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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77679完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾宇鳳 | |
| dc.contributor.author | Lu-Chi Liu | en |
| dc.contributor.author | 劉律琪 | zh_TW |
| dc.date.accessioned | 2021-07-10T22:15:27Z | - |
| dc.date.available | 2021-07-10T22:15:27Z | - |
| dc.date.copyright | 2017-09-04 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-18 | |
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Nature, 2016. 540(7633): p. 458-461. 32. Song, G., et al., Human GLP-1 receptor transmembrane domain structure in complex with allosteric modulators. Nature, 2017. 546(7657): p. 312-315. 33. Zhang, H., et al., Structural basis for selectivity and diversity in angiotensin II receptors. Nature, 2017. 544(7650): p. 327-332. 34. Isberg, V., et al., GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Research, 2016. 44(D1): p. D356-64. 35. Chang, R.L., et al., Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model. Plos Computational Biology, 2010. 6(9). 36. Sanner, M.F., Python: a programming language for software integration and development. J Mol Graph Model, 1999. 17(1): p. 57-61. 37. Morris, G.M., et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem, 2009. 30(16): p. 2785-91. 38. The PyMOL Molecular Graphics System. Schrödinger, LLC. 39. Pontius, J.U., L. Wagner, and G.D. Schuler, UniGene: A unified view of the transcriptome. In: The NCBI Handbook. 2004: National Center for Biotechnology Information. 40. Boguski, M.S., T.M. Lowe, and C.M. Tolstoshev, dbEST--database for 'expressed sequence tags'. Nat Genet, 1993. 4(4): p. 332-3. 41. Kuhn, M., et al., The SIDER database of drugs and side effects. Nucleic Acids Research, 2016. 44(D1): p. D1075-9. 42. Brown, E.G., L. Wood, and S. Wood, The medical dictionary for regulatory activities (MedDRA). Drug Saf, 1999. 20(2): p. 109-17. 43. Huey, R. How to prepare a ligand file for AutoDock 4. 2007; Available from: http://autodock.scripps.edu/faqs-help/how-to/how-to-prepare-a-ligand-file-for-autodock4. 44. Li, H., et al., TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Research, 2006. 34(Web Server issue): p. W219-24. 45. Liu, X., et al., PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Research, 2010. 38(Web Server issue): p. W609-14. 46. Gao, Z., et al., PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics, 2008. 9: p. 104. 47. Trott, O. and A.J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 2010. 31(2): p. 455-61. 48. Upadhyaya, J., et al., Bitter taste receptor T2R1 is activated by dipeptides and tripeptides. Biochem Biophys Res Commun, 2010. 398(2): p. 331-5. 49. Singh, V. and P. Somvanshi, Homology modeling of adenosine A2A receptor and molecular docking for exploration of appropriate potent antagonists for treatment of Parkinson's disease. Curr Aging Sci, 2009. 2(2): p. 127-34. 50. Platania, C.B., et al., Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking evaluation. Plos One, 2012. 7(9): p. e44316. 51. Lipscomb, C.E., Medical Subject Headings (MeSH). Bull Med Libr Assoc, 2000. 88(3): p. 265-6. 52. Pergolide from DrugBank. Available from: https://www.drugbank.ca/drugs/DB01186 - targets. 53. Plerixafor from DrubBank. Available from: https://www.drugbank.ca/drugs/DB06809 - targets. 54. Plerixafor from PubChem. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/65015 - section=Top. 55. Chlorpromazine from DrugBank. Available from: https://www.drugbank.ca/drugs/DB00477 - BE0000756. 56. Chlorpromazine from PubChem. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/2726 - section=Top. 57. Andrejak, M. and C. Tribouilloy, Drug-induced valvular heart disease: an update. Arch Cardiovasc Dis, 2013. 106(5): p. 333-9. 58. Fredholm, B.B., Adenosine, an endogenous distress signal, modulates tissue damage and repair. Cell Death and Differentiation, 2007. 14(7): p. 1315-1323. 59. Burger, J.A. and A. Peled, CXCR4 antagonists: targeting the microenvironment in leukemia and other cancers. Leukemia, 2009. 23(1): p. 43-52. 60. Adverse effects of plerixafor. Available from: https://en.wikipedia.org/wiki/Plerixafor - Adverse_effects. 61. MeSH descriptor for headache. Available from: https://meshb.nlm.nih.gov/record/ui?ui=D006261. 62. MeSH descriptor for muscle pain. Available from: https://meshb.nlm.nih.gov/record/ui?ui=D063806. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77679 | - |
| dc.description.abstract | G蛋白偶聯受體(G Protein-Coupled Receptor)是一類最常被當作藥物靶點的蛋白質，然而因為其遍佈於人體各處，與諸多生理機能與疾病有關，故以他們為靶點的藥物開發非常普遍卻也困難，為了加速這類藥物的發展時程，本篇論文將透過預測GPCR藥物的多標靶(multiple targets)、老藥新用(repurposing)與潛在副作用來幫助這類藥物的開發。
為了準確的預測GPCR藥物的多標靶，我們從GPCR db資料庫中收錄了所有物種為人類的GPCR結晶結構，總共約40種，利用結構式篩選(structure-based screening)的方法將小分子藥物(ligand)與這些GPCR配對，將ligand嵌合(docking)於每個配對的receptor的結合位置(binding site)，模擬每一組ligand-receptor的結合作用(binding action)，將結果依據結合的親和力大小排序，依排序結果判斷該ligand會跟哪些GPCR產生交互作用，藉此預測GPCR藥物的多標靶。在驗證docking的準確度時，我們將資料集中的GPCR複合體(complex)分成ligand跟receptor兩個部分，將他們重新docking，並計算其與原先complex間的方均根偏移(rmsd)，最後和其他現有的系統比較以檢驗其準確度。 脫靶效應(off target effect)往往伴隨著副作用的發生，若開發藥物時能夠知道配體作用在身體哪些地方的話，就可以提前預想該藥物的有效性以及發生副作用的可能性，這是一個很有用但不存在於現有系統的功能，因此我們透過GPCR的表現序列標幟資料(EST Profile)整理出各GPCR於人體中45個部位的表現量，將這個資料集與前面的脫靶效應結果連結，便可以幫助使用者發現可能產生副作用的部位以及確認自己的藥物會不會在其預期的地方產生作用。除此之外，我們從SIDER資料庫中收集所有的副作用資料，將這些副作用與經由EST Profile整理出的45個人體部位透過醫學主題詞(medical subject heading)的分類進行配對，藉此更進一步的幫助使用者預測可能會產生哪些副作用。 除了脫靶效應這樣的負面情形以外，多標靶也可能帶來其他好處，因為和其他的target作用並不一定會發生副作用，相反地，這些target可能會產生不一樣的藥效，如此一來就能夠幫助已知藥物的再利用，進行較安全且更有效率的老藥新用開發。為了驗證系統是否能有效地完成上述功能，我們選擇因具有脫靶效應與心毒性而被下架的Pergolide、沒有發現脫靶效應但具有輕微的神經與肌肉副作用的Plerixafor、以及具有少許肝臟以及心臟副作用並且因多標靶而發現老藥新用的Chlorpromazine作為測試資料，相比於前人進行單一對的ligand-GPCR 的鏈結反應預測時所花的時間，本系統的執行時間遠小於前人們的系統，且可得到有效的結果。 | zh_TW |
| dc.description.abstract | The G protein-coupled receptor (GPCR) superfamily represents the largest known class of therapeutic targets. However, the fact shows that new GPCR drugs in the market start to appear at a decreasing rate due to the GPCRs’ universal in our body and involvements of lots of physiological and pathophysiological processes. To facilitate and help with advanced discovery of GPCR drug, a system aiming to predict GPCR multiple targets, repurposing and side effects of GPCR drugs will present in this thesis.
In this tool, a user only needs to upload the compound of interest to the server. A library of all experimental determined crystal structures of human GPCRs was built in to perform docking to all crystal structures in the system, then a rank list of all the GPCRs to the compound affinities between all GPCRs in our dataset and the user-defined compound was generated. With the ranking list and affinities, users can envisage which GPCRs would be the targets of their compound, whether possible unexpected GPCR targets existed that might contribute to potential side effects (off targets) or potentially a new use of the compound (repurposing). Off target concerns are usually referring to unexpected side effects. If one can have certain information as to the expression level of the enzyme (target) with specific tissues or organs that a drug will react to, then it can be used to estimate the location and potential severity of the side effects. This is a useful information but not present in any of the current tools. Therefore, we linked the predicted GPCR off targets and their expressed sequence tag (EST) profiles to offer their distribution patterns in 45 tissues and organs in human body to predict where the binding of the drug and GPCRs may take place. Moreover, 5,868 side effect data of marketed medicines from SIDER database were collected and mapped to the 45 body sites inferred from EST Profile to provide potential side effects locations. The mapping process was performed using Medical Subject Headings (MeSH) category, which classifies diseases and symptoms by tissues and organs. Besides giving clues of side effects, multiple targets of a ligand can sometimes provide additional information because off targets binding may be hints to new indications and therefore used for drug repurposing. To demonstrate the use of this system, pergolide, a drug withdrawn from the US and Canada market due to off target effects and risk of cardiac valvulopathy, plerixafor, a drug has no known off targets with very mild neurological and muscular side effects, and chlorpromazine, an off-target repurposing drug, were used as case studies. In the case of pergolide, top predicted off-targets did have heart and vascular distribution implying the unintended cardio toxicity. As for plerixafor, 37% of the GPCRs in our dataset were predicted to have zero affinity from it, but almost all top predicted off-targets were found to have distribution in muscle, indicating the cause of its muscular side effects. In addition, the off targets that was later discovered for repurposing of chlorpromazine were also identified. With this tool, GPCR Panel, the prediction of multiple targets, repurposing and side effects can be done by simply uploading a small ligand. This server is freely accessible at http://gpcrpanel.cmdm.tw/index.html. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T22:15:27Z (GMT). No. of bitstreams: 1 ntu-106-R04922017-1.pdf: 3385308 bytes, checksum: 07d3a29d483f6ddc4db162d891f4a2c8 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iv Abstract vi Contents viii List of Figures x List of Tables xi Chapter 1 Introduction 1 1.1 GPCR Drug Discovery 1 1.2 Binding Prediction of Receptor and Drug 2 1.3 GPCR Homology Modeling 3 1.4 Off Target Effects, Side Effects, and Drug Repurposing 3 1.5 Current GPCR Servers 4 Chapter 2 Materials and Methods 8 2.1 Materials 8 2.1.1 GPCR Dataset 8 2.1.2 GPCR Distribution Data 11 2.1.3 Side Effect Dataset 14 2.2 Methods 14 2.2.1 Input 15 2.2.2 Virtual Screening 15 2.2.3 Off Target Prediction 16 2.2.4 Body Site Distribution Prediction 17 2.2.5 Mapping of Side Effects and Body Sites 18 Chapter 3 Experiments 20 3.1 Validation of Docking 20 3.2 Pergolide as Input Ligand 21 3.3 Plerixafor as Input Ligand 22 3.4 Chlorpromazine as Input Ligand 23 Chapter 4 Results 23 4.1 Validation of Docking 23 4.2 Results of Inputting Pergolide 24 4.3 Results of Inputting Plerixafor 27 4.4 Results of Inputting Chlorpromazine 29 Chapter 5 Conclusion 32 Bibliography 34 Appendix 39 | |
| dc.language.iso | en | |
| dc.subject | 多效應 | zh_TW |
| dc.subject | 表現序列標幟 | zh_TW |
| dc.subject | 結構式篩選 | zh_TW |
| dc.subject | 體內分佈 | zh_TW |
| dc.subject | 副作用 | zh_TW |
| dc.subject | 老藥新用 | zh_TW |
| dc.subject | G蛋白偶聯受體 | zh_TW |
| dc.subject | GPCR | en |
| dc.subject | Multiple targets predictions | en |
| dc.subject | Side effect | en |
| dc.subject | Body site distribution | en |
| dc.subject | EST profile | en |
| dc.subject | Repurposing | en |
| dc.subject | Structure-based virtual screening | en |
| dc.title | G蛋白偶聯受體之多標靶、老藥新用與副作用預測 | zh_TW |
| dc.title | GPCR Panel Predictions for Multiple Targets, Repurposing and Side Effects | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 潘秀玲,李昂 | |
| dc.subject.keyword | G蛋白偶聯受體,多效應,副作用,體內分佈,表現序列標幟,老藥新用,結構式篩選, | zh_TW |
| dc.subject.keyword | GPCR,Multiple targets predictions,Side effect,Body site distribution,EST profile,Repurposing,Structure-based virtual screening, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU201703377 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-08-19 | |
| 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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