CHAPTER 5. Conclusion
5.2 Future Works
Computer-aided methods for virtual screening and post screening analysis can be successfully used in identifying a wide range of protein-ligand complexes for a variety of biochemical applications. Thus, the following studies are of significance and particular interest in our future works:
1) We want to investigate a new clustering technique, NeatMap [53], a non-clustering approach using microarray datasets instead of traditional clustered heat map for the possibility of improving accuracy and efficiency of ranking compounds and select more suitable representatives.
2) We aim to investigate targets for nutrition and skin care in future studies. These two areas deserve attention because they are an important part of human health and well being since both nutrition and skin are important defenses against pathogens of all kinds.
As concerns of pollution increase around the world, safer and more natural food products, fertilizers, pesticides and detergents are highly sought after. People in general are becoming more concerned with eating proper diets and maintaining a strong and healthy body therefore, new findings in nutrition, supplements and skin care are of particular interest. Therefore, computer-aided methods for providing the necessary means in identifying compounds used these areas will continue to grow and expand in the near future.
57
Appendix A (Obtained from our published study [48])
1) 61 Active compounds used in TSCC obtained from ACD and CMC public databases:
O
58
59
60
61
Appendix B
List of Publications
Clinciu, D. L., Yang, J. M., Lo, C. C., The Relevance of Interaction Profiles in Various
Computer-Aided Novel Compound Design and Applications, Journal of Current Bioinformatics, 2011, vol 6, no 3, doi:1574-8936/11
Clinciu, D. L., Chen, Y.F., Ko, C.N., Lo, C. C., and Yang, J.M., TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features, BMC Genomics, 2010. doi:10.1186/1471-2164-11-S4-S26
Clinciu, D. L., Chen, Y. L., Yang, M.C., Wallace, S., Yang, J. M., Mao, S. J. T., Vitamin D;
Nutrition, Side Effects and Supplements, Nova Science Publishers, ISBN: 978-1-61728-601-8, 2010)
Wallace, S., Reed, A., Clinciu, D. L., and Yu, H. C., A Comparison of the Usability of Heuristic Evaluations for Online Help, Information Design Journal (accepted, December 2010)
Khudaverdyan S., Dokholyan, J, Arustamyan V., Khudaverdyan, A., Clinciu, D.L., Nuclear Instruments and Methods in Physics Research, Elsevier, 2009. A 610, 314–316
Conference Paper
Clinciu, D. L., Yang, J. M., Chen, Y.F, Ko, C. N, Lo, C. C., InCoB2010, The 9th International Conference on Bioinformatics, TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features (Presented in Tokyo, Sep 27, 2010)
62
REFERENCES
1. Frank, E., et al., Data mining in bioinformatics using Weka. Bioinformatics, 2004. 20(15):
p. 2479-2481.
2. Stahl, M. and T. Schulz-Gasch, Practical database screening with docking tools. Ernst Schering Res Found Workshop 2003. 42: p. 24.
3. Bissantz, C., G. Folkers, and D. Rognan, Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. Journal of Medicinal Chemistry, 2000. 43(25): p. 4759-4767.
4. Joachimiak, A., High-throughput crystallography for structural genomics. Current Opinion in Structural Biology, 2009. 19(5): p. 573-584.
5. Blundell, T.L., H. Jhoti, and C. Abell, High-throughput crystallography for lead discovery in drug design. Nature Reviews Drug Discovery, 2002. 1(1): p. 45-54.
6. Yang, J.M., et al., Combinatorial computational approaches to identify tetracycline derivatives as flavivirus inhibitors. PLoS ONE, 2007. 2(5): p. e428.
7. Chin, K.H., et al., The cAMP receptor-like protein CLP is a novel c-di-GMP receptor linking cell-cell signaling to virulence gene expression in Xanthomonas campestris. J Mol Biol, 2010. 396(3): p. 646-62.
8. Hung, H.C., et al., Aurintricarboxylic acid inhibits influenza virus neuraminidase.
Antiviral Res, 2009. 81(2): p. 123-31.
9. Yang, M.C., et al., Rational design for crystallization of beta-lactoglobulin and vitamin D-3 complex: revealing a secondary binding site Crystal Growth & Design, 2008. 8: p.
4268-4276.
10. Clinciu, D. L., et al. Vitamin D: Nutrition, Side Effects and Supplements, Nova Science Publishers, 2010. ISBN: 978-1-61728-601-8
11. Stahl, M. and M. Rarey, Detailed analysis of scoring functions for virtual screening.
Journal of Medicinal Chemistry, 2001. 44(7): p. 1035-1042.
12. Pfeffer, P. and H. Gohlke, DrugScore(RNA) - Knowledge-based scoring function to predict RNA-ligand interactions. Journal of Chemical Information and Modeling, 2007.
47(5): p. 1868-1876.
13. Weiner, S.J., et al., A New Force-Field for Molecular Mechanical Simulation of Nucleic-Acids and Proteins. Journal of the American Chemical Society, 1984. 106(3): p. 765-784.
14. Gehlhaar, D.K., et al., Moleduclar recognition of the inhibitor AG-1343 BY HIV-1 Protease - Conformationally Flexible Docking by Evolutionary Programming. Chemistry
& Biology, 1995. 2(5): p. 317-324.
15. Charifson, P.S., et al., Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of Medicinal Chemistry, 1999. 42(25): p. 5100-5109.
16. Verdonk, M.L., et al., Virtual screening using protein-ligand docking: Avoiding artificial enrichment. Journal of Chemical Information and Computer Sciences, 2004. 44(3): p.
793-806.
17. Fradera, X., R.M.A. Knegtel, and J. Mestres, Similarity-driven flexible ligand docking.
Proteins-Structure Function and Bioinformatics, 2000. 40(4): p. 623-636.
18. Ewing, T.J.A., et al., DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 2001. 15(5):
p. 411-428.
63
19. Ewing, T.J.A. and I.D. Kuntz, Critical evaluation of search algorithms for automated molecular docking and database screening. Journal of Computational Chemistry, 1997.
18(9): p. 1175-1189.
20. Jones G, Willett P, Glen RC, Leach AR, Taylor R: Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 1997; 267: 727-748.
21. Kroemer, R.T., et al., Assessment of docking poses: Interactions-based accuracy classification (IBAC) versus crystal structure deviations. Journal of Chemical Information and Computer Sciences, 2004. 44(3): p. 871-881.
22. Amari, S., et al., VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening.
Journal of Chemical Information and Modeling, 2006. 46(1): p. 221-30.
23. Deng, Z., C. Chuaqui, and J. Singh, Structural interaction fingerprint (SIFt): A novel method for analyzing three-dimensional protein-ligand binding interactions. Journal of Medicinal Chemistry, 2004. 47: p. 337-344.
24. Nakano, T., et al., Fragment molecular orbital method: use of approximate electrostatic potential. Chemical Physics Letters, 2002. 351(5-6): p. 475-480.
25. Bocker, A., G. Schneider, and A. Teckentrup, NIPALSTREE: A new hierarchical clustering approach for large compound libraries and its application to virtual screening.
Journal of Chemical Information and Modeling, 2006. 46(6): p. 2220-2229.
26. Yang, J.M. and C.C. Chen, GEMDOCK: A generic evolutionary method for molecular docking. Proteins-Structure Function and Bioinformatics, 2004. 55(2): p. 288-304.
27. Shin, J.M. and D.H. Cho, PDB-ligand: a ligand database based on PDB for the automated and customized classification of ligand-binding structures. Nucleic Acids Research, 2005.
33: p. D238-D241.
28. Nuzzo, A. and A. Riva, Genephony: a knowledge management tool for genome-wide research. Bmc Bioinformatics, 2009. 10.
29. Jerajani, H.R., Mizoguchi, H., Li J, The effects of a daily facial lotion containing
vitamins B3 and E and provitamin B5 on the facial skin of Indian women, Indian Journal of Dermatology, 2010. 76 (1): p. 20-26
30. Revollo, J.R., Grimm, A. A., Imai, S, The NAD Biosynthesis Pathway Mediated by Nicotinamide, Journal of Biological Chemistry, 2004. 279 (4): p. 50754-50763 31. Singh, A., Casey, K.D., King, W.D., Efficacy of urease inhibitor to reduce ammonia
emission from poultry house, Journal of Applied Poultry Research, 2009. 18 (1): 34-42 32. Rarey, M., et al., A fast flexible docking method using an incremental construction
algorithm. Journal of Molecular Biology, 1996. 261(3): p. 470-489.
33. Jones, G., et al., Development and validation of a genetic algorithm for flexible docking.
Journal of Molecular Biology, 1997. 267(3): p. 727-748.
34. Abagyan, R., M. Totrov, and D. Kuznetsov, Icm - a New Method for Protein Modeling and Design - Applications to Docking and Structure Prediction from the Distorted Native Conformation. Journal of Computational Chemistry, 1994. 15(5): p. 488-506.
35. Venkatachalam, C.M., et al., LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. Journal of Molecular Graphics & Modelling, 2003. 21(4): p. 289-307.
36. Kuntz, I.D., Structure-Based Strategies for Drug Design and Discovery. Science, 1992.
257(5073): p. 1078-1082.
64
37. Lorber, D.M. and B.K. Shoichet, Flexible ligand docking using conformational ensembles. Protein Science, 1998. 7(4): p. 938-950.
38. Willett, P., J.M. Barnard, and G.M. Downs, Chemical similarity searching. Journal of Chemical Information and Computer Sciences, 1998. 38(6): p. 983-996.
39. Leigh, D.A., Summing up ligand binding interactions. Chemistry & Biology, 2003.
10(12): p. 1143-1144.
40. Chen, K., Kurgan, L., Investigation of Atomic Level Patterns in Protein—Small Ligand Interactions, PLoS One, 2009. 4 (2): e4473
41. Wyss, P.C., et al., Novel dihydrofolate reductase inhibitors. Structure-based versus diversity-based library design and high-throughput synthesis and screening. J Med Chem, 2003. 46: p. 2304-2312.
42. deWolf, F. A., and Brett, G. M., Ligand-Binding Proteins: Their Potential for Application in Systems for Controlled Delivery and Uptake of Ligands, Pharmacological Reviews, 2000. 52 (2): 207-236
43. Hsieh, R.W. et al. Identification of Ligands with Bicyclic Scaffolds Provides Insights into Mechanisms of Estrogen Receptor Subtype Selectivity, Journal of Biological Chemistry, 2006. 281 (26): 17909-17919
44. Champness, J.N., et al., Exploring the active site of herpes simplex virus type-1
thymidine kinase by X-ray crystallography of complexes with aciclovir and other ligands.
Proteins-Structure Function and Genetics, 1998. 32(3): p. 350-361.
45. Yang, J.M., et al., Consensus scoring criteria for improving enrichment in virtual screening. Journal of Chemical Information and Modeling, 2005. 45(4): p. 1134-1146.
46. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 1998; 19: 1639-1662.
47. Mitchell, P., A perspective on protein microarrays. Nature Biotechnology, 2002. 20(3):
225-229.
48. Clinciu, D. L., et al. TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physico-chemical features, BMC Genomics, 2010.
doi:10.1186/1471-2164-11-S4-S26
49. Yang, J.M. and T.W. Shen, A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins, 2005. 59(2): p. 205-20.
50. Yang, J.M., et al., Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model, 2005. 45(4): p. 1134-46.
51. Pearlman DA, Charifson PS. Improved scoring of ligand-protein interactions using OWFEG free energy grids. J Med Che 2001; 44: 502-511.
52. Pan Y, Huang N, Cho S, MacKerell AD, Jr. Consideration of molecular weight during compound selection in virtual target-based database screening. Journal of Chemical Information and Computer Science 2003; 43: 267-272.
53. Rajaram, S. and Y. Oono, NeatMap - non-clustering heat map alternatives in R. Bmc Bioinformatics. 2010; 11.
54. Matter, H., Selecting optimally diverse compounds from structure databases: A validation study of two-dimensional and three-dimensional molecular descriptors. Journal of
Medicinal Chemistry, 1997. 40(8): p. 1219-1229.
65
55. Ruvinsky, A.M., Role of binding entropy in the refinement of protein-ligand docking predictions: Analysis based on the use of 11 scoring functions. Journal of Computational Chemistry, 2007. 28(8): p. 1364-1372.
56. Liu, Q., et al., RNACluster: An integrated tool for RNA secondary structure comparison and clustering. Journal of Computational Chemistry, 2008. 29(9): p. 1517-1526.
57. Zheng, W.F. and A. Tropsha, Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. Journal of Chemical Information and Computer Sciences, 2000. 40(1): p. 185-194.
58. Carhart, R.E., D.H. Smith, and R. Venkataraghavan, Atom Pairs as Molecular -Features in Structure Activity Studies – Definitions and Applications Journal of Chemical Information and Computer Sciences, 1985. 25(2): p. 64-73.
59. Dubes, R. and A.K. Jain, Clustering methodologies in exploratory data analysis. Adv Comput, 1980. 19: p. 113-228.
60. Gluck, O. and Maricic, M. Raloxifene: Recent information on skeletal and non-skeletal effects. Current Opinion in Rheumatology, 2002. 14(4): p. 429-432.
61. Cody, V. et al. Comparison of ternary crystal complexes of F31 variants of human dihydrofolate reductase with NADPH and a classical antitumor furopyrimidine. Anti-cancer Drug Design, 1998. 13(4): p. 8.
62. Verma, R.P. and C. Hansch, A QSAR study on influenza neuraminidase inhibitors.
Bioorganic & Medicinal Chemistry, 2006. 14(4): p. 982-996.
63. Yang, J.M. and T.W. Shen, A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins-Structure Function and Bioinformatics, 2005. 59(2): p. 205-220.
64. Birch, L., et al., Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase. Journal of Computer-Aided Molecular Design, 2002. 16(12): p. 855-869.
65. Clinciu, D. L., et al. The Relevance of Interaction Profiles in Various Computer-Aided Novel Compound Design and Applications, Journal of Current Bioinformatics, 2011, vol 6, no 3, doi:1574-8936/11
66. Yang, M. C. et al. Crystal structure of a secondary vitamin D3 binding site of milk b-lactoglobulin, Proteins, 2008; 71:1197-1210
67. Yang, M. C. et al. Evidence for b-lactoglobulin involvement in vitamin D transport in vivo – role of the c-turn (Leu-Pro-Met) of b-lactoglobulin in vitamin D binding. FEBS Journal, 2009; 276: 2251–2265
68. Chevalier, F. et al. Scavenging of free radicals, antimicrobial, and cytotoxic activities of the Maillard reaction products of beta-lactoglobulin glycated with several sugars. J Agric Food Chem 2001; 49:5031–5038.
69. Nagaoka, S. et al. Identification of novel hypocholesterolemic peptides derived from bovine milk beta-lactoglobulin. Biochem Biophys Res Commun 2001; 281:11–17.