碳水化合物結合模組(carbohydrate binding module; CBM)為碳水化合物合成、代謝、運輸相關酵素的獨立功能區域，其功能為提升天然基質聚合物如澱粉及纖維素催化效率，但目前只有少部份碳水化合物結合區之三級結構已由實驗解出。蛋白質之模擬結構可由計算方法預測，其中同源結構模擬法最重要的因素為精確的目標及樣板序列排比。然而，碳水化合物結合模組彼此之間序列相同程度極低，以致一般的序列排比及生物資訊演算法不易準確預測其三級結構。本論文發現在碳水化合物結合區域家族中親水性芳香環胺基酸(hydrophilic aromatic residues; HARs)及二級結構元素具有關鍵的保留性，結合’附加特徵排比’ (feature-incorporated alignment; FIA)方法可準確比對碳水化合物結合模組中的重要特徵。本論文提出具有改善的序列排比技術可進一步整合至模擬結構建立，明顯提升預測結構的可靠度並偵測潛在的配體結合芳香環胺基酸。各項指標顯示基於FIA所辨識的親水性芳香環胺基酸及預測的三級結構與已知生物實驗結果相近，且對未經實驗確認的預測結果擁有高度的合理性。

本論文所提出的FIA與其他六個知名領先的序列排比方法之中，FIA的排比結果具有最高的平均序列相同程度(sequence identity)及序列相似程度(sequence similarity)，且整合FIA產生的模擬結構亦具有最低的平均表面位能z值(surface-potential z score)，各項目改善數據都具備非常顯著的統計意義。另外親水性芳香環胺基酸可直接用於預測潛在的配體結合胺基酸，比一般的方法具較高的真陽性率。最後，本研究廣泛地針對碳水化合物結合模組家族序列預測其三級結構，並將結果存放於「碳水化合物結合區域預測結構資料庫」(Database of Simulated CBM structures; DS-CBMs)中，線上結構預測系統亦支援即時預測使用者上傳之碳水化合物結合模組序列之結構。

Carbohydrate binding modules (CBMs) have been recognized as attaching to carbohydrate-related enzymes to upgrade catalytic efficiency of natural biological polymers including starch and cellulose. Because only a relatively small number of CBM structures have been resolved, alternatively, in silico structures can be established by computational modeling approaches and an accurate target-template sequence alignment is the most deterministic factor for homology modeling. However, the major bottleneck for qualified CBM structure simulation is low sequence identities among CBM members. Fortunately, the conserved characteristics of hydrophilic aromatic residues (HARs) and secondary structure elements were observed and taken into account in feature-incorporated alignment (FIA) to match up the core motifs among CBMs. In this thesis, the improved alignment results were integrated to in silico structure building to increase reliability of predicted structures and to detect potential ligand-binding aromatic residues.

The identified HARs and predicted structures deriving from FIA were close to in vitro results and possessed high accuracy and reliability under various criteria. Among FIA and six leading alignment programs, FIA achieved the highest sequence identity and sequence similarity on average with statistically significance. In addition, in silico structures deriving from FIA reported lowest average surface-potential z score with statistically significance. Furthermore, HAR identification can be applied to predict ligand-binding aromatic residues with high true positive rates. Finally, this work comprehensively predicted structures for CBM members and deposited them into a repository named Database of Simulated CBM structures (DS-CBMs). Subsequently, online structure modeling system was developed to allow users to predict their own CBM structures.

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