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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22795完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正 | |
| dc.contributor.author | Yu-Feng Huang | en |
| dc.contributor.author | 黃鈺峰 | zh_TW |
| dc.date.accessioned | 2021-06-08T04:28:29Z | - |
| dc.date.copyright | 2010-02-04 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-01-29 | |
| dc.identifier.citation | 1. Crick, F., Central dogma of molecular biology. Nature, 1970. 227(5258): p. 561-3.
2. Laskowski, R.A., et al., Protein clefts in molecular recognition and function. Protein Sci, 1996. 5(12): p. 2438-52. 3. Luscombe, N.M., et al., An overview of the structures of protein-DNA complexes. Genome Biology, 2000. 1(1): p. reviews001.1 - reviews001.37. 4. Jones, S., et al., Protein-RNA interactions: a structural analysis. Nucleic Acids Res, 2001. 29(4): p. 943-54. 5. Jones, S., et al., Protein-DNA interactions: A structural analysis. J Mol Biol, 1999. 287(5): p. 877-96. 6. Mirny, L.A. and M.S. Gelfand, Structural analysis of conserved base pairs in protein-DNA complexes. Nucleic Acids Res, 2002. 30(7): p. 1704-11. 7. Bartlett, G.J., et al., Analysis of catalytic residues in enzyme active sites. J Mol Biol, 2002. 324(1): p. 105-21. 8. Campbell, S.J., et al., Ligand binding: functional site location, similarity and docking. Curr Opin Struct Biol, 2003. 13(3): p. 389-95. 9. Binkowski, T.A., L. Adamian, and J. Liang, Inferring functional relationships of proteins from local sequence and spatial surface patterns. J Mol Biol, 2003. 332(2): p. 505-26. 10. Tian, W. and J. Skolnick, How well is enzyme function conserved as a function of pairwise sequence identity? J Mol Biol, 2003. 333(4): p. 863-82. 11. Lee, D., O. Redfern, and C. Orengo, Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol, 2007. 8(12): p. 995-1005. 12. Devos, D. and A. Valencia, Practical limits of function prediction. Proteins, 2000. 41(1): p. 98-107. 13. Watson, J.D., R.A. Laskowski, and J.M. Thornton, Predicting protein function from sequence and structural data. Curr Opin Struct Biol, 2005. 15(3): p. 275-84. 14. Sadowski, M.I. and D.T. Jones, The sequence-structure relationship and protein function prediction. Curr Opin Struct Biol, 2009. 19(3): p. 357-62. 15. Rentzsch, R. and C.A. Orengo, Protein function prediction--the power of multiplicity. Trends Biotechnol, 2009. 27(4): p. 210-9. 16. Chothia, C. and A.M. Lesk, The relation between the divergence of sequence and structure in proteins. EMBO J, 1986. 5(4): p. 823-6. 17. Rose, G.D. and T.P. Creamer, Protein folding: predicting predicting. Proteins, 1994. 19(1): p. 1-3. 18. Rose, G.D., Protein folding and the Paracelsus challenge. Nat Struct Biol, 1997. 4(7): p. 512-4. 19. Alexander, P.A., et al., The design and characterization of two proteins with 88% sequence identity but different structure and function. Proc Natl Acad Sci U S A, 2007. 104(29): p. 11963-8. 20. Nagano, N., C.A. Orengo, and J.M. Thornton, One fold with many functions: the evolutionary relationships between TIM barrel families based on their sequences, structures and functions. J Mol Biol, 2002. 321(5): p. 741-65. 21. Lesk, A.M. and C. Chothia, How different amino acid sequences determine similar protein structures: the structure and evolutionary dynamics of the globins. J Mol Biol, 1980. 136(3): p. 225-70. 22. Lesk, A.M. and C. Chothia, Evolution of proteins formed by beta-sheets. II. The core of the immunoglobulin domains. J Mol Biol, 1982. 160(2): p. 325-42. 23. Read, R.J., et al., Critical evaluation of comparative model building of Streptomyces griseus trypsin. Biochemistry, 1984. 23(26): p. 6570-5. 24. Todd, A.E., C.A. Orengo, and J.M. Thornton, Evolution of protein function, from a structural perspective. Curr Opin Chem Biol, 1999. 3(5): p. 548-56. 25. Chang, D.T., et al., Protemot: prediction of protein binding sites with automatically extracted geometrical templates. Nucleic Acids Res, 2006. 34(Web Server issue): p. W303-9. 26. Porter, C.T., G.J. Bartlett, and J.M. Thornton, The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res, 2004. 32(Database issue): p. D129-33. 27. Danchin, A., From protein sequence to function. Curr Opin Struct Biol, 1999. 9(3): p. 363-7. 28. Bartlett, G.J., A.E. Todd, and J.M. Thornton, Inferring protein function from structure. Methods Biochem Anal, 2003. 44: p. 387-407. 29. Schlessinger, A. and B. Rost, Protein flexibility and rigidity predicted from sequence. Proteins, 2005. 61(1): p. 115-26. 30. Gerstein, M., A.M. Lesk, and C. Chothia, Structural mechanisms for domain movements in proteins. Biochemistry, 1994. 33(22): p. 6739-49. 31. Daniel, R.M., et al., The role of dynamics in enzyme activity. Annu Rev Biophys Biomol Struct, 2003. 32: p. 69-92. 32. Bloom, J.D., et al., Protein stability promotes evolvability. Proc Natl Acad Sci U S A, 2006. 103(15): p. 5869-74. 33. Jaenicke, R., Stability and stabilization of globular proteins in solution. J Biotechnol, 2000. 79(3): p. 193-203. 34. Shoichet, B.K., et al., A relationship between protein stability and protein function. Proc Natl Acad Sci U S A, 1995. 92(2): p. 452-6. 35. Pakula, A.A. and R.T. Sauer, Genetic analysis of protein stability and function. Annu Rev Genet, 1989. 23: p. 289-310. 36. Beadle, B.M. and B.K. Shoichet, Structural bases of stability-function tradeoffs in enzymes. J Mol Biol, 2002. 321(2): p. 285-96. 37. de Leeuw, M., et al., Coexistence of flexibility and stability of proteins: an equation of state. PLoS One, 2009. 4(10): p. e7296. 38. Reuveni, S., R. Granek, and J. Klafter, Proteins: coexistence of stability and flexibility. Phys Rev Lett, 2008. 100(20): p. 208101. 39. Luque, I. and E. Freire, Structural stability of binding sites: consequences for binding affinity and allosteric effects. Proteins, 2000. Suppl 4: p. 63-71. 40. Fischer, E., Einfluss der Configuration auf die Wirkung der Enzyme. Berichte der deutschen chemischen Gesellschaft, 1894. 27(3): p. 2985-2993. 41. Koshland, D.E., Application of a Theory of Enzyme Specificity to Protein Synthesis. Proc Natl Acad Sci U S A, 1958. 44(2): p. 98-104. 42. Daniel, E.K., Jr., The Key-Lock Theory and the Induced Fit Theory. Angewandte Chemie International Edition in English, 1995. 33(23-24): p. 2375-2378. 43. Koshland, D.E., Jr., Crazy, but correct. Nature, 2004. 432(7016): p. 447. 44. Koshland, D.E., Jr., Enzyme flexibility and enzyme action. J Cell Comp Physiol, 1959. 54: p. 245-58. 45. Stockwell, G.R. and J.M. Thornton, Conformational diversity of ligands bound to proteins. J Mol Biol, 2006. 356(4): p. 928-44. 46. Tsai, C.J., et al., Structured disorder and conformational selection. Proteins, 2001. 44(4): p. 418-27. 47. Yu, E.W. and D.E. Koshland, Jr., Propagating conformational changes over long (and short) distances in proteins. Proc Natl Acad Sci U S A, 2001. 98(17): p. 9517-20. 48. Hornak, V. and C. Simmerling, Targeting structural flexibility in HIV-1 protease inhibitor binding. Drug Discov Today, 2007. 12(3-4): p. 132-8. 49. Hornak, V., et al., HIV-1 protease flaps spontaneously open and reclose in molecular dynamics simulations. Proc Natl Acad Sci U S A, 2006. 103(4): p. 915-20. 50. Hornak, V., et al., HIV-1 protease flaps spontaneously close to the correct structure in simulations following manual placement of an inhibitor into the open state. J Am Chem Soc, 2006. 128(9): p. 2812-3. 51. Jones, D.T., Protein structure prediction in genomics. Brief Bioinform, 2001. 2(2): p. 111-25. 52. Mizoue, L.S. and W.J. Chazin, Engineering and design of ligand-induced conformational change in proteins. Curr Opin Struct Biol, 2002. 12(4): p. 459-63. 53. Kahraman, A., et al., Shape variation in protein binding pockets and their ligands. J Mol Biol, 2007. 368(1): p. 283-301. 54. Kahraman, A., et al., Variation of geometrical and physicochemical properties in protein binding pockets and their ligands. BMC Bioinformatics, 2007. 8(Suppl 8): p. S1. 55. Fischer, D., et al., Surface motifs by a computer vision technique: searches, detection, and implications for protein-ligand recognition. Proteins, 1993. 16(3): p. 278-92. 56. Norel, R., et al., Molecular surface recognition by a computer vision-based technique. Protein Eng, 1994. 7(1): p. 39-46. 57. Dundas, J., et al., CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res, 2006. 34(Web Server issue): p. W116-8. 58. Binkowski, T.A., S. Naghibzadeh, and J. Liang, CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res, 2003. 31(13): p. 3352-5. 59. Ferre, F., et al., Functional annotation by identification of local surface similarities: a novel tool for structural genomics. BMC Bioinformatics, 2005. 6: p. 194. 60. Shulman-Peleg, A., R. Nussinov, and H.J. Wolfson, Recognition of functional sites in protein structures. J Mol Biol, 2004. 339(3): p. 607-33. 61. Tseng, Y.Y., J. Dundas, and J. Liang, Predicting protein function and binding profile via matching of local evolutionary and geometric surface patterns. J Mol Biol, 2009. 387(2): p. 451-64. 62. Tseng, Y.Y., et al., SplitPocket: identification of protein functional surfaces and characterization of their spatial patterns. Nucleic Acids Res, 2009. 37(Web Server issue): p. W384-9. 63. Binkowski, T.A., P. Freeman, and J. Liang, pvSOAR: detecting similar surface patterns of pocket and void surfaces of amino acid residues on proteins. Nucleic Acids Res, 2004. 32(Web Server issue): p. W555-8. 64. Berman, H.M., et al., The Protein Data Bank. Acta Crystallogr D Biol Crystallogr, 2002. 58(Pt 6 No 1): p. 899-907. 65. Shindyalov, I.N. and P.E. Bourne, Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng, 1998. 11(9): p. 739-47. 66. Holm, L. and C. Sander, Dali: a network tool for protein structure comparison. Trends Biochem Sci, 1995. 20(11): p. 478-80. 67. Krissinel, E. and K. Henrick, Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr, 2004. 60(Pt 12 Pt 1): p. 2256-68. 68. Jones, D.T., Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol, 1999. 292(2): p. 195-202. 69. Rost, B., Review: protein secondary structure prediction continues to rise. J Struct Biol, 2001. 134(2-3): p. 204-18. 70. Ahmad, S. and A. Sarai, PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics, 2005. 6: p. 33. 71. Wang, L. and S.J. Brown, Prediction of DNA-binding residues from sequence features. J Bioinform Comput Biol, 2006. 4(6): p. 1141-58. 72. Wu, J., et al., Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics, 2009. 25(1): p. 30-5. 73. Yuan, Z., T.L. Bailey, and R.D. Teasdale, Prediction of protein B-factor profiles. Proteins, 2005. 58(4): p. 905-12. 74. Su, C.T., C.Y. Chen, and C.M. Hsu, iPDA: integrated protein disorder analyzer. Nucleic Acids Res, 2007. 35(Web Server issue): p. W465-72. 75. Su, C.T., C.Y. Chen, and Y.Y. Ou, Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics, 2006. 7: p. 319. 76. Ward, J.J., et al., The DISOPRED server for the prediction of protein disorder. Bioinformatics, 2004. 20(13): p. 2138-9. 77. Boden, M. and T.L. Bailey, Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. Bioinformatics, 2006. 22(15): p. 1809-14. 78. Sigrist, C.J., et al., PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform, 2002. 3(3): p. 265-74. 79. Bystroff, C., V. Thorsson, and D. Baker, HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol, 2000. 301(1): p. 173-90. 80. Sonnhammer, E.L., et al., Pfam: multiple sequence alignments and HMM-profiles of protein domains. Nucleic Acids Res, 1998. 26(1): p. 320-2. 81. Hsu, C.M., C.Y. Chen, and B.J. Liu, MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences. Nucleic Acids Res, 2006. 34(Web Server issue): p. W356-61. 82. Torrance, J.W., et al., Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families. J Mol Biol, 2005. 347(3): p. 565-81. 83. Kinoshita, K. and H. Nakamura, eF-site and PDBjViewer: database and viewer for protein functional sites. Bioinformatics, 2004. 20(8): p. 1329-30. 84. Ivanisenko, V.A., et al., PDBSiteScan: a program for searching for active, binding and posttranslational modification sites in the 3D structures of proteins. Nucleic Acids Res, 2004. 32(Web Server issue): p. W549-54. 85. Schmitt, S., D. Kuhn, and G. Klebe, A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol, 2002. 323(2): p. 387-406. 86. Glaser, F., et al., A method for localizing ligand binding pockets in protein structures. Proteins, 2006. 62(2): p. 479-88. 87. Laskowski, R.A., SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph, 1995. 13(5): p. 323-30, 307-8. 88. Kleywegt, G.J. and T.A. Jones, Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr D Biol Crystallogr, 1994. 50(Pt 2): p. 178-85. 89. Henschel, A., W.K. Kim, and M. Schroeder, Equivalent binding sites reveal convergently evolved interaction motifs. Bioinformatics, 2006. 22(5): p. 550-5. 90. McDonald, I.K. and J.M. Thornton, Satisfying hydrogen bonding potential in proteins. J Mol Biol, 1994. 238(5): p. 777-93. 91. von Hippel, P.H., Biochemistry. Completing the view of transcriptional regulation. Science, 2004. 305(5682): p. 350-2. 92. McGhee, J.D. and P.H. von Hippel, Theoretical aspects of DNA-protein interactions: co-operative and non-co-operative binding of large ligands to a one-dimensional homogeneous lattice. J Mol Biol, 1974. 86(2): p. 469-89. 93. von Hippel, P.H., Protein-DNA recognition: new perspectives and underlying themes. Science, 1994. 263(5148): p. 769-70. 94. von Hippel, P.H. and O.G. Berg, On the specificity of DNA-protein interactions. Proc Natl Acad Sci U S A, 1986. 83(6): p. 1608-12. 95. Von Hippel, P.H. and J.D. McGhee, DNA-protein interactions. Annu Rev Biochem, 1972. 41(10): p. 231-300. 96. Cheng, A.C., et al., Recognition of nucleic acid bases and base-pairs by hydrogen bonding to amino acid side-chains. J Mol Biol, 2003. 327(4): p. 781-96. 97. Boyer, R.F., Concepts in Biochemistry: Structure Tutorials, in Concepts in Biochemistry. 2005, Wiley. p. 736 98. Jeltsch, A., Beyond Watson and Crick: DNA methylation and molecular enzymology of DNA methyltransferases. Chembiochem, 2002. 3(4): p. 274-93. 99. Luscombe, N.M. and J.M. Thornton, Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity. J Mol Biol, 2002. 320(5): p. 991-1009. 100. Ahmad, S., M.M. Gromiha, and A. Sarai, Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics, 2004. 20(4): p. 477-86. 101. Bhardwaj, N., et al., Structure Based Prediction of Binding Residues on DNA-binding Proteins. Conf Proc IEEE Eng Med Biol Soc, 2005. 3: p. 2611-4. 102. Bhardwaj, N. and H. Lu, Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions. FEBS Lett, 2007. 581(5): p. 1058-66. 103. Chen, Y.C., C.Y. Wu, and C. Lim, Predicting DNA-binding amino acid residues from electrostatic stabilization upon mutation to Asp/Glu and evolutionary conservation. Proteins, 2007. 67(3): p. 671-80. 104. Ferrer-Costa, C., et al., HTHquery: a method for detecting DNA-binding proteins with a helix-turn-helix structural motif. Bioinformatics, 2005. 21(18): p. 3679-80. 105. Hwang, S., Z. Gou, and I.B. Kuznetsov, DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins. Bioinformatics, 2007. 23(5): p. 634-6. 106. Jones, S., et al., Using structural motif templates to identify proteins with DNA binding function. Nucleic Acids Res, 2003. 31(11): p. 2811-23. 107. Jones, S., et al., Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins. Nucleic Acids Res, 2003. 31(24): p. 7189-98. 108. Shanahan, H.P., et al., Identifying DNA-binding proteins using structural motifs and the electrostatic potential. Nucleic Acids Res, 2004. 32(16): p. 4732-41. 109. Tjong, H. and H.X. Zhou, DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces. Nucleic Acids Res, 2007. 35(5): p. 1465-77. 110. Tsuchiya, Y., K. Kinoshita, and H. Nakamura, Structure-based prediction of DNA-binding sites on proteins using the empirical preference of electrostatic potential and the shape of molecular surfaces. Proteins, 2004. 55(4): p. 885-94. 111. Tsuchiya, Y., K. Kinoshita, and H. Nakamura, PreDs: a server for predicting dsDNA-binding site on protein molecular surfaces. Bioinformatics, 2005. 21(8): p. 1721-3. 112. Wang, L. and S.J. Brown, BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Res, 2006. 34(Web Server issue): p. W243-8. 113. Yan, C., et al., Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics, 2006. 7: p. 262. 114. Ofran, Y., V. Mysore, and B. Rost, Prediction of DNA-binding residues from sequence. Bioinformatics, 2007. 23(13): p. i347-53. 115. Cho, S.Y., et al., ZIFIBI: Prediction of DNA binding sites for zinc finger proteins. Biochem Biophys Res Commun, 2008. 369(3): p. 845-8. 116. Casari, G., C. Sander, and A. Valencia, A method to predict functional residues in proteins. Nat Struct Biol, 1995. 2(2): p. 171-8. 117. Andrade, M.A., et al., Classification of protein families and detection of the determinant residues with an improved self-organizing map. Biol Cybern, 1997. 76(6): p. 441-50. 118. Pei, J. and N.V. Grishin, AL2CO: calculation of positional conservation in a protein sequence alignment. Bioinformatics, 2001. 17(8): p. 700-12. 119. Chien, T.Y., et al., E1DS: catalytic site prediction based on 1D signatures of concurrent conservation. Nucleic Acids Res, 2008. 120. Chang, D.T., T.Y. Chien, and C.Y. Chen, seeMotif: exploring and visualizing sequence motifs in 3D structures. Nucleic Acids Res, 2009. 121. del Sol, A., F. Pazos, and A. Valencia, Automatic methods for predicting functionally important residues. J Mol Biol, 2003. 326(4): p. 1289-302. 122. Lichtarge, O., H.R. Bourne, and F.E. Cohen, An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol, 1996. 257(2): p. 342-58. 123. Aloy, P., et al., Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. J Mol Biol, 2001. 311(2): p. 395-408. 124. Madabushi, S., et al., Structural clusters of evolutionary trace residues are statistically significant and common in proteins. J Mol Biol, 2002. 316(1): p. 139-54. 125. Reddy, C.K., A. Das, and B. Jayaram, Do water molecules mediate protein-DNA recognition? J Mol Biol, 2001. 314(3): p. 619-32. 126. Luscombe, N.M., R.A. Laskowski, and J.M. Thornton, Amino acid-base interactions: a three-dimensional analysis of protein-DNA interactions at an atomic level. Nucleic Acids Res, 2001. 29(13): p. 2860-74. 127. Chu, W.Y., et al., ProteDNA: a sequence-based predictor of sequence-specific DNA-binding residues in transcription factors. Nucleic Acids Res, 2009(Web Server issue): p. to appear. 128. Calkhoven, C.F. and G. Ab, Multiple steps in the regulation of transcription-factor level and activity. Biochem J, 1996. 317 ( Pt 2): p. 329-42. 129. Latchman, D.S., Transcription factors: an overview. Int J Biochem Cell Biol, 1997. 29(12): p. 1305-12. 130. Pabo, C.O. and R.T. Sauer, Transcription factors: structural families and principles of DNA recognition. Annu Rev Biochem, 1992. 61: p. 1053-95. 131. Liu, J., et al., Intrinsic disorder in transcription factors. Biochemistry, 2006. 45(22): p. 6873-88. 132. Kalodimos, C.G., et al., Structure and flexibility adaptation in nonspecific and specific protein-DNA complexes. Science, 2004. 305(5682): p. 386-9. 133. Luscombe, N.M., et al., An overview of the structures of protein-DNA complexes. Genome Biol, 2000. 1(1): p. REVIEWS001. 134. Luisi, B., DNA-protein interaction at high resolution. In DNA-Protein Structural Interactions. Edited by Lilley DMJ. New York: Oxford University Press, 1995: p. 1 - 48. 135. Harrison, S.C., A structural taxonomy of DNA-binding domains. Nature, 1991. 353(6346): p. 715-9. 136. Prabakaran, P., et al., Classification of protein-DNA complexes based on structural descriptors. Structure, 2006. 14(9): p. 1355-67. 137. Wintjens, R. and M. Rooman, Structural classification of HTH DNA-binding domains and protein-DNA interaction modes. J Mol Biol, 1996. 262(2): p. 294-313. 138. Vazquez, M.E., A.M. Caamano, and J.L. Mascarenas, From transcription factors to designed sequence-specific DNA-binding peptides. Chem Soc Rev, 2003. 32(6): p. 338-49. 139. Siggers, T.W., A. Silkov, and B. Honig, Structural alignment of protein--DNA interfaces: insights into the determinants of binding specificity. J Mol Biol, 2005. 345(5): p. 1027-45. 140. Ashburner, M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25-9. 141. Bhaskar, H., D.C. Hoyle, and S. Singh, Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput Biol Med, 2006. 36(10): p. 1104-25. 142. Larranaga, P., et al., Machine learning in bioinformatics. Brief Bioinform, 2006. 7(1): p. 86-112. 143. Altschul, S.F., et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 1997. 25(17): p. 3389-402. 144. Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. 2001: p. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. 145. Finn, R.D., et al., The Pfam protein families database. Nucleic Acids Res, 2008. 36(Database issue): p. D281-8. 146. Kabsch, W. and C. Sander, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983. 22(12): p. 2577-637. 147. McGuffin, L.J., K. Bryson, and D.T. Jones, The PSIPRED protein structure prediction server. Bioinformatics, 2000. 16(4): p. 404-5. 148. Orengo, C.A., A.E. Todd, and J.M. Thornton, From protein structure to function. Curr Opin Struct Biol, 1999. 9(3): p. 374-82. 149. Pal, D. and D. Eisenberg, Inference of protein function from protein structure. Structure, 2005. 13(1): p. 121-30. 150. Whisstock, J.C. and A.M. Lesk, Prediction of protein function from protein sequence and structure. Q Rev Biophys, 2003. 36(3): p. 307-40. 151. Pearson, W.R. and D.J. Lipman, Improved tools for biological sequence comparison. Proc Natl Acad Sci U S A, 1988. 85(8): p. 2444-8. 152. Altschul, S.F., et al., Basic local alignment search tool. J Mol Biol, 1990. 215(3): p. 403-10. 153. Goldsmith-Fischman, S. and B. Honig, Structural genomics: computational methods for structure analysis. Protein Sci, 2003. 12(9): p. 1813-21. 154. Laskowski, R.A., J.D. Watson, and J.M. Thornton, From protein structure to biochemical function? J Struct Funct Genomics, 2003. 4(2-3): p. 167-77. 155. Gan, H.H., et al., Analysis of protein sequence/structure similarity relationships. Biophys J, 2002. 83(5): p. 2781-91. 156. Juers, D.H., R.E. Huber, and B.W. Matthews, Structural comparisons of TIM barrel proteins suggest functional and evolutionary relationships between beta-galactosidase and other glycohydrolases. Protein Sci, 1999. 8(1): p. 122-36. 157. Jankowsky, E. and M.E. Fairman, RNA helicases--one fold for many functions. Curr Opin Struct Biol, 2007. 17(3): p. 316-24. 158. 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 Res, 2005. 33(Database issue): p. D238-41. 159. Najmanovich, R.J., J.W. Torrance, and J.M. Thornton, Prediction of protein function from structure: insights from methods for the detection of local structural similarities. Biotechniques, 2005. 38(6): p. 847, 849, 851. 160. Sander, O., I. Sommer, and T. Lengauer, Local protein structure prediction using discriminative models. BMC Bioinformatics, 2006. 7: p. 14. 161. Liu, Z.P., et al., Bridging protein local structures and protein functions. Amino Acids, 2008. 162. Wallace, A.C., R.A. Laskowski, and J.M. Thornton, LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng, 1995. 8(2): p. 127-34. 163. Schuttelkopf, A.W. and D.M. van Aalten, PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr, 2004. 60(Pt 8): p. 1355-63. 164. Stuart, A.C., V.A. Ilyin, and A. Sali, LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures. Bioinformatics, 2002. 18(1): p. 200-1. 165. Todd, A.E., C.A. Orengo, and J.M. Thornton, Evolution of function in protein superfamilies, from a structural perspective. J Mol Biol, 2001. 307(4): p. 1113-43. 166. Rost, B., Enzyme function less conserved than anticipated. J Mol Biol, 2002. 318(2): p. 595-608. 167. Rost, B., et al., Automatic prediction of protein function. Cell Mol Life Sci, 2003. 60(12): p. 2637-50. 168. Bairoch, A., The ENZYME data bank. Nucleic Acids Res, 1993. 21(13): p. 3155-6. 169. Chen, S.C. and I. Bahar, Mining frequent patterns in protein structures: a study of protease families. Bioinformatics, 2004. 20 Suppl 1: p. I77-I85. 170. Keskin, O. and R. Nussinov, Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways. Protein Eng Des Sel, 2005. 18(1): p. 11-24. 171. Jonassen, I., et al., Structure motif discovery and mining the PDB. Bioinformatics, 2002. 18(2): p. 362-7. 172. Li, W. and A. Godzik, Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006. 22(13): p. 1658-9. 173. Wolfson, H.J. and I. Rigoutsos, Geometric hashing: an overview. Computational Science and Engineering, IEEE [see also Computing in Science & Engineering], 1997. 4(4): p. 10-21. 174. Martin, A.C., PDBSprotEC: a Web-accessible database linking PDB chains to EC numbers via SwissProt. Bioinformatics, 2004. 20(6): p. 986-8. 175. DeLano, W.L., The PyMOL Molecular Graphics System, D. Scientific, Editor. 2002: Palo Alto, CA, USA. 176. Kim, S., et al., Structure of Physarum polycephalum cytochrome b5 reductase at 1.56 A resolution. Acta Crystallogr Sect F Struct Biol Cryst Commun, 2007. 63(Pt 4): p. 274-9. 177. Percy, M.J., et al., Expression of a novel P275L variant of NADH:cytochrome b5 reductase gives functional insight into the conserved motif important for pyridine nucleotide binding. Arch Biochem Biophys, 2006. 447(1): p. 59-67. 178. Pettersen, E.F., et al., UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem, 2004. 25(13): p. 1605-12. 179. Jmol, Jmol: an open-source Java viewer for chemical structures in 3D, http://www.jmol.org . 2008. 180. Teague, S.J., Implications of protein flexibility for drug discovery. Nat Rev Drug Discov, 2003. 2(7): p. 527-41. 181. Todd, A.E., C.A. Orengo, and J.M. Thornton, Plasticity of enzyme active sites. Trends Biochem Sci, 2002. 27(8): p. 419-26. 182. Tsou, C.L., Conformational flexibility of enzyme active sites. Science, 1993. 262(5132): p. 380-1. 183. Tsou, C.L., The role of active site flexibility in enzyme catalysis. Biochemistry (Mosc), 1998. 63(3): p. 253-8. 184. Yuan, Z., J. Zhao, and Z.X. Wang, Flexibility analysis of enzyme active sites by crystallographic temperature factors. Protein Eng, 2003. 16(2): p. 109-14. 185. Bloom, J.D., et al., Stability and the evolvability of function in a model protein. Biophys J, 2004. 86(5): p. 2758-64. 186. Shafferman, A., et al., Flexibility versus 'rigidity' of the functional architecture of AChE active center. Chem Biol Interact, 2008. 175(1-3): p. 166-72. 187. Petsko, G.A. and D. Ringe, Protein Structure and Function 2003: Blackwell Publishing. 188. Kumar, S., H.J. Wolfson, and R. Nussinov, Protein flexibility and electrostatic interactions. IBM Journal of Research and Development, 2001. 45: p. 14. 189. Blow, D., Outline of Crystallography for Biologists. 2002, New York: Oxford University Press. 190. Minor, D.L., Jr., The neurobiologist's guide to structural biology: a primer on why macromolecular structure matters and how to evaluate structural data. Neuron, 2007. 54(4): p. 511-33. 191. Berman, H., et al., The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res, 2007. 35(Database issue): p. D301-3. 192. The UniProt, C., The Universal Protein Resource (UniProt). Nucl. Acids Res., 2008. 36(suppl_1): p. D190-195. 193. Berman, H.M., et al., The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophys J, 1992. 63(3): p. 751-9. 194. Bairoch, A. and B. Boeckmann, The SWISS-PROT protein sequence data bank. Nucleic Acids Res, 1991. 19 Suppl: p. 2247-9. 195. Richardson, J.S., The anatomy and taxonomy of protein structure. Adv Protein Chem, 1981. 34: p. 167-339. 196. Wetlaufer, D.B., Nucleation, rapid folding, and globular intrachain regions in proteins. Proc Natl Acad Sci U S A, 1973. 70(3): p. 697-701. 197. Bourne, P.E. and H. Weissig, Structural Bioinformatics. Methods of Biochemical Analysis, ed. P.E. Bourne and H. Weissig. Vol. 44. 2003: Wiley-Liss. 198. Dor, O. and Y. Zhou, Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins, 2007. 66(4): p. 838-45. 199. Linding, R., et al., Protein disorder prediction: implications for structural proteomics. Structure, 2003. 11(11): p. 1453-9. 200. Iakoucheva, L.M. and A.K. Dunker, Order, disorder, and flexibility: prediction from protein sequence. Structure, 2003. 11(11): p. 1316-7. 201. Schlessinger, A., G. Yachdav, and B. Rost, PROFbval: predict flexible and rigid residues in proteins. Bioinformatics, 2006. 22(7): p. 891-3. 202. Kuznetsov, I.B. and M. McDuffie, FlexPred: a web-server for predicting residue positions involved in conformational switches in proteins. Bioinformation, 2008. 3(3): p. 134-6. 203. Young, M., et al., Predicting conformational switches in proteins. Protein Sci, 1999. 8(9): p. 1752-64. 204. Dan, A., Y. Ofran, and Y. Kliger, Large-scale analysis of secondary structure changes in proteins suggests a role for disorder-to-order transitions in nucleotide binding proteins. Proteins, 2009. 205. Kuznetsov, I.B., Ordered conformational change in the protein backbone: prediction of conformationally variable positions from sequence and low-resolution structural data. Proteins, 2008. 72(1): p. 74-87. 206. Bradford, J.R. and D.R. Westhead, Improved prediction of protein-protein binding sites using a support vector machines approach. Bioinformatics, 2005. 21(8): p. 1487-94. 207. Neuvirth, H., R. Raz, and G. Schreiber, ProMate: a structure based prediction program to identify the location of protein-protein binding sites. J Mol Biol, 2004. 338(1): p. 181-99. 208. Cheng, G., et al., Improvement in p | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22795 | - |
| dc.description.abstract | 蛋白質功能性區塊的探索一直以來都是生物學家所追求的。蛋白質透過自身的殘基與相互作用的對象進行作用時，作用區內的殘基扮演著不同的角色，有些是負責進行核心作用的辨識機制，而有些則是負責穩定蛋白質與互動對象間的結合環境。因此，預測蛋白質結合殘基座落的位置仍然是生物學家所關心的重大議題，不論是透過序列或結構資訊的深入分析都是重要且必須的處理方式。
對於蛋白質結合殘基預測的方法主要可以分成兩大類：序列為主與結構為主。本論文分別提出序列與結構兩個面向的架構來解決，並分別應用在轉錄因子以及酵素上。透過蛋白質序列來預測轉錄因子上專一性殘基與非專一性參級的座落位置，並且經由結合殘基預測的資料更進一步預測蛋白質與去氧核醣核酸間的結合模式。透過結合模式的預測，我們可以有效地增加預測的效能。而在酵素這類型的蛋白質，雖然這類型的蛋白質是屬於相對穩定的蛋白質，但由於配體的形變因而也造成結合區塊隨之改變。因此我們透過蛋白質穩定區塊所探勘而成的結構模板藉由預測酵素家族來檢定所探勘之結構模板的品質。 最後，對於一個未知的蛋白質，藉由預測的方式取得可能的結合殘基位置可以協助生物學家進行較為精準的生化實驗。而實驗結果也呈現出蛋白質與不同相互作用的對象具有不同的功能，結合環境和特異性。因此藉由更細緻的分群方式使可以有效提升預測的準確性。 | zh_TW |
| dc.description.abstract | Protein carries out its function by interaction between binding sites/residues and its interacting partners. Functional important residues recognize and bind with protein’s interacting partners; residues close to functional important residues play roles to support functional site’s activity; remaining residues play roles to construct global protein structure. Therefore, prediction of protein binding residues from sequence and/or structure is investigated deeply and still a great challenge for many years.
Two directions to predict protein binding sites and then infer protein functions are methods based on evolutionary information and methods based on structural template library. From protein sequence information, predicting DNA-binding residues in transcription factors based on machine learning approach with evolutionary information can help biologists for further experiments because transcription factors are mostly disorder proteins. From protein structure information, structural templates are extracted by stable region identification for identifying enzyme family without the help of protein-ligand complexes. In conclusion, predicting protein binding residues and protein functions is the first step to help biologists to have rudimentary view of proteins with annotations. Experimental results reveal that proteins binding with different interacting targets have different binding environment and specificity. Grouping proteins by protein mechanism, binding target, or structural conformation for predicting binding residues is suggested as well. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T04:28:29Z (GMT). No. of bitstreams: 1 ntu-99-D92922019-1.pdf: 4691630 bytes, checksum: a6d52cd376d6de94b2b57cd43df68a5b (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 審定書 I
謝辭 III 中文摘要 V ABSTRACT VII TABLE OF CONTENTS IX LIST OF FIGURES XI LIST OF TABLES XII 1. INTRODUCTION 1 1.1. BACKGROUND 1 1.2. MOTIVATION 2 1.3. OBJECTIVE 2 1.4. CONTRIBUTIONS 3 1.5. OVERVIEW 4 2. RELATED WORKS 7 2.1. PROTEINS: SEQUENCE, STRUCTURE AND FUNCTION 7 2.1.1. Protein Stability, Protein Flexibility, and Protein Function 9 2.1.2. The Importance of Protein Local Region 11 2.2. OVERVIEW OF PREDICTION OF PROTEIN BINDING SITES 12 2.2.1. Category of Proteins 14 2.2.2. Category of Protein Binding Sites 15 Catalytic Residues in Enzyme Active Sites 15 DNA-binding Sites 16 2.3. RELATED WORKS ON PREDICTION OF PROTEIN BINDING SITES 17 Prediction of DNA-binding Proteins and DNA-binding Residues 17 Prediction of Enzyme Family and Enzyme Active Sites 19 3. PREDICTION OF DNA-BINDING RESIDUE AND BINDING MODE IN TRANSCRIPTION FACTORS 23 3.1. INTRODUCTION 23 3.2. MATERIALS AND METHODS 28 3.2.1. Datasets 28 3.2.2. Defining the DNA-binding residue 30 3.2.3. Framework of DNA-binding residues and binding mode prediction using support vector machine 31 3.2.4. Predictor performance measures 35 3.3. RESULTS AND DISCUSSION 36 3.4. CONCLUSION 42 4. PREDICTION OF ENZYME FAMILY AND ITS BINDING RESIDUES 45 4.1. BACKGROUND 45 4.2. METHODS 48 4.2.1. Structural template extraction 49 4.2.2. Building template library 51 4.2.3. Enzyme family prediction 53 4.3. RESULTS 55 4.3.1. Evaluation on enzyme family prediction 55 4.3.2. Insight into structural templates 59 4.4. DISCUSSION 63 4.5. CONCLUSIONS 66 5. CONCLUSIONS AND FUTURE WORKS 69 REFERENCES 71 APPENDIX 85 A1. INTRODUCTION TO PROTEIN STRUCTURE 85 Protein Structure Levels 85 Primary Structure 85 Secondary Structure 87 Tertiary Structure 88 Quaternary Structure 89 A2. PROTEIN DATABASES 90 Protein Data Bank (PDB) 90 Universal Protein Resource (UniProt) 91 Nucleic Acid Database (NDB) 92 Enzyme Data Bank 92 Enzyme Classification 93 Gene Ontology (GO) 94 Pfam 95 A3. CURRENT STATUS OF STRUCTURAL BIOINFORMATICS 96 LIST OF PUBLICATIONS 101 | |
| dc.language.iso | en | |
| dc.subject | 酵素 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 資料探勘 | zh_TW |
| dc.subject | 蛋白質作用區(結合殘基)預測 | zh_TW |
| dc.subject | 轉錄因子 | zh_TW |
| dc.subject | 與DNA作用之蛋白質 | zh_TW |
| dc.subject | DNA-binding protein | en |
| dc.subject | enzyme. | en |
| dc.subject | machine learning | en |
| dc.subject | data mining | en |
| dc.subject | protein binding sites/residues prediction | en |
| dc.subject | transcription factor | en |
| dc.title | 蛋白質結合殘基預測之研究 | zh_TW |
| dc.title | A Study of Prediction of Protein Binding Residues | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 黃乾綱 | |
| dc.contributor.oralexamcommittee | 許聞廉,林榮信,阮雪芬,曾宇鳳,歐陽明,許永真,趙坤茂 | |
| dc.subject.keyword | 機器學習,資料探勘,蛋白質作用區(結合殘基)預測,轉錄因子,與DNA作用之蛋白質,酵素, | zh_TW |
| dc.subject.keyword | machine learning,data mining,protein binding sites/residues prediction,transcription factor,DNA-binding protein,enzyme., | en |
| dc.relation.page | 103 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2010-01-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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