蛋白質研究一直以來都是非常受重視的研究課題，在後基因體時代，蛋白質區塊的結構資訊和研究被發表的相當迅速。蛋白質的研究也越來越多樣化，大致上有蛋白質表面，蛋白質序列資訊，蛋白質結構資訊，轉錄因子預測，去氧核醣核酸結合蛋白預測，以及蛋白質交互作用等研究。這些研究領域中，蛋白質結構比對的技術都佔有相當重要的地位。而目前為止已有許多的蛋白質比對方法被提出，但只有少數的研究針對蛋白質三級結構進行比對，而針對蛋白質表面的比對更是少數。因此，在本研究中，我們提出一個基於胺基酸特性及表面資訊的蛋白質結構的比對方法。在此機制內將利用Voronoi diagram以及Delaunay triangulation技術過濾構成蛋白質表面之胺基酸。此外，兩個矩陣-胺基酸置換矩陣以及二級結構置換矩陣會搭配相對溶劑可接觸面積應用於過濾胺基酸特性以及二級結構上差異過大之胺基酸。最後，一個特別的比對相似表面的方法將被用於比對兩蛋白質中是否結構上具有高相似度的三角平面存在，並用來評斷兩蛋白質是否有相似度高的表面。
在實驗中，雌激素應答因子結合蛋白質與非雌激素應答因子結合蛋白質的表面資訊特徵量有2倍以上的差異。再者，我們針對八大類易與去氧核醣核酸結合的蛋白質做了比對，可發現組內及組外量化的數據也有2倍以上的差異。最後，我們將結果應用於雌激素應答因子的轉錄因子之預測。在此部分的實驗中，分類器提升了5%的平均精確度以及5.6%的平均查全率。

Protein research has always been a critical territory viewed upon. In the post-genome era, the structures of protein domains have been published rapidly. Protein researches have also gained diversity, generally including researches of protein surfaces, protein sequences information, protein structures information, transcription factor prediction, DNA-binding protein prediction, and protein-protein interaction. To date, many protein alignment methods were brought up for the above mentioned researches, yet only few have concentrated on protein tertiary structure alignment, while alignments focused on protein surfaces are even rarer.
In this research, we have developed a protein structure alignment method based on amino acid features and surface information, the Voronoi diagram and Delaunay triangulation techniques have been employed to distill the amino acid which forms the protein surface. Also, two matrices - Amino Acid Substitution Matrix and Metric SSE Exchange Matrix will collocate with RASA (relative solvent accessible area), and be further applied to sort amino acids that are outliers with respect to the amino acid features and secondary structure. Finally, a special alignment method by surface comparison shall be applied to compare whether there are high structure similarity triangle planes, and to assess if the two proteins exist high resembling surfaces.
Among this research, a new method of protein alignment had been brought up. Experimental data showed that there existed upper than 2 times discrepancy of surface information between ERE (estrogen response element) and non-ERE binding proteins. Furthermore, as we concentrated on aligning eight major types of protein prone to bind with DNA, we discovered there was also upper than 2 times difference between the two groups. Finally, we proceeded to apply these results to ERE transcription factor predictions. Upon this part of research, classifiers increased 5% of average precision and 5.6% of average recall.

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