To identify the characteristics and networking of biological macromolecules is one of the main works of current biology. While the solved structures are available, the three major factors which researchers concern about biological macromolecules are fundamental principles, structures, and functions. Revealing how fundamental principles in physics drive macromolecules folding into their correct structures, how structures possess the characteristics for binding ligands and interacting with each other, and how structures cooperate with fundamental principles together to guide macromolecules function correctly are the main tasks nowadays. Among biological macromolecules, proteins participate in virtually every process within cells. Hence, topics in related fields are in great demand today. In this work, we tried to approach the physic and structural basis underlying interactions between protein and macromolecules/other ligands.

In summary, technically we have developed two complementary structural-alphabet-based methods for approaching structural motif discovery problems: (i) a fully automated strategy to find structural motifs across protein families without requiring a query motif or essential residues and (ii) a strategy using descriptors of key components defined from known motifs to find structurally and functionally equivalent regions in other protein families. We also combined the first strategy with a method based on electrostatic stabilization and evolutionary conservation to illustrate the usefulness for detecting binding sites. Biologically, we pointed out a local structural unit stabilized by conserved intra-interactions employed as the core region for specific function in a known motif can be also found for the same purposes in other proteins with different folds. These kinds of units were defined as key components, such as the ‘corner’ architecture in helix-turn-helix motif and the ‘βα’ components in Rossmann fold domains. The results suggest that these proteins with the same function but divergent in sequences and morphologic structures may from the same ancient species possessing proteins with the simplest forms containing the key components. It may provide another viewpoint to approach the mystery of evolution.

識別生物巨分子的特性與關係網路是現代生物學主要的研究範疇之一，在可以取得結構的情況下，我們主要關注的是基本物理法則、結構以及功能三者之間的關係。揭示物理法則如何驅使生物巨分子摺疊成正確的結構，結構本身如何保有與配體結合或彼此間交互作用所需的特殊構造，以及結構與物理法則之間如何互相配合使生物巨分子能有正確的功能乃是最主要的任務。在各類生物巨分子中，蛋白質參與了細胞內的幾乎每一項反應，因此幾個以蛋白質為中心的相關研究領域，例如：蛋白質摺疊、蛋白質結構模體搜尋以及蛋白質功能注釋等，都有很大的研究需求。在本研究中，我們試圖瞭解蛋白質與其他生物巨分子或其他配體間交互作用的物理及結構基礎。

總結而言，在技術上我們發展出了兩種基於結構字母表的互補策略來尋找蛋白質上的結構模體：(i)一種完全自動化的跨家族蛋白質結構模體搜尋策略，本方法不需要任何已知的模體結構或必要殘基作為參考，以及(ii)一種結構模體關鍵元件搜尋策略，本方法係從已知結構模體中定義出關鍵元件，並利用結構字母表為其建立描述子(descriptors)，再利用描述子來搜尋其他蛋白質家族中結構與功能等價的區域；我們也結合第一種策略以及DNA結合蛋白質結構上的靜電及演化訊息，將其應用於偵測蛋白質上的DNA結合位置。在生物上，我們指出由守恆的內部作用所穩定的區域結構可被已知的結構模體用做執行特定功能所需的核心區域，也可在其他種類的蛋白質上被發現並扮演相同角色。這類的單元結構即是“關鍵元件”，例如在螺旋-轉角-螺旋模體(helix-turn-helix motif)上的角型結構以及Rossmann型蛋白質(Rossmann fold)上的‘βα’結構，這項結果提示部份具有相同功能但是在序列以及整體結構上相異的蛋白質可能演化自具有關鍵元件的最簡單蛋白質形式，而源自相同的祖先物種，這或可為研究演化問題提供另一種觀點。

Chapter 2
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