臺灣博碩士論文加值系統

虹彩病毒與神經壞死病毒是養殖漁蝦所面臨養殖物種死亡的最大威脅，即使目前有許多種抑制病毒的疫苗產品，然而由於仍缺乏對這兩種病毒及感染機制的完全理解，這些免疫方式並不是特別有效。虹彩病毒分為兩類:第一類Alphairidovirinae只能感染脊椎生物，包含Lymphocystivirus、Ranavirus (GIV)和Megalocystivirus (TGIV)；第二類Betairidovirinae主要感染無脊椎生物，包含Iridovirus和Chloriridovirus。神經壞死病毒依照感染宿主專一性可以分為三大類: 第一類Alphanodvirus主要感染昆蟲及植物；第二類Betanodavirus只能感染魚類；第三類是近年新定義的Gammanodavirus類別只能感染蝦類。我們認為這種宿主專一性特徵能提供預測線性抗原表位的重要依據。本論文提出一套基於投票機制的預測系統，結合五套知名的線性抗原表位預測工具，依預測及投票結果進行具有”保留”及”獨特”性的線性抗原表位分類，依宿主專一特徵所預測的線性抗原表位可以有效提升疫苗開發的效能及效率。經由預設參數所辨識的線性抗原表位片段，進一步透過預測3D結構的比對方式判斷該線性抗原表位是否座落於該病原結構的表面區域，經確認座落於表面區域的線性抗原可以提升後續生物實驗驗證的成功率。本論文使用ELISA生物實驗方式驗證系統預測的線性抗原表位的抗原反應特性，經實驗結果證明本系統所預測的線性抗原表位的確與石斑魚抗體有明顯的結合反應。除了直接使用生物實驗驗證方式，我們另外使用IEDB資料庫所收錄已經由實驗驗證的線性抗原表位資料，透過訓練機制建構一套智慧分類器模型，進一步強化辨識先前投票機制所預測的抗原表位是否被成功分類為具有線性抗原表位的重要特徵，可以協助生物學家或免疫學專家決定是否進行生物實驗驗證的選擇。實驗證明此系統能夠精確且有效的預測線性抗原、針對宿主專一性的病原分群方式能提供有效的線性抗原表位預測並為疫苗開發帶來實質助益。
關鍵字：虹彩病毒、神經壞死病毒、宿主專一性、投票機制、線性抗原表位

For agriculture environments, Iridoviridae and Nodaviridae probably are the leading cause of death for farming fishery. To date, many vaccines have developed. However, the efficacies of these vaccines are relatively low. The Iridoviridae is consist of two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infects vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infects invertebrate hosts. Nodaviridae also possesses similar property and is categorized into three subfamilies: Alphanodvirus includes all insect nodaviurses, Betanodavirus infects fishery species, and Gammanodavirus infects prawns. Both virus families possess host-specific characteristics, which can be considers as exclusive features for identifying effective linear epitopes (LEs). In this thesis, we provide a voting-based model that ensembles the predicting results from 5 existing LE prediction servers. According to different clustered pathogen groups, both conserved and exclusive LEs could be identified simultaneously. The advantages of undocumented cross-infection between vertebrate and invertebrate host species of the Iridoviridae and Nodaviridae families were applied to reevaluate the impact of LE prediction. Furthermore, surface structural characteristics of identified conserved and exclusive LE candidates were confirmed through predicted 3D structural alignment analysis. To validate the predicted LEs, ELISA assays were performed to identify host-specific LEs, and the experimental results showed that predicted LEs were reflected in high antigenicity responses for specific grouper species. In addition to perform biological experiments, we have trained an intelligent classifier based on IEDB experimentally verified linear epitope segments. Biologists and immunologists can use this classifier to in silico validate whether the previously predicted LEs are suitable for performing biological experiments. The thesis demonstrates that the proposed system provides an effective approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features among clustered groups.

Keywords: Iridoviridae, Nodaviridae, host-specific, voting mechanism, linear epitope

摘要 I
Abstract III
Contents V
List of Figures VII
List of Tables IX
List of Abbreviations XI
1. Introduction 1
2 Materials and methods 5
2.1 Data collection 5
2.2 System flowchart 7
2.3 Biological experiment 9
3. Results 11
3.1 LE prediction 11
3.1.1 The LEs for Iridoviridae 11
3.2 The LEs for Nodaviridae 16
3.2.1 Alphanodavirus and Betanodavirus 16
3.2.2 Betanodavirus and Gammanodavirus 17
3.2.3 Alphanodavirus and Gammanodavirus 19
3.2.4 Betanodavirus grouper-infecting species only 20
4. Conclusion and discussion 22
5. Reference 25
6. Supplementary materials 29

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