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Development of protein modeling methods for structure refinement in the context of unreliable environments : 신뢰도가 낮은 구조 환경에서의 단백질 모델 정밀화 방법 개발

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dc.contributor.advisor석차옥-
dc.contributor.author이규리-
dc.date.accessioned2017-07-14T05:58:51Z-
dc.date.available2017-07-14T05:58:51Z-
dc.date.issued2017-02-
dc.identifier.other000000140824-
dc.identifier.urihttps://hdl.handle.net/10371/125335-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 화학부 물리화학 전공, 2017. 2. 석차옥.-
dc.description.abstractThe number of experimentally determined protein structures is increasing exponentially. Based on this abundant structural information, homology modeling is now the most popular method for protein structure prediction. Still however, knowledge of high resolution structures is critical for applications using the protein structure such as drug discovery and protein design. By realizing this, protein structure refinement methods have been developed to improve the structure quality of low resolution experimental structures or model structures. Another realm of protein structure refinement is to predict the protein structure in the environment of interest, such as binding to a specific partner, when only structures resolved in different conformational states or model structures are provided.
In this thesis, four modeling methods (GalaxyLoop-PS2, GalaxyRefine2, GalaxyVoyage, and Galaxy7TM) developed in the scope of refining predicted protein structures are introduced. The methods were evolved by either extending the range of structure targeted for refinement or considering the interaction with a particular binding partner. The shared problem of these methods was that the environment of modeling was unreliable due to errors embedded in model structures. Commonly, two approaches were taken to tackle this problem. These were initially searching the conformational space in low resolution and developing a hybrid energy function less sensitive to environmental error. The development and application results of the approaches taken for each modeling method will be addressed in detail.		-
dc.description.tableofcontents1. Introduction 1
2. Protein loop modeling in unreliable structural environments 4
2.1. Introduction 4
2.2. Methods 5
2.2.1. Development of a new hybrid energy function 5
2.2.2. Initial loop conformation sampling 6
2.2.3. Global optimization using conformational space annealing 7
2.2.4. Generation of test sets with various range of structural error 9
2.3. Results and discussion 10
2.3.1. Energy function for protein loop modeling 10
2.3.2. Environmental error of the test set 14
2.3.3. Loop reconstruction in the crystal structure framework 16
2.3.4. Loop modeling in sidechain perturbed environment 19
2.3.5. Loop modeling in backbone perturbed environment 20
2.3.6. Loop modeling on template-based models 23
2.3.7. Comparison of using hybrid energy, physics-based energy, and knowledge-based energy for loop modeling 26
2.4. Conclusion 29
3. Global refinement of protein model structures 30
3.1. Introduction 30
3.1.1. Extension of the range of structure targeted for refinement 30
3.1.2. Conformational search methods and energy functions for global refinement 32
3.2. Global refinement based on loop modeling and overall relaxation 34
3.2.1. Methods 34
3.2.2. Results and discussion 44
3.3. Global refinement with diverse modeling methods applied on unreliable local regions 63
3.3.1. Methods 63
3.3.2. Results and discussion 68
3.4. Conclusion 82
4. Flexible docking of ligands to G-protein-coupled receptors based on structure refinement 83
4.1. Introduction 83
4.1.1. Ligand docking to model structures 83
4.1.2. Predicting the ligand-bound G-protein-coupled receptor structure 84
4.2. Methods 86
4.2.1. Initial docking of ligand to receptor conformations generated by ANM 86
4.2.2. Energy function for complex structure refinement 87
4.2.3. Complex structure refinement and final model selection 89
4.2.4. Benchmark test set construction 90
4.3. Results and discussion 92
4.3.1. Energy function for complex structure refinement 92
4.3.2. Overall performance of Galaxy7TM 96
4.3.3. The relation between the docking accuracy of Galaxy7TM predictions and the receptor model quality 103
4.4. Conclusion 109
5. Conclusion 110
Bibliography 111
국문초록 121		-
dc.formatapplication/pdf-
dc.format.extent1920987 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectProtein model refinement-
dc.subjectprotein structure prediction-
dc.subjectloop modeling-
dc.subjectflexible protein-ligand docking-
dc.subjecthybrid energy function-
dc.subjectG-protein-coupled receptor-
dc.subject.ddc540-
dc.titleDevelopment of protein modeling methods for structure refinement in the context of unreliable environments-
dc.title.alternative신뢰도가 낮은 구조 환경에서의 단백질 모델 정밀화 방법 개발-
dc.typeThesis-
dc.contributor.AlternativeAuthorLee, Gyu Rie-
dc.description.degreeDoctor-
dc.citation.pagesⅸ, 122-
dc.contributor.affiliation자연과학대학 화학부-
dc.date.awarded2017-02-
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