Communications of the Korean Institute of Information Scientists and Engineers (정보과학회지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

구조생물정보학의 신약개발 적용

  • Published : 2011.04.29
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. Altman, R.B. & Dugan, J.M., "Defining bioinformatics and structural bioinformatics," Structural Bioinformatics pp. 3-14, 2005.
  2. Weigelt, J., "Structural genomics-impact on biomedicine and drug discovery," Experimental Cell Research 316, pp.1332-8, 2010. https://doi.org/10.1016/j.yexcr.2010.02.041
  3. Mandell, D.J. & Kortemme, T., "Backbone flexibility in computational protein design," Currrent Opinion in Biotechnology 20, pp.420-8, 2009. https://doi.org/10.1016/j.copbio.2009.07.006
  4. Ratti, E. & Trist, D., "The continuing evolution of the drug discovery process in the pharmaceutical industry," Farmaco 56, pp.13-9, 2001. https://doi.org/10.1016/S0014-827X(01)01019-9
  5. Chen, Y., Li, Z. & Ung, C., "Computational method for drug target search and application in drug discovery," Molecular Engineering of Biological and Chemical Systems (MEBCS) pp.1-8, 2003.
  6. Blundell, T.L. et al., "Structural biology and bioinformatics in drug design: Opportunities and challenges for target identification and lead discovery," Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 361, pp.413-23, 2006. https://doi.org/10.1098/rstb.2005.1800
  7. Rohl, C. et al., "Modeling structurally variable regions in homologous proteins with Rosetta," Proteins 55, pp.656-77, 2004. https://doi.org/10.1002/prot.10629
  8. Chen, H. & Kihara, D., "Effect of using suboptimal alignments in template-based protein structure prediction," Proteins 79, pp. 315-34, 2011. https://doi.org/10.1002/prot.22885
  9. Zhou, H. & Skolnick, J., "Improving threading algorithms for remote homology modeling by combining fragment and template comparisons," Proteins 78, pp. 2041-8, 2010.
  10. Peng, J. & Xu, J., "Low-homology protein threading," Bioinformatics 26, pp.i294-300, 2010. https://doi.org/10.1093/bioinformatics/btq192
  11. Kolinski, A., "Protein modeling and structure prediction with a reduced representation," Acta Biochimica Polonica 51, pp. 349-71, 2004.
  12. Li, B. et al., "Characterization of local geometry of protein surfaces with the visibility criterion," Proteins 71, pp. 670-683, 2008. https://doi.org/10.1002/prot.21732
  13. Kalidas, Y. & Chandra, N., "PocketDepth: A new depth based algorithm for identification of ligand binding sites in proteins," Journal of Structural Biology 161, pp. 31-42, 2008. https://doi.org/10.1016/j.jsb.2007.09.005
  14. Huang, B. & Schroeder, M., "LIGSITEcsc: Predicting ligand binding sites using the Connolly surface and degree of conservation," BMC Structural Biology 6, p.19, 2006. https://doi.org/10.1186/1472-6807-6-19
  15. Xie, L., Xie, L. & Bourne, P.E., "A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery," Bioinformatics 25, pp.i305-312, 2009. https://doi.org/10.1093/bioinformatics/btp220
  16. Alonso, H., Bliznyuk, A. a & Gready, J.E., "Combining docking and molecular dynamic simulations in drug design," Medicinal Research Reviews 26, pp.531-68, 2006. https://doi.org/10.1002/med.20067
  17. Chen, Y. & Shoichet, B.K., "Molecular docking and ligand specificity in fragment-based inhibitor discovery," Nature Chemical Biology 5, pp. 358-64, 2009. https://doi.org/10.1038/nchembio.155
  18. Sael, L. & Kihara, D., "Binding ligand prediction for proteins using partial matching of local surface patches," International Journal of Molecular Sciences 11, pp. 5009-5026, 2010. https://doi.org/10.3390/ijms11125009
  19. Chikhi, R., Sael, L. & Kihara, D., "Real-time ligand binding pocket database search using local surface descriptors," Proteins 78, pp. 2007-2028, 2010. https://doi.org/10.1002/prot.22715
  20. Desjarlais, R.L., "Using computational techniques in fragment-based drug discovery," Methods in Enzymology 493, pp. 137-55, 2011.
  21. Villar, H.O. & Hansen, M.R., "Computational techniques in fragment based drug discovery," Current Topics in Medicinal Chemistry 7, pp. 1509-1513, 2007. https://doi.org/10.2174/156802607782194725
  22. Highsmith, J., "Biologic Therapeutic Drugs: Technologies and Global Markets (BIO079A)," 2011 at
  23. Lippow, S.M. & Tidor, B., "Progress in computational protein design," Current Opinion in Biotechnology 18, pp. 305-11, 2007. https://doi.org/10.1016/j.copbio.2007.04.009
  24. Bellows, M.L. & Floundas, C.A., "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," Current Drug Target 11, pp. 264-278, 2010. https://doi.org/10.2174/138945010790711914
  25. Rosenberg, M. & Goldblum, A., "Computational protein design: A novel path to future protein drugs," Current Pharmaceutical Design 12, pp. 3973-97, 2006. https://doi.org/10.2174/138161206778743655
  26. Ventura, S. et al., "Conformational strain in the hydrophobic core and its implications for protein folding and design," Nature Structural Biology 9, pp. 485-93, 2002. https://doi.org/10.1038/nsb799
  27. Filikov, A.V. et al., "Computational stabilization of human growth hormone," Protein Science 11, pp. 1452-1461, 2002. https://doi.org/10.1110/ps.3500102
  28. Malakauskas, S.M. & Mayo, S.L., "Design, structure and stability of a hyperthermophilic protein variant," Nature Structural Biology 5, pp. 470-475, 1998. https://doi.org/10.1038/nsb0698-470
  29. Slovic, A.M. et al., "Computational design of watersoluble analogues of the potassium channel KcsA," Proceedings of the National Academy of Sciences 101, pp. 1828-33, 2004. https://doi.org/10.1073/pnas.0306417101
  30. Slovic, A.M. et al., "Computational design of a watersoluble analog of phospholamban," Protein Science 12, pp. 337-48, 2003. https://doi.org/10.1110/ps.0226603
  31. Strop, P. & Mayo, S.L., "Contribution of surface salt bridges to protein stability," Biochemistry 39, pp. 1251-1255, 2000. https://doi.org/10.1021/bi992257j
  32. Berezovsky, I.N., Zeldovich, K.B. & Shakhnovich, E.I., "Positive and negative design in stability and thermal adaptation of natural proteins," PLoS Computational Biology 3, e52, 2007. https://doi.org/10.1371/journal.pcbi.0030052
  33. Samish, I. et al., "Theoretical and Computational Protein Design," Annual Review of Physical Chemistry pp. 129-149, 2010.
  34. Bolon, D.N. & Mayo, S.L., "Enzyme-like proteins by computational design," Proceedings of the National Academy of Sciences 98, 14274, 2001. https://doi.org/10.1073/pnas.251555398
  35. Chevalier, B.S. et al., "Design, activity, and structure of a highly specific artificial endonuclease," Molecular Cell 10, pp. 895-905, 2002. https://doi.org/10.1016/S1097-2765(02)00690-1
  36. Bolon, D.N. et al., "Specificity versus stability in computational protein design," Proceedings of the National Academy of Sciences 102, pp. 12724-9, 2005. https://doi.org/10.1073/pnas.0506124102
  37. Rothlisberger, D. et al., "Kemp elimination catalysts by computational enzyme design," Nature 453, pp. 190-5, 2008. https://doi.org/10.1038/nature06879
  38. Khersonsky, O. et al., "Evolutionary optimization of computationally designed enzymes: Kemp eliminases of the KE07 series," Journal of Molecular Biology 396, 1025-42, 2010. https://doi.org/10.1016/j.jmb.2009.12.031
  39. Sircar, A. & Gray, J.J., "SnugDock: Paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models," PLoS Computational Biology 6, e1000644, 2010. https://doi.org/10.1371/journal.pcbi.1000644
  40. Reichert, J. & Pavlou, A., "From the analystʼs couch: Monoclonal antibodies market," Nature Reviews Drug Discovery 3, pp. 383-384, 2004. https://doi.org/10.1038/nrd1386
  41. Schwede, T. et al., "Outcome of a workshop on applications of protein models in biomedical research," Structure 17, pp. 151-9, 2009. https://doi.org/10.1016/j.str.2008.12.014
  42. Kuhlman, B. et al., "Design of a novel globular protein fold with atomic-level accuracy," Science 302, pp. 1364-8, 2003. https://doi.org/10.1126/science.1089427
  43. Bender, G.M. et al., "De novo design of a single-chain diphenylporphyrin metalloprotein," Journal of the American Chemical Society 129, pp. 10732-40, 2007. https://doi.org/10.1021/ja071199j
  44. Jiang, L. et al., "De novo computational design of retro-aldol enzymes," Science 319, pp. 1387-91(2008). https://doi.org/10.1126/science.1152692
  45. Fry, H.C. et al., "Computational design and elaboration of a de novo heterotetrameric alpha-helical protein that selectively binds an emissive abiological , (porphinato) zinc chromophore," Journal of the American Chemical Society 132, pp. 3997-4005, 2010. https://doi.org/10.1021/ja907407m
  46. Suarez, M. & Jaramillo, A., "Challenges in the computational design of proteins," Journal of the Royal Society Suppl 4, pp. S477-91, 2009.