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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林祥泰 | zh_TW |
| dc.contributor.advisor | Shiang-Tai Lin | en |
| dc.contributor.author | 劉庭嘉 | zh_TW |
| dc.contributor.author | Ting-Chia Liu | en |
| dc.date.accessioned | 2023-10-03T17:29:38Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-24 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90759 | - |
| dc.description.abstract | 蛋白質間的相互作用在生物過程中扮演著重要的角色，包括病原體和宿主的相互作用。本研究關注COVID-19病毒（SARS-CoV-2）棘蛋白上的受體結合域（RBD）與廣泛中和抗體（bNAbs）之間的相互作用，bNAbs 能夠辨識並結合到病毒蛋白不容易突變的保守區域。為了確定哪些 HIV-1 bNAbs 與 SARS-CoV-2 棘蛋白受體結合域的親和力更強，我們開發了一個自動化程序，將各種計算工具與預處理、後處理相串接。該程序包括蛋白質－蛋白質複合體的立體結構預測（使用分子對接軟體）、穩定結構的能量最小化模擬，以及分子力學/泊松-玻爾茲曼表面積法（MMPBSA）來計算結合自由能。此外，我們建立了一個機器學習模型對MMPBSA多種模型結果做綜合考量，提高結合自由能預測的準確性。
這個程序的效率以及自動化構成了我們對該領域的貢獻，為其他蛋白質的研究提供了一個模型。這個程序可以應用於從現有抗體資料庫中篩選出能對抗特定病毒的抗體，並有助於選擇潛在的治療候選藥物用於新興病毒性疾病。 | zh_TW |
| dc.description.abstract | Protein-protein interactions play a crucial role in biological processes, including pathogen-host interactions. This study focuses on the interaction between the RBD (receptor binding domain) on the spike protein of the COVID-19 virus (SARS-CoV-2) and bNAbs (broadly neutralizing antibodies), which recognize and bind to conserved (not easily mutate) regions of viral proteins. To identify which HIV-1 bNAbs have stronger affinity with the SARS-CoV-2 spike protein RBD, we develop an automated procedure that integrates various computational tools with preprocessing and postprocessing. The procedure includes 3D structure prediction of protein-protein complex (using docking software), energy minimization simulation for stable structure, and MMPBSA methods to calculate binding free energy. Additionally, we have developed a machine learning model that integrates multiple MMPBSA models to improve the accuracy of binding free energy predictions.
The efficiency and automation of this procedure contribute to our advancements in the field, providing a framework for protein research. This procedure may be applied to identify the most potent antibodies in cases of other virus with existing antibody databases, and facilitate the selection of potential therapeutic candidates for emerging viral diseases. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:29:38Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T17:29:38Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員審定書i
致謝ii 摘要iv Abstract v Contents vi List of Figures x List of Tables xiii Chapter 1 Introduction 1 1.1 The Role of Computer Simulation in Drug and Antibody Design . . . 1 1.2 Cross Neutralization of SARS-CoV-2 by HIV-1 bNAbs . . . . . . . . 2 1.3 Overview of Binding Structure and Binding Free Energy Calculation 3 1.4 Challenges in Determination of Binding Structure for Proteins . . . . 6 1.5 Evaluation of Binding Free Energy of Complexes . . . . . . . . . . . 7 1.6 Explicit vs. Implicit Solvent Models for Solvation Calculation . . . . 9 Chapter 2 Theory 11 2.1 Binding Free Energy . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Definition of Binding Free Energy . . . . . . . . . . . . . . . . . . 12 2.1.1.1 Solvation Free Energy . . . . . . . . . . . . . . . . . . 13 2.1.1.2 Binding Free Energy . . . . . . . . . . . . . . . . . . . 14 2.1.2 Derive Binding Free Energy from Experimental measurement . . . 15 2.2 Calculating Binding Free Energy - MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) . . . . . . . . . . . . . . . . . . . 16 2.2.1 Molecular Mechanics (MM) for Bonded and Nonbonded Interactions 18 2.2.2 Generalized Born (GB) / Poisson-Boltzmann (PB) Models for Polar Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.2.1 Born Model and Generalized Born Model (GB) . . . . 20 2.2.2.2 Poisson-Boltzmann Solvation Model (PB) . . . . . . . 22 2.2.2.3 Shared Mathematical and Physical Formulations in GB and PB Models . . . . . . . . . . . . . . . . . . . . . 23 2.2.3 Solvent Accessible Surface Area (SA) for Nonpolar Interactions . . 24 2.2.4 Computational Protocol . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3 Binding Structures Using Glowworm Swarm Optimization (GSO) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Algorithm for Molecular Strucutre Optimization Using Conjugate Gradient (CG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5 Machine Learning Model . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.1 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 3 Computational Details 35 3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.1 Dataset 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.2 Dataset 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.1.3 Dataset 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.1.4 SKEMPI Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.1.5 HIV-1 bNAbs Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Computational Procedure . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3 Binding Structure of Proteins by LightDock . . . . . . . . . . . . . . 46 3.3.1 Parameter Configuration . . . . . . . . . . . . . . . . . . . . . . . 46 3.4 Protein Preprocessing and Structure Optimization . . . . . . . . . . . 47 3.4.1 PDBFixer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.2 Biopython . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.3 GROMACS Energy Minimization . . . . . . . . . . . . . . . . . . 49 3.5 Calculating Binding Free Energy by gmx_MMPBSA . . . . . . . . . 49 3.5.1 GB Models (igb) . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.5.2 PB Models (ipb) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.5.2.1 Methods for Nonpolar Contribution to the Solvation Free Energy (inp) . . . . . . . . . . . . . . . . . . . . . . . 53 3.5.3 Parameter Configuration . . . . . . . . . . . . . . . . . . . . . . . 55 3.6 Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.6.1 Data preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.6.2 XGBRegressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.6.3 Parameter Configuration . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 4 Results and Discussion 63 4.1 Prediction of 3D Structure of Complex . . . . . . . . . . . . . . . . 63 4.1.1 Receptor-centered vs. Ligand-centered . . . . . . . . . . . . . . . . 64 4.1.2 Real Ranking of LightDock Predictions . . . . . . . . . . . . . . . 67 4.2 Calculation of Binding Free Energy . . . . . . . . . . . . . . . . . . 71 4.2.1 Performance of GB and PB Models . . . . . . . . . . . . . . . . . . 71 4.2.1.1 Computing from Experimental Structures in the RCSB PDB Database . . . . . . . . . . . . . . . . . . . . . . 71 4.2.1.2 Computing from Docking Structures in LightDock . . . 78 4.2.2 Improvement by ML Regression Model . . . . . . . . . . . . . . . 81 4.3 Ranking of the Binding Affinities of Protein Pairs . . . . . . . . . . . 86 4.3.1 Ranking of the Binding Affinities of bNAbs to SARS-CoV-2 . . . . 86 4.3.2 Ranking of the Binding Affinities of Other Protein Pairs . . . . . . . 89 Chapter 5 Conclusion and Future Prospects 93 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 References 96 Appendix A — Datasets 107 A.1 Dataset 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.2 Dataset 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 A.3 Dataset 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 A.4 SKEMPI dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.5 HIV-1 bNAbs dataset . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Appendix B — Results Demonstrating Significant Deviations in Error Values 124 | - |
| dc.language.iso | en | - |
| dc.subject | 自動化程序 | zh_TW |
| dc.subject | 親和力 | zh_TW |
| dc.subject | 廣泛型中和抗體 | zh_TW |
| dc.subject | 蛋白質間相互作用 | zh_TW |
| dc.subject | 新型冠狀病毒 | zh_TW |
| dc.subject | 結合自由能 | zh_TW |
| dc.subject | COVID-19 | en |
| dc.subject | binding free energy | en |
| dc.subject | affinity | en |
| dc.subject | bNAbs | en |
| dc.subject | protein-protein interaction | en |
| dc.subject | automated procedure | en |
| dc.title | 用於篩選對抗 COVID-19 棘蛋白的廣泛型中和抗體的電腦模擬計算程序 | zh_TW |
| dc.title | A Computational Procedure for Screening Broadly Neutralizing Antibodies against COVID-19 Spike Protein | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳台偉;洪英傑;賴品光 | zh_TW |
| dc.contributor.oralexamcommittee | Tai-Wei Wu;Ying-Chieh Hung;Pin-Kuang Lai | en |
| dc.subject.keyword | 蛋白質間相互作用,新型冠狀病毒,廣泛型中和抗體,結合自由能,親和力,自動化程序, | zh_TW |
| dc.subject.keyword | protein-protein interaction,COVID-19,bNAbs,binding free energy,affinity,automated procedure, | en |
| dc.relation.page | 124 | - |
| dc.identifier.doi | 10.6342/NTU202301840 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-07-25 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 化學工程學系 | - |
| 顯示於系所單位： | 化學工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 未授權公開取用 | 5.29 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。