2008, 29, 1574-88
22. LHM: Low-resolution refinement for ligand comparative modeling J. Comput. Chem. 2010, 31, 1093-105
23. Lee, H. S.; Zhang, Y. BSP-SLIM: A blind low-resolution ligand-protein docking approach using predicted protein structures Proteins 2011, 80, 93-110
24. Zhou, H.;; Gao, M.; Kumar, N.; Skolnick, J. SUNPRO: Structure and function predictions of proteins from representative organisms. http://cssb.biology.gatech.edu/sunpro/index.html.
25. Dean, P.; Zanders, E.; Bailey, D. Industrial-scale,genomics-based drug design and discovery TRENDS Biotechnol. 2001, 19 (8) 288-292
26. X: A Structure-Based, Small Molecule Virtual Screening Approach with Application to All Identified Human GPCRs Mol. Pharmaceutics 2012, 9 (6) 1775-1784
27. Gaulton, A.; Bellis, L.; Bento, A.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. ChEMBL: a large-scale bioactivity database for drug discovery Nucl. Acid. Res. 2012, 40 (D1) D1100-1107
28. Wishart, D.; Knox, C.; Guo, A.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration Nucl. Acid. Res. 2006, 34 (Database) D668-672
29. Trott, O.; Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading J. Comput. Chem. 2010, 31, 455-461
30. Ewing, T. J. A.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases J. Comput.-Aided Molec. Design 2001, 15, 411-428
31. Huang, N.; Shoichet, B.; Irwin, J. Benchmarking Sets for Molecular Docking J. Med. Chem. 2006, 49 (23) 6789-6801
32. Okuno, Y.; Tamon, A.; Yabuuchi, H.; Niijima, S.; Minowa, Y.; Tonomura, K.; Kunimoto, R.; Feng, C. GLIDA: GPCR-ligand database for chemical genomics drug discovery-database and tools update Nucl. Acid. Res. 2007, 36, D907-D912
33. Skolnick, J.; Kihara, D.; Zhang, Y. Development and large scale benchmark testing of the PROSPECTOR 3.0 threading algorithm Proteins 2004, 56, 502-518
34. Zhang, Y.; Skolnick, J. A scoring function for the automated assessment of protein structure template quality Proteins 2004, 57, 702-710
35. Anonymous. Daylight Theory Manual; Daylight Chemical Information Systems, Inc: Aliso Viejo, CA: 2007.
36. Skolnick, J.; Zhou, H.; Mu, B. Further Evidence for the Likely Completeness of the Library of Solved Single Domain Protein Structures J. Phys. Chem. B 2012, 116 (23) 6654-6664
37. Zhou, H.; Zhou, Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments Proteins 2005, 58, 321-328
38. Henikoff, S.; Henikoff, J. G. Amino Acid Substitution Matrices from Protein Blocks Proc. Natl. Acad. Sci. U.S.A. 1992, 89, 10915-10919
39. Zhang, Y.; Skolnick, J. Automated structure prediction of weakly homologous proteins on genomic scale Proc. Natl. Acad. Sci. (USA) 2004, 101, 7594-7599
40. VMT Proteins 2011, 80 (2) 352-361
41. Brozell, S.; Mukherjee, S.; Balius, T.; Roe, D.; Case, D.; Rizzo, R. Evaluation of DOCK 6 as a pose generation and database enrichment tool J. Comput.-Aided Mol. Des. 2012, 26 (6) 749-73
42. 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
43. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 2004, 47, 1739-1749
44. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 2004, 47, 1750-1759
45. Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking Proteins 1999, 37, 228-241
46. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM-a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation J. Comput. Chem. 1994, 15, 488-506
47. Totrov, M.; Abagyan, R. Flexible protein-ligand docking by global energy optimization in internal coordinates Proteins 1998, Suppl., 215-220
48. Joseph-McCarthy, D.; Thomas, B. E. I. V.; Belmarsh, M.; Moustakas, D.; Alvarez, J. C. Pharmacophore-based molecular docking to account for ligand flexibility Proteins 2003, 51, 172-188
49. Joseph-McCarthy, D.; McFadyen, I. J.; Zou, J.; Walker, G.; Alvarez, J. C. Pharmacophore-based molecular docking: A practical guide Drug Discovery Ser. 2005, 1, 327-347
50. Jain, A. N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine J. Med. Chem. 2003, 46, 499-511
51. Pham, T. A.; Jain, A. N. Parameter Estimation for Scoring Protein-Ligand Interactions Using Negative Training Data J. Med. Chem. 2006, 49, 5856-5868
52. Jain, A. N. Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search J. Comput.-Aided Mol. Des. 2007, 21, 281-306
53. Irwin, J. J.; Shoichet, B. K. ZINC-A Free Database of Commercially Available Compounds for Virtual Screening J. Chem. Inf. Model. 2005, 45, 177-182