基於角度與距離影像比對技術開發出高效率的蛋白質結構比對方法

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研究生：	朱家漢 Chu, Chia-Han
論文名稱：	基於角度與距離影像比對技術開發出高效率的蛋白質結構比對方法 An efficient protein structural comparison method based on angle-distance image matching techniques
指導教授：	唐傳義 Tang, Chuan-Yi
口試委員:
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2010
畢業學年度：	99
語文別：	英文
論文頁數：	161
中文關鍵詞：	距離影像圖、二集結構比對、蛋白質結構分類、蛋白質結構比對、蛋白質功能區域交換、功能區域交換、結構排比、二集結構元素、功能區域交換偵測、蛋白質構形疾病
外文關鍵詞：	A-D image, secondary structural matching, protein structure classification, protein structural comparison, 3D domain swapping, domain swapping, structural alignment, secondary structural element, domain swapping detection, protein conformational disease
相關次數：	點閱：4 下載：0
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蛋白質結構比對與分類經常被運用來了解蛋白質結構與功能之間的演化關係。然而，蛋白質結構比對是非常耗費時間的方法，為了降低計算時間，我們在此論文中發展了一個新穎的蛋白質結構比對方法，透過將一個三維蛋白質結構轉換成二維角度與距離的影像(A-D image)，將原本的蛋白質結構比對問題轉換成影像模板比對問題，所提出的方法不僅改善了蛋白質結構比對的效率並且因其獨特的特性，帶來了一些應用，這些應用是傳統方法無法完全解決的問題。

角度與距離影像是對每一個蛋白質的二級結構元素進行向量化程序，並運用任兩個二級結構元素在空間上的角度以及中心點距離關係建立一張角度距離影像。根據任兩個二級結構的組成關係，將每一張距離影像分解成三張不同型態的子影像，接著，即可經由轉換過後的影像，對任兩個蛋白質結構進行互相關的比對分析以便從中發現相似的影像樣版。此方法與二級結構的連結方式以及個別功能區塊在空間上的方向無關，進而可從角度與距離影像上的樣本相似性來判斷蛋白質結構的相似程度。實驗結果證明所提出的方法，即使對低序列相似度的兩個蛋白質，也能夠有效將其正確地分類到由SCOP資料庫定義的折疊層中。

基於距離與角度影像的技巧，本論文提出另一個新穎的方法，得以精準地偵測蛋白質結構區域交換(3D domain swapping; DS)現象的方法。蛋白質結構區域交換是一種形成蛋白質四級結構的機制，可以想像成很多個單體 (monomer)將其原本關閉的結構打開並且將打開的部分互相交換，形成互相纏繞的寡聚體 (oligomer)。自從第一個蛋白質結構區域交換現象被提出之後，越來越多證據說明在適當的環境下並且該結構具有不受限制的兩端，是形成此現象的原因。到目前為止，此區域交換現象已被研究認定與分子演化、功能調節、阿茲海默症(Alzheimer’s disease)以及普粒子疾病(prion disease)等息息相關，除此之外，蛋白質結構區域交換亦可應用在製作生物材料方面上，因此，為了更深入研究此現象形成的原因以及礙於目前研究蛋白質結構區域交換生物資訊資源的缺乏，此方法的發展將促使相關研究領域向前邁進。

總結，在本論文中，我們提出了一個創新的蛋白質結構比對方法以及一個新穎的蛋白質結構區域交換的偵測系統。實驗結果證實了本論文提出之方法比其他現存的工具更能夠有效地偵測蛋白質結構區域交換關係。在未來研究進程上，蛋白質結構區域交換資料庫將被建立並用來促進相關生物工程的發展。

Protein structure comparison (PSC) and classification have been utilized to comprehend evolutionary relationship between protein structures and functions. However, PSC is computationally time-consuming due to the multiple dimensions of geometric information and the complexity of spatial organizations of atoms. In order to reduce the computational complexity, we have developed a novel PSC method by transforming a three-dimensional structure into a two-dimensional angle-distance (A-D) image. By converting geometric comparison problems into image template matching problems, our methodology not only achieves an improved PSC efficiency but also brings about some unique properties and applications that are difficult for conventional PSC methods.
Angle-distance images are created by utilizing secondary structure information of proteins. Subsequently, they are compared by using the cross-correlation approaches which are free from the limitations of the connectivity of secondary structural elements (SSEs) and the spatial orientations of individual domains. Similarities between protein structures are thus identified as various similarities of patterns in A-D images. Our experimental results demonstrate that the proposed method can accurately and efficiently classify protein structures at the fold level defined by the SCOP database even for proteins sharing low sequence identities.
Based on the A-D image techniques developed here, we develop a novel and the first practical detection method for three-dimensional domain swapping (DS). DS is a mechanism for forming protein quaternary structure that can be visualized as if monomers had “opened” their “closed” structures and exchanged the opened portion to form intertwined oligomers. DS has been considered possible to occur in a protein with an unconstrained terminus under appropriate conditions. It may play important roles in the molecular evolution and functional regulation of proteins, and in the course of formation of Alzheimer’s and prion diseases. In addition, DS is promising for the design of auto-assembling biomaterials. Given the increasing interest paid to DS and the lack of bioinformatics resources specifically designed for studying DS, our developments may help move related fields forward.
To sum up, in this dissertation, a new PSC methodology and a novel detection system for DS have been proposed. The results have been demonstrated to be more applicable to detect DS relationships than the well-known existing sequence/structural alignment and domain motion detection methods. In the future, DS database is expected to be built to promote the development of the related biological engineering.

Contents
中文摘要    1
Abstract    2
誌謝辭    3
Contents    4
Introduction    9
Chapter 1. Protein Structural Classification by using Angle-Distance image matching technique    21
1.3    Results and Discussion    23
1.3.1    Case1: Novel dataset from SCOP    23
1.3.2    Case2: Leluk-Konieczny-Roterman dataset    25
1.3.3    Case3: Chew-Kedem dataset    27
1.3.4    Case4: Skolnick dataset    29
1.3.5    Case 5: Kinase dataset    32
1.1    Materials    37
1.1.1    Novel dataset    37
1.1.2    Leluk-Konieczny-Roterman dataset    37
1.1.3    Chew-Kedem dataset    38
1.1.4    Skolnick dataset    38
1.1.5    Protein Kinase (PK) dataset    39
1.2    Methods    40
1.2.1    Vector transformation    44
1.2.2    Intra-relationship analysis    44
1.2.3    A-D image construction    45
1.2.4    A-D image comparison    47
1.2.5    Time complexity analysis for the A-D images Comparison    51
1.4    Conclusion    52
1.5    Acknowledgements    53
1.6    Tables    54
Table 1.1: List of proteins included in the novel dataset (randomly selected from the SCOP database). There were six superfamilies categorized into the four major classes all α, all β, α+β, α/β.    54
Chapter 2. The detection of 3D domain swapping phenomenon by using Angle-Distance image matching technique    56
2.1    Overview of the Proposed Method    59
2.2    Results    62
2.2.1    Feasibility of conventional protein structure comparison methods for detecting the 3D domain swapping phenomenon    62
2.2.2    Performance of the proposed A-D image based DS-detecting method combined with conventional structural measures.    66
2.2.3    Definition and evaluations of a novel DSco pair score    68
2.2.4    Evaluations of the proposed method using literature-derived and manually identified 3D domain swapping cases    75
2.2.5    Quality of structural alignment    76
2.2.6    Detecting various 3D domain swapping types    78
2.2.7    Effects of sequence identity on the performance of the proposed method        79
2.2.8    Identification of hinge loops    80
2.2.9    Implementation and illustrative examples of structural alignment            82
2.3  Discussion    83
2.3.1    Difficulties in DS-detection for conventional alignment approaches    83
2.3.2    Crucial factors for the DS-detecting ability of the proposed method        88
2.3.3    Precision of hinge loop determinations    90
2.3.4    Sensitivities to middle-domain swapping cases    93
2.3.5    Conclusions    95
2.4    Materials and Methods    97
2.4.1    Preparation of Experimental Datasets    98
2.4.1.1    Dataset L    98
2.4.1.2    Dataset M    100
2.4.2    A-D image-based protein secondary structural matching    101
2.4.2.1    Construction of A-D images and determination of allowed vertices in the pair graph    105
2.4.2.2    Scoring scheme for edge computation    106
2.4.2.3    Deducing the equivalence of SSEs from the equivalence of A-D points    107
2.4.2.4    SSE matching    109
2.4.3    Locating candidate hinge loops by the profile of the A-D product            109
2.4.3.1    Superposition-dependent protein structural alignment    110
2.4.3.2    Profile of the A-D product (A-D profile)    111
2.4.3.3    Identification of significant transitions in the A∙D profile    112
2.4.3.4    Identification of 3D domain swapping types    113
2.4.3.5    Determination of the approximate opening point of a hinge loop        114
2.4.4    Refinement of the location and range of hinge loops    114
2.4.4.1    Assigning small candidate swapped domains    115
2.4.4.2    Refining the location of opening points of candidate hinge loops        116
2.4.4.3    Validating the feasibility of candidate hinge loops and swapped domains    117
2.4.4.4    Structural alignment of the determined main and swapped domains        118
2.4.4.5    Determining the range of hinge loops    118
2.4.5    Calculation of the DS score    119
2.4.6    The choices of positive and negative data for binary classification experiments    121
2.4.7    Experimental parameters    122
2.4.7.1    Alignment ratio    122
2.4.7.2    Virtual structural similarity measures    123
2.4.7.3    BLAST and SARST alignment parameters    124
2.5 Conclusion    126
2.6 Open problems    127
2.6.1 Does the proposed method make sense to serve as an all against all DS detection system?    127
2.7    Acknowledgements    128
2.8    Tables    129
Table 2.1. Average alignment size and RMSD of all available DSCO pairs calculated by the proposed method    129
Table 2.2. Sensitivity for the detection of various DS types    130
Table 2.3. Performance of DS-detection over various sequence identities    131
Table 2.4. Comparison of manually examined hinge loops and hinge loops identified by the proposed method    132
References    133
Appendices    140
Appendix 1.1    Derivation of vector formulation from a set of points    140
Appendix 2.1    The number of DS-related homologs, common homologs and non-homologs remaining in the test set as the alignment ratio cutoff decreases.            143
Appendix 2.2    Stability evaluations of the discriminatory model of the proposed method by k-fold cross-validations.    145
Appendix 2.3    Performances of several protein structure/sequence comparison methods for the detection of global structural similarities between DS-related homologs with various sequence identities.    147
Appendix 2.4    Examples of the A∙D profile and related hinge loop detection procedure.        149
Appendix 2.5    Table A2.5-1. DS-detecting performance of DynDom assessed based on Eisenberg’s DS dataset    151
Appendix 2.6    Table A2.6-1. Performance of various alignment methods on the identification of homologs    152
Table A2.6-2. Performance of various alignment methods on the identification of DS-related homologs (negative data: non-homologs)    153
Table A2.6-3. Performance of various alignment methods on the identification of DS-related homologs (negative data: common homologs)    154
Table A2.6-4. Performance of various structural similarity measures on the identification of homologs    155
Table A2.6-5. Performance of various structural similarity measures on the identification of homologs (negative data: non-homologs)    156
Table A2.6-6. Performance of various structural similarity measures on the identification of DS-related homologs (negative data: common homologs)    157
Appendix 2.7    Table A2.7-1. Results of inter-dataset training and testing of the proposed method for the identification of DS-related homologs    158
Appendix 2.8    Table A2.8-1 (Table S4). Results of the structural alignments and hinge loop determinations for DSco pairs in Datasets L and M    159
Appendix 2.9    Table A2.9-1 (Table S5). Structure-based sequence alignments for DSco pairs in Datasets L and M performed by several protein structural comparison methods    159
Appendix 2.10    Table A2.10-1. Number of SSEs in the swapped domains    160
Appendix 2.11    Dataset A2.11-1. Dataset L.    161
Dataset A2.11-2. Dataset M    161

                                

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