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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林榮信(Jung-Hsin Lin) | |
| dc.contributor.author | Yu-Hsuan Chen | en |
| dc.contributor.author | 陳侑宣 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:46:03Z | - |
| dc.date.available | 2020-03-12 | |
| dc.date.copyright | 2015-03-12 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-02-03 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55066 | - |
| dc.description.abstract | 原則上來說，不同的配體 (ligand) 所誘發的蛋白質動力學特性應可加以區別並可反映其配體的功能 (functional type)。然而，A型G蛋白耦合受體 (G protein-coupled receptor, GPCRs) 所具有的構型異質性 (conformational heterogeneity，即結合不同功能配體的蛋白質具有相似的分子構型)，使得探究不同配體對於蛋白質動力學特性之影響成為一極富挑戰的工作。 本論文中，我們運用分子動力學模擬 (molecular dynamics, MD) 技術，以中間態 (intermediate state) 構型為起點，探討三種促效劑 (agonist) 、三種拮抗劑 (antagonist) 與腺苷酸受體IIA亞型 (A2A adenosine receptor, A2AAR) 結合後、以及沒有任何配體存在下腺苷酸受體IIA亞型的構形變化。所產生的大量分子構型進一步運用馬可夫模型 (Markov state model, MSM) 分析，首先將幾何上的相似的分子構型組合成許多微觀態 (microstate)，接著將動力性質相似的微觀態合併為宏觀態 (macrostate)。這些宏觀態呈現出A型G蛋白耦合受體所具有的構型異質性，此外，我們意外地發現某些宏觀態幾乎是由與特定類型配體結合的構型所組成。這些相對同質的宏觀態清楚地刻畫了不同功能配體對於腺苷酸受體IIA亞型所造成的典型影響，使其所誘發的蛋白質動力學特性得以區別。利用這些宏觀態，我們建立了功能分析計算模式可用以區別腺苷酸受體IIA亞型的促效劑與拮抗劑。此模式可進一步推廣至A型G蛋白耦合受體，應用於藥物發展過程中快速地預測化合物對於受體之作用。 | zh_TW |
| dc.description.abstract | In principle, the differential dynamics of a protein perturbed by various ligands should be able to reflect ligands’ different functions. However, in the field of G protein-coupled receptor (GPCR), the phenomenon of conformational heterogeneity, i.e., the sharing of conformations traversed by differently liganded receptors, poses a challenge for delineating ligand’s action on perturbing protein dynamics. In this work, we conduct multiple molecular dynamics (MD) simulations of the agonists- and antagonists-bound human A2A adenosine receptor (A2AAR) starting from an intermediate state structure to maximize the sensitivity of ligand-perturbed dynamics. Conformational heterogeneity can be visualized directly by the Markov state model (MSM) analysis, which is a two-stage procedure first by performing clustering based on conformational similarity to form microstates, and then kinetic lumping based on state inter-convertibility to aggregate microstates into macrostates. To our surprise, some macrostates exhibit significantly less conformational heterogeneity, which are designated as the agonist-enriched, the apo-enriched, and the antagonist-enriched macrostates, respectively, according to ligands’ function types in these macrostates. Analyses on these purer macrostates show that the ensembles of these less-mixed states clearly characterize the “classical” dynamical features of A2AAR in different ligand-bound states. With the constructed macrostates we are able to propose general schemes to predict the functional types of ligands acting on GPCRs. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:46:03Z (GMT). No. of bitstreams: 1 ntu-104-F96423001-1.pdf: 4860917 bytes, checksum: 665801b329214fd2adb846b82e2651cd (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 中文摘要 i Abstract ii Table of Contents iii List of Figures v List of Tables vii Chapter 1: Introduction 1 1.1 G protein-coupled receptor (GPCR) 1 1.1.1 Classification of GPCRs 1 1.1.2 Architectures of GPCRs 2 1.1.3 Ballesteros-Weinstein nomenclature in GPCRs 7 1.1.4 Activation features of GPCRs 9 1.1.5 Dynamics of GPCRs 15 1.2 Adenosine receptor 21 1.2.1 Classification of adenosine receptor 21 1.2.2 Therapeutic significance of adenosine receptor 21 1.2.3 Crystal structures of A2A adenosine receptor 24 1.3 Specific Aims 29 Chapter 2: Material and Methods 30 2.1 Structural refinement and system preparation 30 2.2 Computational details of molecular dynamics (MD) simulation 34 2.3 Markov state model (MSM) construction 35 2.4 Analyzing methods and tools 43 2.4.1 Distance analysis 43 2.4.2 Atomic fluctuation analysis 44 2.4.3 Cross-correlation calculation 44 2.4.4 Principal component analysis (PCA) 45 2.4.5 Helix geometry analysis 45 2.4.6 Hydrogen bond and decision tree analysis 45 2.4.7 Graphic softwares 46 Chapter 3: Results and Discussions 47 3.1 Markov state models analysis 47 3.2 Structural analyses on the coarse-grained Markov states 55 3.3 Dynamics analyses on the coarse-grained Markov states 60 3.4 Residue in contact with ligands 72 3.5 Principal component analysis on molecular dynamics trajectories 75 3.6 Helix geometry analysis on molecular dynamics trajectories 79 3.7 Hydrogen bond analysis on the microstates 81 3.8 Prediction of ligand functional type 85 3.8.1 Based on hydrogen bond fingerprint and Decision tree 85 3.8.2 Based on dynamically discriminable states 87 Chapter 4: Concluding Remarks 90 References 91 | |
| dc.language.iso | en | |
| dc.title | A型G蛋白耦合受體之分子動力學模擬與其配體之功能預測 | zh_TW |
| dc.title | Molecular Dynamics Simulations of a Class A G Protein-Coupled Receptor and the Functional Type Prediction for Its Ligands | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 方俊民,陳儀莊,許世宜,孫英傑 | |
| dc.subject.keyword | G蛋白耦合受體,腺?酸受體IIA亞型,分子動力學模擬,馬可夫模型,構型異質性, | zh_TW |
| dc.subject.keyword | G protein-coupled receptor,A2A adenosine receptor,molecular dynamics simulation,Markov state model,conformational heterogeneity, | en |
| dc.relation.page | 106 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-02-03 | |
| dc.contributor.author-college | 藥學專業學院 | zh_TW |
| dc.contributor.author-dept | 藥學研究所 | zh_TW |
| Appears in Collections: | 藥學系 | |
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