臺灣博碩士論文加值系統

使用AMBER 2003的力場和內含水的模型研究Trp-cage和protein G在全原子的動力學性質。複本交換的方法提升摺疊組態空間的採樣。摺疊模擬是由24個複本，溫度從276K到508K開始。對Trp-cage開始的結構是個完全延展開的結構而protein G是從自然狀態開始。分子模擬的組態系綜經由聚類方法OPTICS分析。結果顯示，對兩個蛋白質的摺疊組態空間是階層示的。對Trp-cage而言，代表中心聚類結構的平均結構與實驗的結構相較有1.2A的骨架方均根偏差，而protein G則是1.4 A。回歸樹分析對OPTICS產生的聚類序作的映射顯示，摺疊組態空間可以階層示的界定成四個形式：為摺疊型態，二節結構形成，三級結構形成，以及原生型態。三個特徵的因子支配Trp-cage的摺疊空間Pro12-Ψ雙面角, Leu2-Ψ雙面角和總能；protein G有四個因子：Lys4-Ψ，Thr18-Ψ， Glu42-Ψ和Asp22-Ψ.

The kinetics of the folding of the Trp-cage and protein G were studied in all-atomic molecular dynamics simulations using the AMBER 2003 force-field in implicit solvent. Replica exchange method (REM) was used to enhance sampling of folding conformational space. Folding simulations of twenty-four replicas of Trp-cage and protein G were run from extended state and native state, respectively, ranging from 276 K to 508 K. The conformational ensemble of molecular simulations was clustering by OPTICS. The results showed that the folding conformational spaces for both proteins are hierarchical. The average conformation representing centroid clustering structure for Trp-cage has a backbone root mean square deviation of 1.2 A relative to experimental structure, and 1.4 A for protein G. After regression tree analyze for mapping cluster ordering generated by OPTICS, the folding conformational space can demarcate four hierarchical regimes: unfolded state, formation of secondary structure, formation of tertiary structure, and native state. Three characterized factors for Trp-cage, Pro12-Ψ angle, Leu2-Ψ angle, and total energy, dominated folding space; and four factors for protein G: Lys4-Ψ, Thr18-Ψ, Glu42-Ψ, and Asp22-Ψ.

中文摘要 i
ABSTRACT ii
誌謝 iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
CHAPTER

1. PROTEIN STRUCTURE AND FOLDING 1
1.1 Introduction 1
1.2 Hierarchical Structure of Proteins 1
1.2.1 Primary Structure 1
1.2.2 Secondary Structure 2
1.2.3 Tertiary Structure and Quaternary Structure 4
2. METHODS AND MATERIALS 5
2.1 Modeling System 5
2.1.1 Molecular Dynamics Algorithm 5
2.1.2 Force Fields 6
2.1.2.1 Non-bonded Interactions 7
2.1.2.2 Bonded Interactions 8
2.1.3 Replica-exchange method 9
2.2 Machine Learning 12
2.2.1 Cluster Analysis 12
2.2.1.1 K-Means Clustering 12
2.2.1.2 Hierarchical Clustering 13
2.2.1.3 Density-based Clustering 15
2.2.2 Decision Tree 17
2.3 Supervised Learning after Cluster Analysis 20
2.4 Materials 21
2.4.1 Trp-cage 21
2.4.2 Protein G 23
3. RESULTS 25
3.1 Trp-Cage 25
3.1.1 Characteristic Structures 25
3.1.2 Feature Selection 28
3.2 Protein G 29
3.2.1 Characteristic Structures 29
3.2.2 Feature Selection 31
4. DISCUSSION 33
4.1 Trp-cage 33
4.2 Protein G 34
5. CONCULSIONS 36
APPENDICES

A. CLUSTERING CONFORMATIONAL SPACE OF TRP-CAGE FOR VARIOUS TEMPERATURES 37
B. CLUSTERING CONFORMATIONAL SPACE OF PROTEIN G FOR VARIOUS TEMPERATURES 39
BIBLIOGRAPHY 41

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