Chemoinformatic Approaches for Inhibitors of DNA Methyltransferases: Comprehensive Characterization of Screening Libraries

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DOI: 10.4236/cmb.2011.11002    3,487 Downloads   10,491 Views   Citations


Virtual screening of compound databases is a promising approach to identify inhibitors of DNA methyltransferases and other epigenetic targets. An important first step before conducting virtual screening is to characterize the structural diversity and chemical space coverage of the screening collections. Herein, we report a comprehensive chemoinformatic characterization of novel screening libraries, including a focused collection directed to inhibitors of DNA methyltransferases (DNMTs), and two natural product databases. The compound databases were assessed in terms of physicochemical properties, molecular scaffolds, and fingerprints. As part of the scaffold diversity analysis, a recently developed method, based on Shannon Entropy, was used. The overall approach enabled the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity. Overall, the analysis of the distribution of physicochemical properties indicates that the DNMT focused library and the two natural products collections have molecules with properties similar to approved drugs. Moreover, the natural products databases analyzed in this work have different chemical structures from approved drugs and synthetic databases and therefore are attractive for virtual screening for DNMT inhibitors. The scaffold analysis revealed that the focused library has, overall, the largest scaffold diversity and that the most frequent scaffolds are not identified in the other analyzed collections. Therefore, the focused library is also attractive to perform virtual and experimental screening for novel inhibitors. This study represents a first step towards the virtual screening of novel compound databases to identify inhibitors of DNMTs. Results of this study are general and can be used for the virtual screening of the compound databases against targets directed to other therapeutic applications.

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J. Yoo and J. Medina-Franco, "Chemoinformatic Approaches for Inhibitors of DNA Methyltransferases: Comprehensive Characterization of Screening Libraries," Computational Molecular Bioscience, Vol. 1 No. 1, 2011, pp. 7-16. doi: 10.4236/cmb.2011.11002.


[1] K. D. Robertson, “DNA Methylation and Human Disease,” Nature Reviews Genetics, Vol. 6, No. 8, 2005, pp. 597- 610. doi:10.1038/nrg1655
[2] C. A. Miller, C. F. Gavin, J. A. White, R. R. Parrish, A. Honasoge, C. R. Yancey, I. M. Rivera, M. D. Rubio, G. Rumbaugh and J. D. Sweatt, “Cortical DNA Methylation Maintains Remote Memory,” Nature Neuroscience, Vol. 13, No. 6, 2010, pp. 664-666. doi:10.1038/nn.2560
[3] N. H. Zawia, D. K. Lahiri and F. Cardozo-Pelaez, “Epigenetics, Oxidative Stress, and Alzheimer Disease,” Free Radical Biology & Medicine, Vol. 46, No. 9, 2009, pp. 1241-1249. doi:10.1016/j.freeradbiomed.2009.02.006
[4] C. Stresemann and F. Lyko, “Modes of Action of the DNA Methyltransferase Inhibitors Azacytidine and Decitabine,” International Journal of Cancer, Vol. 123, No. 1, 2008, pp. 8-13. doi:10.1002/ijc.23607
[5] J. L. Medina-Franco and T. Caulfield, “Advances in the Computational Development of DNA Methyltransferase Inhibitors,” Drug Discovery Today, Vol. 16, No. 9-10, 2011, pp. 418-425. doi:10.1016/j.drudis.2011.02.003
[6] D. Kuck, N. Singh, F. Lyko and J. L. Medina-Franco, “Novel and Selective DNA Methyltransferase Inhibitors: Docking-Based Virtual Screening and Experimental Evaluation,” Bioorganic & Medicinal Chemistry, Vol. 18, No. 22010, pp. 822-829.
[7] P. Siedlecki, R. G. Boy, T. Musch, B. Brueckner, S. Suhai, F. Lyko and P. Zielenkiewicz, “Discovery of Two Novel, Small-Molecule Inhibitors of DNA Methylation,” Journal of Medicinal Chemistry, Vol. 49, No. 2, 2006, pp. 678-683. doi:10.1021/jm050844z
[8] J. Medina-Franco, F. López-Vallejo, D. Kuck and F. Lyko, “Natural Products as DNA Methyltransferase Inhibitors: A Computer-Aided Discovery Approach,” Molecular Diversity, Vol. 15, No. 2, 2011, pp. 293-304. doi:10.1007/s11030-010-9262-5
[9] T. Scior, P. Bernard, J. L. Medina-Franco and G. M. Maggiora, “Large Compound Databases for Structure-Activity Relationships Studies in Drug Discovery,” Mini-Reviews in Medicinal Chemistry, Vol. 7, No. 8, 2007, pp. 851-860. doi:10.2174/138955707781387858
[10] F. López-Vallejo, T. Caulfield, K. Martínez-Mayorga, M. A. Giulianotti, A. Nefzi, R. A. Houghten and J. L. Medina-Franco, “Integrating Virtual Screening and Combinatorial Chemistry for Accelerated Drug Discovery,” Combinatorial Chemistry & High Throughput Screening, Vol. 14, No. 6, 2011, pp. 475-487. doi:10.2174/138620711795767866
[11] D. Fourches, E. Muratov and A. Tropsha, “Trust, but Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research,” Journal of Chemical Information and Modeling, Vol. 50, No. 7, 2010, pp. 1189-1204. doi:10.1021/ci100176x
[12] F. López-Vallejo, A. Nefzi, A. Bender, J. R. Owen, I. T. Nabney, R. A. Houghten and J. L. Medina-Franco, “Increased Diversity of Libraries from Libraries: Chemoinformatic Analysis of Bis-Diazacyclic Libraries,” Chemical Biology & Drug Design, Vol. 77, No. 5, 2011, pp. 328-342. doi:10.1111/j.1747-0285.2011.01100.x
[13] N. Singh, R. Guha, M. A. Giulianotti, C. Pinilla, R. A. Houghten and J. L. Medina-Franco, “Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Re- pository,” Journal of Chemical Information and Model- ing, Vol. 49, No. 4, 2009, pp. 1010-1024. doi:10.1021/ci800426u
[14] M. Feher and J. M. Schmidt, “Property Distributions: Differences between Drugs, Natural Products, and Molecules from Combinatorial Chemistry,” Journal of Chemical Information and Modeling, Vol. 43, No. 1, 2003, pp. 218-227. doi:10.1021/ci0200467
[15] J. Rosén, J. Gottfries, S. Muresan, A. Backlund and T. I. Oprea, “Novel Chemical Space Exploration via Natural Products,” Journal of Medicinal Chemistry, Vol. 52, No. 7, 2009, pp. 1953-1962. doi:10.1021/jm801514w
[16] S. R. Langdon, N. Brown and J. Blagg, “Scaffold Diversity of Exemplified Medicinal Chemistry Space,” Journal of Chemical Information and Modeling, Vol. 51, No. 9, 2011, pp. 2174-2185. doi:10.1021/ci2001428
[17] V. Le Guilloux, L. Colliandre, S. Bourg, G. Guenegou, J. Dubois-Chevalier and L. Morin-Allory, “Visual Characterization and Diversity Quantification of Chemical Libraries: 1. Creation of Delimited Reference Chemical Subspaces,” Journal of Chemical Information and Modeling, Vol. 51, No. 8, 2011, pp. 1762-1774. doi:10.1021/ci200051r
[18] L. F. Pekka Tiikkainen, “Analysis of Commercial and Public Bioactivity Databases,” Journal of Chemical Information and Modeling, 2011, in press. doi:10.1021/ci2003126
[19] J. Yoo and J. L. Medina-Franco, “Homology Modeling, Docking, and Structure-Based Pharmacophore of Inhibitors of DNA Methyltransferase,” Journal of Computer- Aided Molecular Design, Vol. 25, No. 6, 2011, pp. 555- 567. doi:10.1007/s10822-011-9441-1
[20] J. Yoo and J. L. Medina-Franco, “Discovery and Optimization of Inhibitors of DNA Methyltransferase as Novel Drugs for Cancer Therapy,” in: C. Rundfeldt, Ed., Drug Development—A Case Study Based Insight into Modern Strategies, pp. 3-22.
[21] J. Yoo, J. Kim, K. D. Robertson and J. L. Medina-Franco, “Molecular Modeling of Inhibitors of Human DNA Methyltransferase with a Crystal Structure: Discovery of a Novel DNMT1 Inhibitor,” in; C. Christov and T. Karabencheva-Christova, Eds., Advances in Protein Chemistry and Structural Biology, Elsevier, Berlin, 2011, In press.
[22] J. L. Medina-Franco, M. A. Giulianotti, Y. Yu, L. Shen, L. Yao and N. Singh, “Discovery of a Novel Protein Kinase B Inhibitor by Structure-Based Virtual Screening,” Bioorganic & Medicinal Chemistry Letters, Vol. 19, 2009, pp. 4634- 4638. doi:10.1016/j.bmcl.2009.06.078
[23] A. Hernández-Campos, I. Velázquez-Martínez, R. Castillo, F. López-Vallejo, P. Jia, Y. Yu, M. A. Giulianotti and J. L. Medina-Franco, “Docking of Protein Kinase B Inhibitors: Implications in the Structure-Based Optimization of a Novel Scaffold,” Chemical Biology & Drug Design, Vol. 76, No. 3, 2010, pp. 269-276.
[24] J. J. Irwin and B. K. Shoichet, “ZINC—A Free Database of Commercially Available Compounds for Virtual Screening,” Journal of Chemical Information and Modeling, Vol. 45, No. 1, 2005, pp. 177-182. doi:10.1021/ci049714+
[25] Drug Bank, 2011.
[26] T. Fink and J.-L. Reymond, “Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery,” Journal of Chemical Information and Modeling, Vol. 47, No. 2, 2007, pp. 342-353. doi:10.1021/ci600423u
[27] MACCS Structural Keys, CA (USA): Symyx Software San Ramon.
[28] G. M. Maggiora and V. Shanmugasundaram, “Molecular Similarity Measures,” in: J. Bajorath, Ed., Chemoinformatics and Computational Chemical Biology, Methods in Molecular Biology, Springer, New York, 2011, pp. 39-100. doi:10.1007/978-1-60761-839-3_2
[29] A. Bender and R. C. Glen, “Molecular Similarity: A Key Technique in Molecular Informatics,” Organic & Biomolecular Chemistry, Vol. 2, No. 22, 2004, pp. 3204- 3218. doi:10.1039/b409813g
[30] Y. Wang and J. Bajorath, “Development of a Compound Class-Directed Similarity Coefficient That Accounts for Molecular Complexity Effects in Fingerprint Searching,” Journal of Chemical Information and Modeling, Vol. 49, No. 6, 2009, pp. 1369-1376. doi:10.1021/ci900108d
[31] S. Senger, “Using Tversky Similarity Searches for Core Hopping: Finding the Needles in the Haystack,” Journal of Chemical Information and Modeling, Vol. 49, No. 6, 2009, pp. 1514-1524. doi:10.1021/ci900092y
[32] L. Tan, H. Geppert, M. T. Sisay, M. Gutschow and J. Bajorath, “Integrating Structure- and Ligand-Based Virtual Screening: Comparison of Individual, Parallel, and Fused Molecular Docking and Similarity Search Calculations on Multiple Targets,” ChemMedChem, Vol. 3, No. 10, 2008, pp. 1566-1571. doi:10.1002/cmdc.200800129
[33] J. L. Medina-Franco, K. Martínez-Mayorga, M. A. Giu- lianotti, R. A. Houghten and C. Pinilla, “Visualization of the Chemical Space in Drug Discovery,” Current Computer—Aided Drug Design, Vol. 4, No. 4, 2008, pp. 322- 333. doi:10.2174/157340908786786010
[34] J. R. Owen, I. T. Nabney, J. L. Medina-Franco and F. López-Vallejo, “Visualization of Molecular Fingerprints,” Journal of Chemical Information and Modeling, Vol. 51, No. 7, 2011, pp. 1552-1563. doi:10.1021/ci1004042
[35] J. L. Medina-Franco, G. M. Maggiora, M. A. Giulianotti, C. Pinilla and R. A. Houghten, “A Similarity-Based Data-fusion Approach to the Visual Characterization and Comparison of Compound Databases,” Chemical Biology & Drug Design, Vol. 70, No. 5, 2007, pp. 393-412. doi:10.1111/j.1747-0285.2007.00579.x
[36] Spotfire, “Spotfire,” Spotfire, version 9.1.1, TIBCO Software, Inc., Somerville.
[37] Y. J. Xu and M. Johnson, “Using Molecular Equivalence Numbers To Visually Explore Structural Features that Distinguish Chemical Libraries,” Journal of Chemical Information and Modeling, Vol. 42, No. 4, 2002, pp. 912- 926. doi:10.1021/ci025535l
[38] J. L. Medina-Franco, K. Martínez-Mayorga, A. Bender and T. Scior, “Scaffold Diversity Analysis of Compound Data Sets Using an Entropy-Based Measure,” QSAR & Combinatorial Science, Vol. 28, No. 11-12, 2009, pp. 1551-1560. doi:10.1002/qsar.200960069
[39] F. López-Vallejo, R. Castillo, L. Yépez-Mulia and J. L. Medina-Franco, “Benzotriazoles and Indazoles Are Scaffolds with Biological Activity against Entamoeba histolytica,” Journal of Biomolecular Screening, Vol. 16, No. 8, 2011, pp. 862-868. doi:10.1177/1087057111414902
[40] J. L. Medina-Franco, J. Petit and G. M. Maggiora, “Hierarchical Strategy for Identifying Active Chemotype Classes in Compound Databases,” Chemical Biology & Drug Design, Vol. 67, No. 6, 2006, pp. 395-408. doi:10.1111/j.1747-0285.2006.00397.x
[41] A. H. Lipkus, Q. Yuan, K. A. Lucas, S. A. Funk, W. F. Bartelt, R. J. Schenck and A. J. Trippe, “Structural Diversity of Organic Chemistry. A Scaffold Analysis of the CAS Registry,” Journal of Organic Chemistry, Vol. 73, No. 12, 2008, pp. 4443-4451. doi:10.1021/jo8001276
[42] C. E. Shannon and W. Weaver, “The Mathematical Theory of Communication,” University of Illinois Press, Urbana, 1963.
[43] J. W. Godden and J. Bajorath, “Analysis of Chemical Information Content Using Shannon Entropy,” in: K. B. Lipkowitz and T. R. Cundari, eds., Reviews in Computational Chemistry, John Wiley & Sons, Inc., Hoboken, 2007, pp. 263-289. doi:10.1002/9780470116449.ch5
[44] J. Datta, K. Ghoshal, W. A. Denny, S. A. Gamage, D. G. Brooke, P. Phiasivongsa, S. Redkar and S. T. Jacob, “A New Class of Quinoline-Based DNA Hypomethylating Agents Reactivates Tumor Suppressor Genes by Blocking DNA Methyltransferase 1 Activity and Inducing Its Degradation,” Cancer Research, Vol. 69, No. 10, 2009, pp. 4277-4285. doi:10.1158/0008-5472.CAN-08-3669
[45] M. A. Johnson and G. M. Maggiora, “Concepts and Applications of Molecular Similarity,” Wiley, New York, 1990.
[46] G. W. Bemis and M. A. Murcko, “The Properties of Known Drugs. 1. Molecular Frameworks,” Journal of Medicinal Chemistry, Vol. 39, No. 15, 1996, pp. 2887-2893. doi:10.1021/jm9602928
[47] Traditional Chinese Medicine (TCM), 2011.
[48] C. Y.-C. Chen, “TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico,” Plos One, Vol. 6, No. 1, 2011, pp. 862-868. doi:10.1371/journal.pone.0015939
[49] Specs, 2010.
[50] National Cancer Institute (NCI) Diversity Set, 2010.
[51] DNMT-Focused Library, 2011.

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