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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林文澧,歐陽彥正 | |
| dc.contributor.author | Yu-Cheng Liu | en |
| dc.contributor.author | 劉諭承 | zh_TW |
| dc.date.accessioned | 2021-06-15T04:10:31Z | - |
| dc.date.available | 2015-02-01 | |
| dc.date.copyright | 2010-02-01 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-01-29 | |
| dc.identifier.citation | Reference
1. Teilum K OJ, Kragelund BB.: Functional aspects of protein flexibility. cellular and molecular life sciences 2009, 66(14):p2231-2247. 2. Yuan Z, Zhao J, Wang ZX: Flexibility analysis of enzyme active sites by crystallographic temperature factors. Protein Eng 2003, 16(2):109-114. 3. Mary K. Campbell SOF: biochemistry, fifth edn: thomson brooks/cole; 2006. 4. Gerhard Wagner KW: Observation of internal motility of proteins by nuclear magnetic resonance in solution. methos in enzymology 1986, 131:p307-326. 5. C. M. Dobson MK: Internal motion of proteins: Nuclear magnetic resonance measurements and dynamic simulations. methos in enzymology 1986, 131:P362-389. 6. Dagmar Ringe GAP: Study of protein dynamics by X-ray diffraction. methos in enzymology 1986, 131:p389-433. 7. Wüthrich K: Protein structure determination in solution by NMR spectroscopy. journal of biological chemistry 1990, 265(36):p22059-22062. 8. Gaohua Liu YS, Hanudatta S. Atreya, David Parish, Ying Shao, Dinesh K. Sukumaran, Rong Xiao, Adelinda Yee, Alexander Lemak, Aneerban Bhattacharya, Thomas A. Acton, Cheryl H. Arrowsmith, Gaetano T. Montelione and Thomas Szyperski: NMR data collection and analysis protocol for high-throughput protein structure determination. PNAS 2005, 102(30):p10487-10492. 9. Oliviero Carugo PA: Accessibility to internal cavities and ligand binding sites monitored by protein crystallographic thermal factors Proteins 1998, 31:p201-213. 10. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000, 28(1):235-242. 11. Fiaux J BE, Horwich AL, Wüthrich K.: NMR analysis of a 900K GroEL GroES complex. nature 2002, 418(6894):p207-211. 12. Roscoe G. Dickinson ALR: The Crystal Structure of Hexamethylene-Tetramine. journal of the american chemical society 1923, 45(1):pp22-29. 13. Boer GMd: X-ray Investigation of the Polymorphism of Fatty Acids. nature 1927 (30th April), 119:p634-635. 14. Yan Qi MCS, Kwangho Nam, Anirban Banerjee, Sao Jiralerspong, Martin Karplus and Gregory L. Verdine: Encounter and extrusion of an intrahelical lesion by a DNA repair enzyme. nature 2009 December 10th, 462:p762-766. 15. Seungjoo Lee LA, Rune Hartmann, Karen J Knaus, Krystyna Surewicz, Witold K Surewicz, Vivien C Yee: Conformational diversity in prion protein variants influences intermolecular β-sheet formation The EMBO journal 2009:p1-12. 16. Hans Frauenfelder GAPaDT: Temperature-dependent X-ray diffraction as a probe of protein structural dynamics. nature 1979, 280:p558-563. 17. P. J. Artymiuk CCFB, D. E. P. Grace, S. J. Oatley, D. C. Phillips and M. J. E. Sternberg Crystallographic studies of the dynamic properties of lysozyme nature 1979, 280:p563-568. 18. Akinori Kidera NG: Normal mode refinement: Crystallographic refinement of protein dynamic structure : I. Theory and test by simulated diffraction data. JMB 1992, 225:p547-475. 19. Akinori Kidera KI, Masaaki Matsushima, Nobuhiro Go: Normal mode refinement: Crystallographic refinement of protein dynamic structure : II. Application to human lysozyme. JMB 2002, 225:p477-486. 20. Nobutami kasai MK: X-ray diffraction by macromolecules. Kodansha and Springer-Verlag Berlin Heidelberg 2005. 21. Wikipedia: http://en.wikipedia.org/wiki/Protein_crystallography#Protein_crystallography. 22. Wilson CC: A basic introduction to thermal motions of atoms in crystal structures, the underlying potentials and the physical information available from their analysis. crystallography reviews 2009, 15(1):p3-56. 23. Halle B: Flexibility and packing in proteins. Proc Natl Acad Sci U S A 2002, 99(3):1274-1279. 24. RasMol and OpenRasMol [http://www.openrasmol.org/] 25. Jmol: an open-source Java viewer for chemical structures in 3D. [http://www.jmol.org/ ] 26. Geoffrey A. Mueller, Koteppa Pari, Eugene F. DeRose, Thomas W. Kirby, London RE: Backbone Dynamics of the RNase H Domain of HIV-1 Reverse Transcriptase. Biochemistry 2004, 43(29):p9332-9342. 27. R. Ragone FF, A. Facchiano, A.M. Facchiano and G. Colonna: Flexibility plot of proteins Peds 1989, 2(7):p497-504. 28. Hinsen K: Structural flexibility in proteins: impact of the crystal environment. Bioinformatics 2008, 24(4):521-528. 29. Song G JR: vGNM: A Better Model for Understanding the Dynamics of Proteins in Crystals. JMB 2007, 369(3):p880-893. 30. Sibsankar Kundu JSM, Dan C Sorensen, and George N Phillips, Jr: Dynamics of proteins in crystals: comparison of experiment with simple models. biophysical 2002, 82(2):p723-732. 31. Liu L KL, Gronenborn AM, Bahar I.: A comparative analysis of the equilibrium dynamics of a designed protein inferred from NMR, X-ray, and computations. Proteins 2009, 77(4):p927-939. 32. Dengming Ming YK, Maxime A. Lambert, Zhong Huang, Jianpeng Ma: How to Describe Protein Motion without Amino Acid Sequence and Atomic CoordinatesHow to Describe Protein Motion without Amino Acid Sequence and Atomic Coordinates. PNAS 2002, 99:pp. 8620-8625. 33. Toby W. Allen OSA, and Benoit Roux: On the importance of atomic fluctuations, protein flexibility, and solvent in ion permeation. JOURNAL OF GENERAL PHYSIOLOGY 2004, 124:p679-690. 34. Bertil Hille CMAaRM: Ion channels: from idea to reality. nature medicine 1999, 5(p1105-1109). 35. Karplus PA, Schulz GE: Prediction of chain flexibility in proteins: a tool for the selection of peptide antigens. Naturwissenschaften 1985, 72:212-213. 36. Vihinen M: Relationship of protein flexibility to thermostability. Protein Eng 1987, 1(6):477-480. 37. Vihinen M, Torkkila E, Riikonen P: Accuracy of protein flexibility predictions. Proteins 1994, 19(2):141-149. 38. Parthasarathy S, Murthy MR: Protein thermal stability: insights from atomic displacement parameters (B values). Protein Eng 2000, 13(1):9-13. 39. Parthasarathy S, Murthy MR: Analysis of temperature factor distribution in high-resolution protein structures. Protein Sci 1997, 6(12):2561-2567. 40. Wampler JE: Distribution Analysis of the Variation of B-Factors of X-ray Crystal Structures: Temperature and Structural Variations in Lysozyme. J Chem Inf Comput Sci 1997, 37(6):p1171-1180. 41. Smith DK, Radivojac P, Obradovic Z, Dunker AK, Zhu G: Improved amino acid flexibility parameters. Protein Sci 2003, 12(5):1060-1072. 42. Radivojac P, Obradovic Z, Smith DK, Zhu G, Vucetic S, Brown CJ, Lawson JD, Dunker AK: Protein flexibility and intrinsic disorder. Protein Sci 2004, 13(1):71-80. 43. Schlessinger A, Yachdav G, Rost B: PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 2006, 22(7):891-893. 44. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997, 25(17):3389-3402. 45. Yuan Z, Bailey TL, Teasdale RD: Prediction of protein B-factor profiles. Proteins 2005, 58(4):905-912. 46. Carugo O AP: Protein-protein crystal-packing contacts. Protein Science 1997, 6(10):p2261-2263. 47. Schlessinger A, Rost B: Protein flexibility and rigidity predicted from sequence. Proteins 2005, 61(1):115-126. 48. Shih CH, Huang SW, Yen SC, Lai YL, Yu SH, Hwang JK: A simple way to compute protein dynamics without a mechanical model. Proteins 2007, 68(1):34-38. 49. Carugo O: Correlation between occupancy and B factor of water molecules in protein crystal structures. Peds 1999, 12(12):p1021-1024. 50. Ou Y-Y: QuickRBF: an efficient RBFN package. http://wwwcsientuedutw/~yien/quickrbf. 51. Yen-Jen Oyang S-CH, Yu-Yen Ou, Chien-Yu Chen, Zhi-Wei Chen: A Novel Learing Algorithm for Data Classfication with Radial Basis Function Networks. In: 9th international conference on neural information processing: 2002; Singapore; 2002. 52. Yu-Yen Ou C-YC, Yen-Jen Oyang: A Novel Radial Basis Function Network Classifier with Centers Set by Hierarchical Clustering. In: IJCNN. 2005. 53. Yen-Jen Oyang S-CH, Yu-Yen Ou, Chien-Yu Chen, Zhi-Wei Chen: Data Classification with the Radial Basis Function Network Based on a Novel Kernel Density Estimation Algorithm. IEEE Transactions on Neural Networks 2005:p225-236. 54. Ou Y-Y: A study on machine learning with radial basis function networks. Ph. D. Taipei: National Taiwan University; 2005. 55. William H.; Saul A. Teukolsky WTV, Brian P. Flannery Numerical Recipes in C: The Art of Scientific Computing, second edition edn: Cambridge University Press; 1992. 56. Lin C-CCaC-J: LIBSVM : a library for support vector machines. Software available at http://wwwcsientuedutw/~cjlin/libsvm 2001. 57. Bishop CM: pattern recognition and machine learning: springer; 2006. 58. Bishop CM: neural networks for pattern recognition: oxford; 1995. 59. Hill R: elementary linear algebra with applications: saunders college 1996. 60. Y Hwang SB: An efficient method to construct a radial basis function neural network classifier. nerual networks 1997, 10(8):p1495-1503. 61. CY Chen SH, and YJ Oyang: an incremetal hierarchical data clustering algorithm nased on gravity theory. In: PAKDD: 2002; 2002: p237-250. 62. A.K. Jain RCD: algorithm for clustering data: pretice hall international; 1988. 63. A. K. Jain MNM, P. J. Flynn: data clustering: a review. ACM computing surveys 1999, 31(3):p137-142. 64. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z: Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 2006, 7:208. 65. Su CT, Chen CY, Ou YY: Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics 2006, 7:319. 66. M.J. Betts, Russell RB: Amino acid properties and consequences of subsitutions. In: Bioinformatics for Geneticists. Edited by M.R. Barnes ICG: Wiley; 2003. 67. Craig D. Livingstone, Barton GJ: Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation CABIOS 1993, 9(6):p745-756. 68. Jones TD: Protein secondary structure prediction based on position specific matrices. J Mol Biol 1999, 292:195-202. 69. Adamczak R PA, Meller J.: Accurate prediction of solvent accessibility using neural networks-based regression. Proteins 2004, 56:p753-767. 70. Nguyen MN RJ: Two-stage support vector regression approach for predicting accessible surface areas of amino acids. Proteins 2006, 63:p542-550. 71. Ahmad S SA: PSSM-based prediction of DNA binding sites in proteins. BMC Bioinformatics 2005, 6(33). 72. Mridul K. Kalita UKN, Ansuman Pattnaik, Anandhan Sivalingam, Gowthaman Ramasamy, Manish Kumar, Gajendra P. S. Raghava, Dinesh Gupta: CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences. PLos ONE 2008, 3(7):e2605. 73. Igor V. Grigoriev CZaS-HK: Sequence-based detection of distantly related proteins with the same fold Peds 2001, 14(7):p455-458. 74. UniProtKB/Swiss-Prot [http://au.expasy.org/sprot/] 75. PIR: Protein information resource [http://pir.georgetown.edu/pirwww/index.shtml] 76. Dunker AK LJ, Brown CJ, Williams RM, Romero P, Oh JS, Oldfield CJ, Campen AM, Ratliff CM, Hipps KW, Ausio J, Nissen MS, Reeves R, Kang C, Kissinger CR, Bailey RW, Griswold MD, Chiu W, Garner EC, Obradovic Z.: Intrinsically disordered protein. J Mol Graph Model 2001, 19(1):p26-59. 77. Wright HJDaPE: Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 2005, 6:p197-208. 78. Jones DT WJ: Prediction of disordered regions in proteins from position specific score matrices. proteins 2003, 53(suppl 6):p573-578. 79. Xavier I Ambroggioa, Kuhlman B: Design of protein conformational switches Current Opinion in Structural Biology 2006, 16(4):p525-530. 80. Mikael Bodén 1, Bailey TL: Identifying sequence regions undergoing conformational change via predicted continuum secondary structure Bioinformatics 2006, 22(15):p1809-1814. 81. Kuznetsov IB: Ordered conformational change in the protein backbone: Prediction of conformationally variable positions from sequence and low-resolution structural data. Proteins 2008, 72(1):p74-87. 82. M Gerstein, Krebs W: A database of macromolecular motions. NAR 1998, 26:p4280-4290. 83. Claus A. F. Andersen, Arthur G. Palmer, Søren Brunak, Rost B: Continuum Secondary Structure Captures Protein Flexibility Structure 2002, 10(2):p175-184. 84. Sander WKaC: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22(12):p2577-2637. 85. Hua Zhang, Tuo Zhang, Ke Chen, Shiyi Shen, Jishou Ruan, Kurgan L: On the relation between residue flexibility and local solvent accessibility in proteins. Proteins 2009, 76(3):p617-636. 86. Chothia C: The nature of the accessible and buried surfaces in proteins. JMB 1976, 105(1):p1-14. 87. Adam Zemla, Eslovas Venclovas, Krzysztof Fidelis, Rost B: A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins 1999, 34(2):p220-223. 88. J. Cheng, A. Randall, M. Sweredoski, Baldi P: SCRATCH: a Protein Structure and Structural Feature Prediction Server. NAR 2005, 33(web server):w72-76. 89. Zheng Yuan TLB, and Rohan D. Teasdale: Prediction of protein B-factor profiles. Proteins : structure, function, and bioinformatics 2005, 58(4):905-912. 90. Avner Schlessinger BR: Protein flexibility and rigidity predicted from sequence. Proteins : structure, function, and bioinformatics 2005, 61(1):115-126. 91. David K. Smith PR, Zoran Obradovic, A. Keith Dunker and Guang Zhu: improved amino acid flexibility parameters. Protein Science 2003, 12:1060-1072. 92. Adam Zemla CV, Krzysztof Fidelis, and Burkhard Rost: A Modified Definition of Sov, a Segment-Based Measure for Protein Secondary Structure Prediction Assessment. Proteins : Structure, Function, and Genetics 1999, 34:220-223. 93. Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK: Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins 2005, 61 Suppl 7:176-182. 94. Lise S JD: Sequence patterns associated with disordered regions in proteins. proteins 2005, 58(1):p144-150. 95. Klaus Scheffzek MRA, Wolfgang Kabsch, Lisa Wiesmüller, Alfred Lautwein, Frank Schmitz, Alfred Wittinghofer: The Ras-RasGAP Complex Structural Basis for GTPase Activation and Its Loss in Oncogenic Ras Mutants. Science 1997, 277:333-338. 96. Vermersch PS, Tesmer JJ, Lemon DD, FA Q: A Pro to Gly mutation in the hinge of the arabinose-binding protein enhances binding and alters specificity. Sugar-binding and crystallographic studies. J Biol Chem 1990, 265(27):p16592-16603. 97. Irene Luque, Freire E: Structural stability of binding sites: Consequences for binding affinity and allosteric effects. Proteins 2000, 41(S4):p63-71. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45244 | - |
| dc.description.abstract | 本論文完成了新型以蛋白質序列為基礎之雙模型蛋白質B值預測器。蛋白質結構與蛋白質功能息息相關，蛋白質結構的可變性決定蛋白質功能的類別。因此，從未知結構的蛋白質序列中或者改變蛋白質序列排列組合，蛋白質序列的可變性預測在蛋白質工程上是需要被得到的資訊。佐以其他以蛋白質序列為基礎之蛋白質體特性預測器，未知結構與功能之蛋白質將可被進一步了解，節省了解蛋白質結構與功能的煩瑣工程或合成特定功能的蛋白質所需要的實驗時間。
本論文所提出之新型蛋白質序列可變性預測器，以本研究團隊所研發之QuickRBFR迴歸法為核心，蛋白質的可變性以蛋白質B值為定義依據，蛋白質B值由X光晶體繞射資料取得，從原子電子雲的分佈情形，可估計出原子的在固態環境中的熱能變動情形，進而推測原子在液態環境中的可動性。 相較於其他兩種蛋白質B值預測器，本論文所完成之雙模型蛋白質序列B值預測器，不但在B值預測值與觀察值的相關系數上，表現優於其他兩種蛋白質B值預測器，在B值預估值與觀察值的平均絕對差異上，更改進了過去在高觀察B值與低觀察B值預測不準確的問題，使得本預測器在可變性分類的問題上也獲得了較好的敏感度(Sensitivity)與準確度(F-measure)。同時，本論文提出了蛋白質可變性梯度特徵向量，成功提升了雙模型蛋白質序列B值預測器的預測效能。 蛋白質的可變性可以蛋白質變動的振幅與速率分類，是以蛋白質的可變性定義多形，但其代表的蛋白質運動物理意義不同，結合多種蛋白質序列可變性預測器，可獲得更多的蛋白質可變性資訊，進而可預測蛋白質運動傾向。 關鍵字 : 蛋白質可變性、蛋白質B值、雙模型、蛋白質可變性梯度 | zh_TW |
| dc.description.abstract | A novel dual model sequence based protein B-factors predictor has been achieved in this thesis. To perform functions proteins would change their conformations or be triggered by binding ligand or molecular. Therefore, some protein regions should be flexible (adaptive) to adapt interaction with other molecular and some residues should be rigid to accept molecular single. Predicting protein functions from protein sequence could help us to understand functions of proteins, which would be synthesized to possess certain functions or would not be solved structures by tedious experiments. Protein B-factors, generated from X-ray crystallography, are used to be the protein flexibility index in this study. Protein B-factors reflect the thermal-dynamics of protein solid state and then used to presume the movability of proteins in solution.
In this study, the dual model sequence based protein B-factors predictor perform a superior performance in correlation coefficient 0.5283, generated from independent test, which compare with other two works. The correlation coefficient measures between experimental B-factors and predicted B-factors, comparing with two other works. Moreover, the fitness of prediction is improved in high B-factor regions and low B-factor regions. In this study, the protein flexibility gradient feature is conducted to improve the prediction performance. There are many different protein sequence based flexibility predictors on variant protein flexibility definitions. Combining information of different protein flexibility predictors may help us to presume protein dynamics. Key works : Protein flexibility, B-factors, Dual model, The flexibility gradient feature | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T04:10:31Z (GMT). No. of bitstreams: 1 ntu-99-F90548051-1.pdf: 2158193 bytes, checksum: 15f78f23c1de8b5db63ab4b72327fed3 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | TABLES LIST IV
FIGURES LIST V ABSTRACT 2 CHAPTER 1 BACKGROUND AND RELATED WORKS 3 SECTION 1.1 BACKGROUND 3 SECTION 1.2 WHAT ARE THE STATE-OF-ART ON PROTEIN STRUCTURE REPRESENTATION METHODS NOWADAYS, AND B-FACTORS? 4 SECTION 1.3 WHY PREDICT B-FACTOR FROM AMINO ACID SEQUENCE? 6 SECTION 1.4 NOT ONLY PROTEIN FLEXIBLE REGIONS ARE RELATED TO FUNCTIONAL SITES BUT RIGID REGIONS ARE ALSO. 7 SECTION 1.5 RELATED WORKS 8 SECTION 1.5.1 REPRESENTING AMINO ACID B-FACTORS DISTRIBUTION BY STATISTIC MODEL 8 SECTION 1.5.2 MACHINE LEARNING APPROACH 10 SECTION 1.6 CHALLENGES OF B-FACTOR PREDICTION FROM AMINO ACID SEQUENCE 11 SECTION 1.7 GEOMETRY RELATIONSHIP 11 CHAPTER 2 THE QUICKRBF REGRESSION METHOD FUNDAMENTAL 14 SECTION 2.1 THE MATHEMATIC BASIC OF RADIAL BASIS FUNCTION NETWORK (RBFN) 14 SECTION 2.2 THE FUNDAMENTAL OF RADIAL BASIS FUNCTION NETWORK 14 SECTION 2.3 THE MATHEMATICAL BASIC OF QUICKRBF FOR REGRESSION 15 SECTION 2.4 THE PARAMETERS ESTIMATION WITH THE LEAST MEAN SQUARE METHOD 17 SECTION 2.5 THE PARAMETER SPACE SEARCH 19 SECTION 2.6 DETERMINING THE CENTERS 20 SECTION 2.7 CALCULATION OF THE WEIGHTS 20 SECTION 2.8 SUMMARIZATION 21 CHAPTER 3 EXPERIMENTAL DATA AND B-FACTORS CHARACTERISTIC ANALYSIS 23 SECTION 3.1 EXPERIMENT DATASET 23 SECTION 3.2 DATA INVESTIGATE AND FEATURES 24 SECTION 3.3 SEQUENCE INFORMATION 25 SECTION 3.3.1 AMINO ACID COMPOSITIONS 25 SECTION 3.3.2 PHYSICOCHEMICAL PROPERTIES 26 SECTION 3.3.3 EVOLUTIONARY INFORMATION 28 SECTION 3.4 MOBILITY GRADIENT STATICS ― AMINO ACID PAIR B-FACTORS DIFFERENCES 30 SECTION 3.5 SECONDARY STRUCTURE INFORMATION 32 SECTION 3.5.1 BASIC STATISTIC OF B-FACTOR OCCURRENCE IN THREE SECONDARY STRUCTURE CLASSES 32 SECTION 3.5.2 B-FACTORS IN SECONDARY STRUCTURE TERMINAL POSITIONS 35 SECTION 3.6 SURFACE AREA OF SOLVENT ACCESSIBILITY OF AMINO ACIDS (SASA) 37 CHAPTER 4 DESIGN OF SEQUENCE-BASED PROTEIN B-FACTORS PREDICTOR 39 SECTION 4.1 OVERVIEW OF THIS SECTION 39 SECTION 4.2 METRIC 39 SECTION 4.3 SYSTEM PARAMETERS TUNING AND FEATURE SELECTIONS 41 SECTION 4.4 REGRESSION RESULTS OF FEATURES COMBINATIONS 44 SECTION 4.5 FIVE FOLD CROSS VALIDATION RESULTS 45 SECTION 4.5.1 REGRESSION RESULTS OF FIVE FOLD CROSS VALIDATION 45 SECTION 4.5.2 CLASSIFICATION RESULTS OF FIVE FOLD CROSS VALIDATION 47 SECTION 4.6 DEVELOPMENT OF DUAL MODEL AND EXPERIMENT RESULTS 49 SECTION 4.6.1 REGRESSION RESULTS OF DUAL MODEL INDEPENDENT TEST 52 SECTION 4.6.2 CLASSIFICATION RESULTS OF DUAL MODEL INDEPENDENT TEST 54 SECTION 4.7 CASE STUDY 55 SECTION 4.7.1 RAS•GTPASE ACTIVE SITES 55 SECTION 4.7.2 ABP SUGAR BINDING PROTEIN ACTIVE SITES 57 SECTION 4.8 BISEER: B-FACTOR PREDICTION WEB SERVER 60 SECTION 4.8.1 SYSTEM WORK FLOWCHART AND TUTORIAL 60 CHAPTER 5 DISCUSSION AND CONCLUSION 63 | |
| dc.language.iso | en | |
| dc.subject | 蛋白質可變性梯度 | zh_TW |
| dc.subject | 蛋白質可變性 | zh_TW |
| dc.subject | 蛋白質B值 | zh_TW |
| dc.subject | 雙模型 | zh_TW |
| dc.subject | Dual model | en |
| dc.subject | Flexibility gradient feature | en |
| dc.subject | B-factors | en |
| dc.subject | protein flexibility | en |
| dc.title | 以序列為基礎之蛋白質B值預測之研究 | zh_TW |
| dc.title | A Study on Sequence Based Prediction of Protein B-factors | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳倩瑜,黃乾綱,張天豪,蔣以仁 | |
| dc.subject.keyword | 蛋白質可變性,蛋白質B值,雙模型,蛋白質可變性梯度, | zh_TW |
| dc.subject.keyword | protein flexibility,B-factors,Dual model,Flexibility gradient feature, | en |
| dc.relation.page | 71 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-01-29 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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