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研究生： 鄭博瀚
Cheng, Po-Han
論文名稱： 一個利用Delaunay三角化模型的蛋白質表面搜尋的去氧核糖核酸結合蛋白預測方法
Protein Surface Search in DNA-binding Protein Prediction by Delaunay Triangulation Modeling
指導教授： 高宏宇
Kao, Hung-Yu
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 54
中文關鍵詞： 蛋白質結構比對 、蛋白質表面搜尋 、Voronoi圖 、Delaunay三角化 、去氧核醣核酸結合蛋白預測 、相對溶劑可接觸面積 、雌激素應答因子
外文關鍵詞： Protein structure alignment, Protein surface search, Voronoi diagram, Delaunay triangulation, DNA-binding protein prediction, Relative solvent accessible area, Estrogen response elements
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    • 近年來，結構蛋白質組計畫及基因組定序的快速進展使得蛋白質結構和序列的資訊量急速提升。越來越多蛋白質的研究也被提出，例如轉錄因子預測，去氧核醣核酸結合蛋白預測，同源模擬，以及蛋白質交互作用等等。在這些研究中蛋白質結構比對的技術都扮演重要的角色。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表面進行比對。
    在本研究中，我們提出一個基於蛋白質表面搜尋的去氧核醣核酸結合蛋白預測系統，利用Voronoi diagram以及Delaunay triangulation技術建立蛋白質表面之三角化模型。此外，我們設計一個疊代方法以建立連續且完整的蛋白質三角化表面。最後，一個整合表面結構資訊的系統將被用於搜尋蛋白質的共同表面，並用來預測去氧核醣核酸結合蛋白。
    與過去方法比較的實驗中，過濾掉的內部三角形數量有顯著提升。再者，尋找表面氨基酸的實驗中也比以相對溶劑可接觸面積為門檻的方法好。此外，針對整個PDB資料庫的搜尋可以鑒別出雌激素受體的蛋白家族。最後，我們將雌激素受體在整個PDB的搜尋結果與真實的化學實驗結果比較，證實預測高分的14-3-3β蛋白確實會與雌激素應答因子結合。

    In recent years, high-throughput structural proteomics and genome sequencing have lead to a burst in the amount of structure and sequence information available. Various protein researches have been published, such as transcription factor prediction, DNA-binding protein prediction, homology modeling, and protein-protein interaction. Structure alignment techniques have played an important role in those researches. Many protein alignment methods have been brought up, yet only few have concentrated on protein surfaces.
    Among this research, we have developed a DNA-binding protein prediction system based on protein surface search, using Voronoi diagram and Delaunay triangulation to model molecular surface. Also, we designed an iterative method to construct a consecutive and closed surface of protein. Finally, a system integrating surface structure information is applied to search common surface of proteins, and to predict protein-DNA interaction.
    While comparing to our previous method, there’s a significant increase of inner triangles identified and removed. Furthermore, experimental data showed that our method has better performance in identifying surface residues than the widely used method using an RASA cutoff. Besides, as we search the entire Protein Data Bank (PDB) for similar surface to Estrogen Receptor α (ERα), we discovered that the majority of the family of ERα in Pfam has been found with high scores. Finally, we compared our data with results of chemical experiment that suggest several proteins tending to bind to Estrogen Response Elements (ERE), finding an interesting result that the 14-3-3β protein with high score in our prediction is proven to bind to ERE in the further chemical experiment.

    中文摘要 III ABSTRACT IV Content VI Table listing X Figure listing XI 1. INTRODUCTION 1 1.1 Background 1 1.2 Motivation 1 1.3 Our approach 4 1.4 Paper structure 6 2. RELATED WORK 7 2.1 Related research 7 2.1.1 Alignment 7 2.1.2 Voronoi Tessellation 8 2.1.3 Delaunay Triangulation 11 2.1.4 DSSP 12 2.2 Data resource 14 2.2.1 PDB 14 2.2.2 UniProt 15 2.2.3 TRANSFAC 16 2.2.4 BAliBASE 3.0 16 3. METHOD 19 3.1 Overview 19 3.2 Protein Structure Triangulation 19 3.3 Protein Surface Retrieval 20 3.3.1 ISB (Initial Spherical Border): Method of Previous Work 20 3.3.2 ATR (Accumulated Tetrahedron Reduction): Improvement of ISB 21 3.3.3 IATR (Iterative Accumulated Tetrahedron Reduction): Improvement of ATR 24 3.4 Identifying Similar Triangles 28 3.4.1 Triangle Distance 28 3.4.2 Amino Acid Substitution Matrix 28 3.4.3 Metric SSE exchange matrix 29 3.4.4 RASA 30 4. EXPERIMENTS 31 4.1 Dataset 31 4.2 Experiment 33 4.2.1 Evaluation of Surface Extraction 33 4.2.2 Influence of Distance Threshold on Triangle Surface 35 4.2.3 Evaluation of Extracted Surface 36 4.2.4 Distinguishing Different Structure 43 4.2.5 Experiment on PDB 45 4.2.6 Prediction of Zf-C4 family 47 4.2.7 Compare to Results of Chemical Experiment 49 5. CONCLUSIONS AND FUTURE WORK 51 5.1 Conclusions 51 5.2 Future work 51 6. REFERENCES 52

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