In this post-genomics era, protein structural comparison is one of the hot topics in bioinformatics. This thesis will lead you to two derivatives of linear encoding algorithm developed in our laboratory together with some demonstrations of the new tools that we developed for comparative structural bioinformatics studies.
As protein structures are determined rapidly, more and more new cases with which people are not familiar with remain to be studied; thus, more efficient structural comparison tools are in an urgent need. Previously, an algorithm named Ramachandran Sequential Transformation (RST) has been proposed to linearly encode protein structural data into a sequence form. It has been applied to rapid protein structural similarity searches and the determination of novel structural relationships. We further take the advantages of RST to develop a multiple protein structure alignment tool MSARST (Multiple Structural Alignment aided by RST) and an enzyme functions prediction tool EC-SARST (Enzyme Commission number Search tool Aided by RST). Evolutionary relationships among protein structures can be analyzed by MSARST and novel structures newly discovered by structural genomics projects can be automatically annotated by EC-SASRST.
In the third chapter, we would like to present our reconstruction on several recent phylogenetic trees using metabolic substrate-product relationships. Comparing to the whole genome approaches, its simplicity and scalability are suitable for reconstructing the phylogeny in upcoming future.
To summarize, we developed tools that reduce the time expense of exploring large scale structural data. We hope that the web services described in this thesis may facilitate scientists to compare, analyze protein structures and understand their evolutionary/functional relationships.

在後基因體的時代裡，蛋白質結構比對是生物資訊領域裡的熱門課題之一。本篇文章將會帶領讀者了解三維結構線性編碼的兩個應用以及一些結構綜合比較工具的展示。
目前，蛋白質結構正迅速地被解出，但有許多的結構還尚待研究，因此急需開發結構比對工具。先前我們提出了「拉馬銓德朗氏線性轉換法」，能將蛋白質三維結構資料轉換為一維文字字串，它已被應用在結構搜尋以及尋找新興的結構關係上。我們採用它快速的特點並研發出多重蛋白質結構比對（MSARST）以及酵素功能預測工具 (EC-SARST)。藉此，我們能夠使用MSARST分析蛋白質結構的演化關係，而剛被解出的新興蛋白質結構也可以透由EC-SARST預測其酵素功能。
在本文第三章，我們另外提出了利用代謝反應中反應物和生成物的關係來重建演化樹，相較於利用整個基因體的資訊，此方法的簡易性及延展性非常適合用來重建演化樹。
綜合以上，我們發展的這些工具縮短了分析大規模資料的時間，並預期前述這些網路服務將有助於科學家更快地分析比較蛋白質結構與了解其演化和功能上的關係。

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