蛋白質結構快速搜尋法SARST之開發及其於特殊結構偵測之應用

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研究生：	羅惟正 Lo, Wei-Cheng
論文名稱：	蛋白質結構快速搜尋法SARST之開發及其於特殊結構偵測之應用 SARST: an efficient protein structural similarity search method applied to the detection of novel protein structural relationships
指導教授：	呂平江 Lyu, Ping-Chiang
口試委員:
學位類別：	博士 Doctor
系所名稱：	生命科學暨醫學院 - 生物資訊與結構生物研究所 Institute of Bioinformatics and Structural Biology
論文出版年：	2009
畢業學年度：	97
語文別：	英文
論文頁數：	150
中文關鍵詞：	線性編碼、蛋白質結構、蛋白質結構搜尋比對、蛋白質結構環狀序列重組、蛋白質之結構區域交換、結構重組
外文關鍵詞：	linear encoding, protein structure, SARST, circular permutation, three-dimensional domain swapping, structural rearrangement
相關次數：	點閱：1 下載：0
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The journey toward comparative protein structural bioinformatics that this thesis will guide you starts with a linear encoding algorithm for three-dimensional (3D) protein structures. In addition to the applications of this algorithm to structural similarity searches, following the letters upon the journey several interesting biological phenomena will be introduced, inclusive of circular permutation (CP), 3D domain swapping (DS) and complicated structural rearrangements in proteins.
First of all, protein structural data is increasing exponentially nowadays, such that more efficient tools are required to access structure similarity search. In this work, Ramachandran Sequential Transformation (RST) algorithm has been proposed to linearly encode protein structural data into a sequence form and a database search tool SARST (Structural similarity search Aided by RST) has been developed. SARST is comparable to Combinatorial Extension in terms of accuracy while running ~240,000 times faster. Based on this speed advantage and the unique properties of linearly- encoded structural data, RST has been successfully applied to detecting interesting protein structural relationships like CP and DS, which are difficult to be identified by using conventional methods. CP is an evolutionary event resulting in the fact that structurally similar proteins may have different locations of termini. Because of the complicated rearrangement nature of CP, convenient computational resources were not available yet for the study of CP before our work. We have developed an efficient search tool CPSARST (CP Search Aided by RST), the first CP database (CPDB) and a viable CP site predictor CPred. DS is a mechanism for forming protein quaternary structures from monomers. It is defined as two or more protein chains exchanging part of their identical structure to form intertwined oligomers. A new DS-detecting method, DS-SARST (DS Search Aided by RST), is described in this thesis. Finally, a multi- processor, batch-processing web server iSARST is introduced before we end this article with several proposed future works related to RST. iSARST is an integrated implementation of several structural comparison tools and RST-based searching methods. It can be used to retrieve common structural homologs from large databases or to identify DS, CP and other structural rearrangements more complicated than CP.
To sum up, the algorithms, searching methods and web services described in this thesis provide scientists an efficient, innovative set of resources to search, compare and analyze protein structures, especially those with novel relationships. We expect that they can be helpful for the understanding of protein evolutionary mechanisms and will facilitate the application of structural permutation and 3D domain swapping in bioengineering and biotechnology fields.

此論文將透過一個蛋白質三維結構線性編碼方法，帶領讀者進行一趟蛋白質結構生物學之旅。除了闡述此線性編碼方法如何應用於蛋白質結構相似性搜尋比對外，本文還能使讀者了解幾個有趣的生物現象，如蛋白質結構環狀序列重組 (circular permutation; CP)、蛋白質結構之區域交換現象 (3D domain swapping; DS)、以及複雜的蛋白質結構重組。
目前蛋白質結構資料量正以指數型態快速增加，因此，發展高效率的蛋白質結構比對搜尋方法對結構生物學研究而言非常重要。在此研究中，我們提出一個「拉馬銓德朗氏線性轉換法 (Ramachandran Sequential Transformation; RST)」，能將蛋白質三維結構資料轉換為一維文字字串。此外並發展了一個高速蛋白質結構比對搜尋工具SARST (Structural similarity search Aided by RST)。SARST在結構搜尋比對上的準確率不但接近Combinatorial Extension方法，速度更有其二十四萬多倍快。立基於這樣的速度優勢及運用一維化結構資訊之特性，我們已成功將RST演算法應用來偵測新穎而有趣的蛋白質結構關連性，如蛋白質結構環狀序列重組和蛋白質結構之區域交換現象。這些都是傳統方法不容易偵測出來的。蛋白質結構環狀序列重組是一連串演化事件所致，可使結構極相似的蛋白質擁有不同開口。換言之，它可看作是某蛋白質原來的N端與C端被接在一起，然後於另一處產生開口。因為這種複雜的重組關係不易分析，所以在我們的研究工作開始前，相關的生物資訊資源一直很少。在此，我們發展了一個高效能的蛋白質結構環狀序列重組搜尋工具CPSARST (CP Search Aided by RST)、世界上第一個蛋白質結構環狀序列重組資料庫CPDB及一個CP切位預測程式CPred (CP site predictor)。蛋白質結構區域交換現象是種單體 (monomer) 蛋白質互相結合為具四級結構之寡體 (oligomer) 的機制。此現象的定義是：兩個或多個蛋白質間相互交換一段相同的結構區域而成為外型交錯的寡體。此論文中，我們將介紹一個新的蛋白質結構區域交換偵測程式DS-SARST (DS Search Aided by RST)。除上述工具外，我們還開發了一個多工且批次化的蛋白質結構搜尋比對網路服務器，名為iSARST。iSARST整合了各RST相關搜尋工具及數種知名的結構比對方法，用以提供高速度且高準確率的蛋白質結構比對搜尋服務。它可偵測一般的蛋白質結構類似物，也可偵測蛋白質結構區域交換或蛋白質結構環狀序列重組現象，亦能偵測更複雜型態的蛋白質結構重組。文末，我們提出數個RST的未來應用方向，藉此充分說明其潛力。
綜合而言，本論文中所描述的演算法、搜尋方法及網路服務提供了科學家們一組有創意而高效能的生物資訊資源，可用以搜尋、比對與分析蛋白質結構，尤其是分析蛋白質間的新穎性結構關連性。我們預期這些資源將有助人們了解蛋白質的演化，並可加速蛋白質結構重組及結構區域交換現象之相關技術於蛋白質工程和生物科技領域的應用。

中文摘要    1
Abstract    2
謝誌    3
Contents    5
Introduction    9
Chapter 1.  Ramachandran Sequential Transformation (RST) and SARST: a linear encoding methodology for protein structural data and its application to protein structural similarity searches    15
1    Background    16
2    Results    19
2.1    Algorithm – Ramachandran sequential transformation (RST)    19
2.2    Building scoring matrices – a regenerative approach    21
2.3    Optimization of the scoring matrix    21
2.4    Evaluation of speed    23
2.5    Evaluation of accuracy    23
2.6    Implementation: Performance using different structural classes    23
2.7    Performance on incomplete structures    25
2.8    Effects of low sequence identities    26
2.9    Reliability of searching results    27
2.10    Normalization of SARST scores    27
2.11    Distantly related homologs retrieved by SARST: two examples    28
3    Discussion    31
3.1    On speed    31
3.2    On accuracy    31
3.3    On improvements    32
3.4    Significance of SARST score    33
3.5    Expected applications of SARST    34
4    Conclusions    35
5    Methods    35
5.1    Optimization of the search engine parameters    36
5.2    Practical parameter settings for SARST    36
5.3    Assessment of speed and precision    36
6    Acknowledgements    37
7    Tables    38
Chapter 2.  CPSARST: an application of RST to the detection of circular permutation in proteins    41
1    Background    42
2    Results    46
2.1    Performance on random circular permutants    46
2.2    Accuracy evaluations with engineered CPs    46
2.3    Pair-wise comparisons of naturally occurring CPs    48
2.4    Protein structural database searches    49
2.5    Novel CP family detected by CPSARST    50
2.6    Circular permutants detected by CPSARST    52
3    Discussion    55
3.1    Detecting circular permutants with low sequence identities    55
3.2    Speed improvements    55
3.3    The prevalence and definition of circular permutation    56
3.4    Possible applications of CPSARST    58
4    Conclusions    59
5    Materials and methods    59
5.1    Linear encoding of protein structures    60
5.2    Generation and analyses of random circular permutants    60
5.3    Screening of CP candidates    61
5.4    Refinement of the search results    61
5.5    Pair-wise CP structural alignments    62
5.6    Implementation    63
6    Acknowledgements    64
7    Tables    65
Chapter 3.  CPDB: the first database of circular permutation in proteins    70
1    Background    71
2    Contents and methods    72
2.1    Identification of circular permutation    72
2.2    Categorization of circular permutants    73
2.3    Circularly-permuted alignments and the visualization of CP relationships    74
2.4    Prediction of viable circular permutants    75
3    Web interface    75
4    Future works    77
5    Acknowledgements    77
Chapter 4.  DS-SARST: an efficient three-dimensional domain swapping search tool based on RST methodology    78
1    Introduction    80
2    Results and Discussions    84
2.1    Performance of the SVM classifier in DS-SARST    84
2.2    Experiments on published DS pairs    85
2.3    Hinge loop detection    87
3    Conclusion and future works    88
4    Materials and methods    88
4.1    Linear encoding of protein structures    88
4.2    The double filter-and-refine strategy    89
4.3    Alternative structure alignment    89
4.4    Preparation of training dataset    91
4.5    Setting up Filter I    94
4.6    Setting up the SVM classifier    94
4.7    Hinge loop detections    96
5    Acknowledgements    97
6    Tables    98
Chapter 5.  iSARST: an integrated web server for rapid protein structural alignment searches    102
1    Introduction    103
2    Methods    105
2.1    Linear encoding of protein structures    107
2.2    Structural similarity searches    107
2.3    Refinement of searching results    107
2.4    Multi-processor implementations    108
3    Experiments    109
4    Web server description    110
4.1    Input and the searching page    110
4.2    Output: hit list    111
4.3    Output: structure inspection page    111
5    Expected applications    113
6    Acknowledgements    113
7    Tables    114
Chapter 6.  Ongoing projects related to SARST and other possible applications of RST    116
1    Prediction of viable circular permutation sites using weighted closeness    118
1.1    Background    118
1.2    Methods    118
1.3    Preliminary results    120
1.4    Future works    121
2    FASARST: a potential method for the detection of complicated protein structural rearrangements by fragmented alignment searches    127
2.1    Background    127
2.2    Methods    128
2.3    Future works    128
3    Other possible applications of RST and SARST-related proposals    130
Chapter 7.  Concluding remarks    132
References    135
Appendices    147
Appendix 1.1 The training set of SARST    147
Appendix 1.2 Performances of SARST scoring matrices    147
Appendix 1.3 The query and target proteins for information retrieval experiments    147
Appendix 1.4 Effects of gap penalties    147
Appendix 1.5 Query proteins with incomplete structures    148
Appendix 2.1 The nrPDB-90 dataset    148
Appendix 2.2 The nrSCOP-90 dataset    148
Appendix 2.3 Candidate CP pairs in nrPDB-90 detected by CPSARST    148
Appendix 2.4 Structural neighbors of protein YlqF retrieved by DALI    149
Appendix 2.5 Additional statistics of CPSARST database searching    149
Appendix 2.6 The RCP dataset    149
Appendix 2.7 Score and E-value ratios calculated from the RCP dataset    149
Appendix 2.8 Parameter settings of CPSARST used in this study    150
Appendix 3.1 The non-redundant PDB dataset used for the development of CPDB    150
Appendix 3.2 Classification of proteins according to the secondary structural elemental contents    150

                                

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