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研究生(外文):Chiang-Man Sun
論文名稱(外文):Distance Matrix Analysis of Mutual Secondary Structure Pairs for Multiple Structure Alignment
指導教授(外文):Tun-Wen Pai
外文關鍵詞:distance matrixlocal region searchmultiple protein structure alignmentangle-distance mapiterative refinement algorithm
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In the fields of biology, biologists comprehend the reaction and correlation of protein evolution through analyzing protein sequences, structures, and surfaces. However, the number of protein sequences and resolved structures continues to grow exponentially, so how to design bioinformatics algorithms to efficiently and effectively analyze the relationship among proteins becomes one of the most important issues. The traditional method for protein comparison applies sequence alignment, but it has been found that a certain number of protein sets hold low sequence identities but possess functional or structural similarities. Depending only on sequence alignment cannot overcome this dilemma. Hence, this study developed a multiple structure alignment system which is based on the secondary structure information. The main approach of this proposed system is to feature the characteristics of mutual correlation of secondary structure element (SSE) pairs in a protein structure. The algorithm utilizes the local matching advantages through distance matrix matching criteria to extract suitable candidates of SSE pairs from each protein. The similarity scores of compared distance matrices of mutual SSE pairs are calculated and ranked to decide representative points as key anchors for multiple structure alignment. Based on these three key anchor points, translation and rotation transformations are performed to obtain an initial alignment results and followed by an iterative refinement procedure for an optimal solution. The experimental results were verified by SCOP, Homstrad, and SABmark benchmark databases. Several cases with low sequence identity were compared with well-known protein structure alignment tools. The results showed the averagely aligned residues were evidently increased and the root-mean-square-deviance decreased within these low sequence similarity protein sets.
Abstract(chinese) i
Abstract iii
Acknowledgments v
List of figures vii
List of tables viii
1. Introduction 1
2. Protein structure alignment 4
3. System architectures 9
4. System module description 11
4.1. Data Preprocessing 11
4.2. Vector Transformation 12
4.3. Intra-relationship analysis 13
4.4. Target protein determination 14
4.5. Inter-relationship analysis 17
4.6. Constrained multiple structure feature alignment 22
4.7. Iterative refinement techniques 24
5. Experiment results 25
6. Conclusions 34
7. Reference 36
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