老藥新用是透過利用安全可行並已用在臨床治療上的藥物來抑制其他的藥物標的以治療其他疾病的一種藥物開發的策略。由於新藥開發困難，曠日費時，難以短時期針對突然出現並威脅生命的傳染病做出應對（如2020起的新冠肺炎大流行），老藥新用儼然已成為一個常見的策略找出安全且能治療新的適應症的「新藥」。我們收集了2016個美國FDA核准的用藥並用電腦建構出其3D結構和力場參數，同時也建構了整合了分子對接及分子動力學模擬的電腦自動化虛擬藥物篩選流程。為了促進老藥新用，我們建置了一個新的線上藥物篩選網站名叫DRDOCK, Drug Repurposing DOcking with Conformation-sampling and pose re-ranKing。其致力於提供一般的研究和臨床人員能夠使用一個完全自動化的虛擬藥物篩選流程，並根據能夠從無效藥中辨認出真正的有效藥的一個合理且可靠的排名方法來找出針對特定的標的蛋白上的標的位的有效藥。我們也使用此藥物篩選流程找出了一個美國FDA核准的藥物，tioconazole，來以做為一個正構抑制劑改用於一個與腫瘤發展相關且於好幾種癌症中高度表達的一個藥物標的ATG4B。此外，我們運用了簡單的物理模型，anisotropic network model (ANM) 和linear response theory (LRT)，與分子動力學模擬來了解結晶結構中所呈現由LC3B導致的可調節ATG4B酵素活性的分子間異位調控中的分子細節。我們並進一步設計了透過能與ATG4B N端尾巴競爭LC3B上的結合位的胜肽並證明其確能抑制ATG4B的酵素活性。此結果提供了未來一個開發或搭配使用的異位調控藥來抑制ATG4B的新的策略。最後，我們使用了此藥物篩選流程來尋找藥物能夠針對nsp16及SARS-CoV2的RNA偽結結構結合並期望降低其酵素催化 及 -1 programmed ribosomal frameshifting (-1 PRF)的效率，希望能夠找到能立即使用在COVID-19治療上的藥物。

Drug repurposing is a drug development strategy where a drug with known prescription and safety for clinical treatment is reused to target other drug targets and disease treatments. Due to the slow growth of the number of yearly reported new drugs, the need for new treatments for incurable diseases, and to achieve prompt reaction against the emergent life-threatened epidemics, drug repurposing has become a prevalent strategy to polish the old drugs for new indications. We collect and compile a set of 2016 FDA-approved drugs of which the 3D structures and force field parameters were computationally generated, and build an automatic virtual drug screening pipeline that integrates the molecular docking and molecular dynamics (MD) simulations. To facilitate the drug repurposing, we construct a new online drug screening webserver called DRDOCK, Drug Repurposing DOcking with Conformation-sampling and pose re-ranKing. It aims to provide the general researchers and clinicians the access to this fully automated virtual drug screening pipeline to prioritize potent drugs that could target a specified target site of a given target protein, according to a rational and reliable ranking algorithm calibrated for distinguishing the true binders from decoys. We also use the automatic virtual drug screening pipeline to identify that an FDA-approved drug, tioconazole, can be repurposed as an orthosteric inhibitor of autophagin ATG4B, a tumor progression-associated drug target that has been found to highly expressed in several cancers. Besides, we use the simplified physical models, anisotropic network model (ANM) and linear response theory (LRT), and MD simulations to understand the molecular details behind the intermolecular allosteric regulation of ATG4B enzyme activity mediated by LC3B suggested from solved crystal structures. We further design the peptides that can interfere with this allosteric regulation by competing with the ATG4B N-terminal tail to the LC3B binding site and therefore reduce ATG4B activity, which suggested a new strategy of designing and combining allosteric drug to target ATG4B. Finally, we used our drug screening pipeline to seek drugs that could target the pseudoknot and impair the -1 programmed ribosomal frameshifting (-1 PRF) efficiency of SARS-CoV2 for ready use in COVID-19 treatment.

Dedication - 2 -
獻詞 - 3 -
Acknowledgments - 4 -
English Abstract - 5 -
中文摘要 - 6 -
Table of Contents - 7 -
List of Figures - 11 -
List of Tables - 14 -
List of Movies - 16 -
Chapter 1. DRDOCK: A Drug Repurposing platform integrating automated docking, simulations and a log-odds-based drug ranking scheme - 20 -
1.1 Introduction - 20 -
1.2 Materials and methods - 24 -
1.2.1 In silico FDA-approved drug library - 24 -
1.2.2 Protein preprocessing - 24 -
1.2.3 Benchmark set curated for the development of drug ranking methods - 25 -
1.2.4 Virtual drug screening and its resulting features - 27 -
1.2.5 Ad hoc method for drug ranking - 28 -
1.2.6 Log-odds score (LOD score) - 29 -
1.2.7 Feature ranking score (FRS) - 30 -
1.2.8 Performance evaluation of the ranking methods - 31 -
1.2.9 Molecular dynamics (MD) simulation and MM/GBSA - 32 -
1.3 Website - 34 -
1.3.1 Architecture - 34 -
1.3.2 Workflow - 34 -
1.3.3 Result rendering - 35 -
1.4 Results - 38 -
1.5 Application - 47 -
1.6 Discussion - 51 -
1.7 Conclusions - 53 -
1.8 Supplementary information - 53 -
1.8.1 Supplementary results - 53 -
1.8.2 Supplementary figures - 55 -
1.8.3 Supplementary tables - 59 -
1.9 References - 60 -
Chapter 2. Drug Repurposing Screening Identifies Tioconazole as an ATG4 Inhibitor that Suppresses Autophagy and Sensitizes Cancer Cells to Chemotherapy - 79 -
2.1 Introduction - 79 -
2.2 Materials and Methods - 81 -
2.2.1 Reagents and Cell Culture - 81 -
2.2.2 Docking and Explicit Solvent MD Simulations Used in Drug Screening and Inhibitory Mechanism Studies - 82 -
2.2.3 Structure Preparation - 83 -
2.2.4 ATG4 Reporter Assays - 83 -
2.2.5 Autophagic Flux and Immunoblotting - 84 -
2.2.6 Tumor Xenograft - 85 -
2.2.7 Bimolecular Fluorescence Complementation Assay - 86 -
2.2.8 Fluorescence Microscopy - 86 -
2.2.9 Transmission Electron Microscopy (TEM) - 86 -
2.2.10 Cell viability assay - 87 -
2.2.11 Real-Time PCR - 87 -
2.2.12 Flow Cytometry for Mitochondrial Membrane Potential and Apoptosis - 88 -
2.2.13 Spheroid Cell Culture and Live/ Dead Assay - 88 -
2.2.14 Statistical Analysis - 89 -
2.3 Results - 89 -
2.3.1 In Silico Drug-repurposing Screening to Identify Tioconazole as an ATG4 Inhibitor - 89 -
2.3.2 Docking and MD Simulations Further Support Direct Blockage of the ATG4B Active Site as the Primary Inhibitory Effect of Tioconazole - 95 -
2.3.3 Tioconazole Accumulates Autophagosomes and Diminishes Autophagic Flux in Cancer Cells - 98 -
2.3.4 Tioconazole Sensitizes Cancer Cells to Starvation and Chemotherapeutic Drugs - 101 -
2.3.5 Tioconazole Enhances Chemotherapy Efficacy in Spheroid Cell Culture and Xenografted Tumors - 107 -
2.4 Discussion - 110 -
2.5 References - 114 -
2.6 Supplementary information - 119 -
2.6.1 Supplemental experimental procedures - 119 -
2.6.2 Supplemental figures - 123 -
2.6.3 Supplemental tables - 128 -
2.6.4 Supplemental movies - 129 -
2.6.5 Supplemental references - 129 -
Chapter 3. The autophagin ATG4B encodes an LC3B-regulated intrinsic intermolecular allostery targetable for cancer therapy - 133 -
3.1 Introduction - 133 -
3.2 Methods - 138 -
3.2.1 Structures preparation - 138 -
3.2.2 Anisotropic Network Model (ANM) - 138 -
3.2.3 Time-Independent Linear Response Theory (ti-LRT) - 139 -
3.2.4 Molecular Dynamics (MD) simulations - 139 -
3.2.5 In silico peptide design - 140 -
3.2.6 Protein expression and purification - 141 -
3.2.7 Peptides synthesis - 143 -
3.2.8 ATG4B cleavage assay - 143 -
3.2.9 ATG4B activity reporter assay - 144 -
3.2.10 NMR spectroscopy and chemical shift perturbation - 144 -
3.2.11 Cell viability assay - 145 -
3.3 Results - 145 -
3.3.1 X-ray crystallographically resolved difference in active sites of open and closed forms of ATG4B - 145 -
3.3.2 The binding of LC3B on ATG4B N-terminal tail perturbed the intrinsic dynamics of ATG4B and induced intermolecular allosteric regulation - 146 -
3.3.3 LC3-induced allosteric regulation of ATG4B can modulate the active site conformation and enhance to the ligand binding stability - 149 -
3.3.4 In silico designed peptides mimicking ATG4B N-terminus for LC3B binding showed moderate inhibition of ATG4B enzyme activity - 152 -
3.3.5 The inhibitory effect of the designed peptides to ATG4B activity resulted from their binding to the LIR binding pocket in LC3B - 158 -
3.3.6 The designed peptides inhibited the viability of cancer cell line - 162 -
3.4 Discussions - 165 -
3.5 Conclusions - 170 -
3.6 Supplementary information - 171 -
3.7 References - 181 -
Chapter 4. Repurpose FDA-approved drugs as SARS-CoV2 -1 PRF attenuator using structural modeling and virtual drug screening for COVID-19 treatment - 194 -
4.1 Introduction - 194 -
4.2 Methods - 196 -
4.2.1 Sequence comparison of COVID-19 and SARS pseudoknot and modeling of COVID-19 pseudoknot 3D structure - 196 -
4.2.2 Computational drug screening targeting COVID-19 pseudoknot for 2016 FDA-approved drugs - 198 -
4.2.3 Plasmid construction and purification - 200 -
4.2.4 In vitro transcription and translation - 201 -
4.2.5 Dual luciferase assays for measurement of -1 frameshifting efficiency - 201 -
4.2.6 Molecular similarity search - 202 -
4.3 Results and discussions - 202 -
4.3.1 Computationally modeled COVID-19 virus pseudoknot structure forms stable characteristic secondary and tertiary structures after 1 µs MD simulations - 202 -
4.3.2 Virtual drug screening on 2016 FDA-approved drugs identifies drugs that have favorable binding toward particular sites in the SARS-CoV2 pseudoknot (PKV2) - 204 -
4.3.3 Dual luciferase assays showed no effect on the -1 frameshifting efficiency for neither the compound 43 and selected FDA drugs - 207 -
4.4 Discussion - 210 -
4.5 Supplementary information - 212 -
4.6 References - 215 -
Chapter 5. Conclusions - 226 -

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