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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 邱繼輝(Kay-Hooi Khoo) | |
| dc.contributor.author | Yu-Chun Chien | en |
| dc.contributor.author | 簡瑜君 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:33:10Z | - |
| dc.date.available | 2027-02-09 | |
| dc.date.copyright | 2022-02-18 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-02-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81960 | - |
| dc.description.abstract | 以質譜分析蛋白特異性位點的醣基化，目前仍有許多尚待克服的技術面困難點。就N型醣蛋白而言，最常遇到的問題是軟體錯誤鑑定；O型醣蛋白則是難以精準定位；而多重N型及O型醣蛋白更是缺乏一個定點精準分析的流程，因此本研究致力於研發多重醣基化胜肽的質譜分析方法，並著重於數據深度分析，以利未來有效應用於醣蛋白質體學。 近年來，由新型冠狀病毒（SARS-CoV-2）引起的嚴重特殊傳染性肺炎（COVID-19）擴散於世界各地，目前已知新型冠狀病毒的棘蛋白結構、抗原性、功能等會受特異性醣基化影響；而貓冠狀病毒中的貓傳染性腹膜炎病毒 (Feline infectious peritonitis virus；FIPV)也會引起致死性貓傳染性腹膜炎 (FIP)。先前已利用軟體 (Byonic)分析過新型冠狀病毒及貓傳染性腹膜炎病毒的棘蛋白所產生的醣胜肽，為了進一步了解特異性位點的醣基化，本研究導入不同原理的分析軟體 (pGlyco3)以建立更值得信賴的定性及定量流程。藉由門檻設定篩選後，兩種軟體皆鑑定到的醣胜肽最為可信，由其中一種軟體鑑定到的醣胜肽次之，而利用滯留時間分組可更進一步校正醣的異構物以降低偽陽性的鑑定結果；另外，大部分的定量分析結果一致，但微量的醣胜肽在兩種分析軟體並不完全一致，則需進一步驗證。 先前已知蛋白酪氨酸磷酸酶A型受體 (Receptor-like protein-tyrosine phosphatase alpha；PTPRA) 的膜外區域只有122個胺基酸，卻是個高度N型醣基化的蛋白。缺乏PTPRA的成纖維細胞會減少其訊號傳導、細胞遷移及轉移的能力，但此能力是否受醣基化影響，且其N型特異性位點及是否有高度O型醣基化位點仍未知。為進一步了解PTPRA的特異性醣位點，在此利用PTPRA-Fc融合蛋白建立多重N型及O型醣蛋白的分析方法。首先，利用去N型及去O型醣蛋白來評估不同軟體對於多重N型及O型醣胜肽的分析能力。從去O型醣蛋白的醣胜肽分析結果發現Byonic與 pGlyco3 分別鑑定到的非重複醣胜肽總數目相近，但 Byonic 可提供較多的匹配結果，且只有Byonic可以鑑定到N型醣胜肽的N型共有序列上的O型醣基化修飾。相較於Byonic， pGlyco3及O-pair這兩種數據分析軟體則可以鑑定到更多源自去N型醣蛋白的O型醣胜肽，結果顯示突變的去N型醣蛋白有更多的O型醣基化修飾，而以同樣的樣品處理方式製備PTPRA-Fc，只能鑑定到少數的醣胜肽片段。因此，接下來以不同蛋白酶降低醣胜肽的長度及複雜度。在胰蛋白酶及絲氨酸蛋白酶的雙作用下，總共鑑定到四個N型及八個O型特異性醣基化位點，但只有其中一個O型位點具有唾液酸修飾；以胰蛋白酶及O型蛋白酶 (OpeRATOR) 雙作用則能找到更多唾液酸修飾的N型醣基化修飾並額外找到19個O型醣基化位點，且得以驗證O型蛋白酶的作用位點傾向在帶有core 1的O型醣基化絲氨酸及蘇氨酸。此外，分析結果也顯示醣胜肽的滯留時間會受到O型醣基化的數量影響。 為了減少偽陰性鑑定，本研究進一步開發一Fishing策略，利用相同胜肽序列會有相近的滯留時間特性，由可信的醣胜肽為基準，找出未鑑定到的醣異構物修飾。由此方法，可找到更多唾液酸化的 PTPRA醣胜肽，雖然因缺乏證據性的斷片離子，無法得知特異性位點，但可藉由滯留時間的特性，推斷出有幾個O型醣基化位點。最後，將成功建立的分析流程應用於全長的 PTPRA，總共可鑑定到兩段不同的胜肽序列，並由酵素的特性推斷出此全長的醣蛋白相較於融合蛋白可能有更多Tn、core 2或唾液酸化 core 1的O型醣基化修飾。 總結而言，由於目前內生性膜蛋白的醣基化修飾分析仍有技術上的高度困難，可藉由蛋白如PTPRA-Fc先建立分析方法，並評估各醣蛋白數據分析軟體的優勢及限制，以利往後更有效分析目前不易鑑定到的多重N型及O型醣胜肽。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:33:10Z (GMT). No. of bitstreams: 1 U0001-0802202214451900.pdf: 9810975 bytes, checksum: bc24e63c720990d1a6de1392e524117e (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 謝誌 I 摘要 II Abstract IV Chapter 1 Introduction 1 1.1 Protein N- and O-glycosylation 1 1.1.1 Site-specific N-glycosylation 3 1.1.2 Heavily O-glycosylated mucin domain 4 1.1.3 Heavily O-glycosylated and N-glycosylated domain structures 6 1.2 General glycopeptide analysis 8 1.2.1 LC-MS/MS for N- and O-glycopeptides 10 1.2.2 Software for N- and O-glycopeptides 13 1.3 Limitation of the glycopeptide analysis 17 1.3.1 N-glycopeptide analysis 17 1.3.2 O- glycopeptide analysis 19 1.3.3 Analysis of N- and O- glycopeptide 21 1.4 Specific Aims 22 Chapter 2 Material and Methods 25 2.1 Plasmid construction and protein purification 25 2.2 Proteolytic digestion 25 2.3 TMT0 labeling 25 2.4 Glycan analysis 26 2.5 Glycopeptide identification 26 2.6 Data analysis 26 Chapter 3 Results 29 3.1 To reduce false-positive assignments of the identified glycopeptides by dual software 29 3.1.1 Establishing an automated N-glycopeptide data analysis workflow 29 3.1.2 The advantage of the combined workflow 36 3.2 To characterize the N-glycopeptide with multiple O-glycans 43 3.2.1 De-N- or de-O-glycosylated glycopeptide analysis 46 3.2.1.1 Analysis of de-O-glycosylated N-glycopeptides 47 3.2.1.2 Analysis of de-N-glycosylated O-glycopeptides 50 3.2.2 N- and O-glycopeptide analysis 56 3.2.3 Characterization of the elution time of N-glycopeptides with multiple O-glycans on the C18 column 62 3.2.3.1 N-glycopeptide with an O-glycan 62 3.2.3.2 Multiple O-glycan on the same peptide 64 3.2.3.3 N-glycopeptide with multiple O-glycans 67 3.2.4 Glycopeptide analysis with TMT labeling 68 3.3 To develop a fishing strategy to reduce false-negative results 71 3.3.1 Workflow of fishing strategy 71 3.3.2 Application to PTPRAFc 73 3.3.3 Application to PTPRAFL 78 3.3.3.1 Two bands of PTPRAFL-YFP by in gel digestions 78 3.3.3.2 PTPRAFL-TST compared to PTPRAFL-YFP and PTPRAFC by in solution digestions 81 3.3.3.3 PTPRAFL-TST by in gel digestions 83 Chapter 4 Discussion and Perspectives 88 4.1 Dual search strategy helps to identify reliable N-glycopeptides 88 4.1.1 Identification of N-glycopeptides 88 4.1.2 Quantification of N-glycopeptides 88 4.2 Characterization of the N- and multiple O-glycosylated peptides by LC-MS/MS 90 4.2.1 Identification of N- and multiple O-glycosylated peptides 90 4.2.2 Various software for N- and multiple O-glycosylated peptides 92 4.2.3 Combinations of proteases for N- and multiple O-glycosylated peptides 95 4.2.4 Elution orders of the N- and multiple O-glycosylated peptides 96 4.3 Fishing strategy with the aid of retention time 97 4.3.1 Verification of fishing strategy by application to PTPRAFc 97 4.3.2 Application of fishing strategy to PTPRAFL 99 4.4 Current and future perspectives 100 4.4.1 Biochemical aspects 100 4.4.2 Analytical aspects 103 References 105 | |
| dc.language.iso | en | |
| dc.subject | 醣胜肽 | zh_TW |
| dc.subject | glycopeptide | en |
| dc.title | 開發質譜分析流程以探討多重N型及O型醣蛋白定點精準分析技術 | zh_TW |
| dc.title | Development and applications of advanced mass spectrometry-based analytical workflows for multiple N- and O-glycosylated glycoproteins | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0002-2485-2932 | |
| dc.contributor.oralexamcommittee | 孟子青(Da-Ming Wang),安形高志(Jeffrey Chi-Sheng Wu),徐尚德(Chii-Dong Chen),陳玉如(Wen-Zern Hwang) | |
| dc.subject.keyword | 醣胜肽, | zh_TW |
| dc.subject.keyword | glycopeptide, | en |
| dc.relation.page | 112 | |
| dc.identifier.doi | 10.6342/NTU202200381 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-02-11 | |
| dc.contributor.author-college | 生命科學院 | zh_TW |
| dc.contributor.author-dept | 生化科學研究所 | zh_TW |
| dc.date.embargo-lift | 2027-02-09 | - |
| 顯示於系所單位： | 生化科學研究所 | |
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