蛋白質序列的結構預測工具有助於生物學家尋找蛋白質交互作用中的正確功能區位。在蛋白質序列比對相似度較低的情形中，提高結構預測工具的準確度已成為生物資訊科學領域中的重要研究課題。在本論文中，我們提出一套新的序列配對工具，利用相似家族類型的蛋白質二級結構資訊，來預測未知標的物之功能區位。配對工具的演算法是將蛋白質二級結構分段再重新排列之後，再計算出最佳的配對組合。我們利用了醣類結合模組(CBM)蛋白質家族當中的澱粉結合區域(SBD)來進行測試驗證，並且和其它各種預測工具做比較，最終得到與生物實驗一致的相符結果。

Structure prediction tools for protein sequences benefit the biologists to search for the real protein interactive function sites. In the low sequence alignment similarity cases, increasing the accuracy of the structure prediction tools have become an important research issue in bio-informatics. In this thesis, we propose a new novel sequence matching tool using the secondary structure information of the protein in the similar families to predict the function sites of the unknown target. The algorithm of the matching tool is cutting the protein secondary structure, rearranging the topology, and then computing the best matching pairs. We take starch-binding domain (SBD) in the carbohydrate-binding module (CBM) protein family to be verified by matching tool. We also compare these tests with other tools, and our results correspond with the final biology experiments.

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