SARS-CoV-2是一種正單鏈RNA病毒，也是引起全球2019冠狀病毒病（COVID-19）大流行的原兇，目前仍迫切需要對抗這種病毒的有效藥物。冠狀病毒的mRNA成熟涉及複雜的過程，包括病毒的非結構蛋白（Nsp）16進行mRNA加帽。這種修飾導致mRNA Cap-1的形成，mRNA Cap-1表示在mRNA 5'端的第一個核苷酸的核糖的糖的2'-O位置上添加甲基基團，此甲基基團影響病毒的免疫逃避和轉譯啟動。我專注於通過生化方法評估針對病毒mRNA成熟中至關重要的Nsp16/10複合物的抑制劑，來開發對抗SARS-CoV-2的有效藥物。我借助一個實驗室已開發的電腦輔助老藥新用篩選藥物平台，稱為DRDOCK（Drug Repurposing DOcking with Conformation-sampling and pose re-ranKing），來搜尋美國FDA批准的藥物（舊的藥物）以對抗SARS-CoV-2 Omicron變種病毒。鑒於Nsp16/10是Druggable DynOmics中收集的藥物標的之一，我還提出了一個標準程序用以選擇適合的實驗結構以進行模擬，並對Druggable DynOmics中部分藥物標的的活性位點進行調查。在論文中，我們測試了多個RNA受質，結果顯示一個缺乏腺嘌呤但富含鳥嘌呤的RNA受質最能促進Nsp16/Nsp10的催化作用，我以螢光強度來測量其催化效果。這一步確保了我能測量DRDOCK篩選的老藥和正控制組藥物的抑制效果。我的研究結果展現了DRDOCK平台在預測針對Nsp16/10的舊藥物的抑制潛力方面的有效性。這一開創性的方法在藥物發現流程中和現有藥物再利用策略中都代表了重大進步。Nsp16蛋白在各種SARS-CoV-2變體中的保守性表明針對Nsp16/10複合物的藥物的可能具有抑制病毒的潛力。這些藥物不僅有望對抗當前的COVID-19大流行，還能對抗新出現的致病冠狀病毒變體。

SARS-CoV-2, a positive-sense single-stranded RNA virus, is responsible for the global Coronavirus Disease 2019 (COVID-19) pandemic. Effective drugs against the virus are urgently needed. Coronavirus mRNA maturation involves intricate processes, including mRNA capping by the viral nonstructural protein (Nsp) 16. This modification leading to formation of Cap-1, also known as a methyl group to the 2'-O position of the ribose sugar on the first nucleotide of the mRNA's 5' end, is essential for immune evasion and translation initiation. In this thesis, I focus on the development of effective drugs against SARS-CoV-2 by biochemically evaluating inhibitors targeting the Nsp16/10 complex, crucial in viral mRNA maturation. Leveraging previously established drug repurposing platform, DRDOCK (Drug Repurposing DOcking with Conformation-sampling and pose re-ranKing) systematically screens FDA-approved (old) drugs for their repurposed use against the Nsp16/10 in the Omicron strain of SARS-CoV2. In view of Nsp16/10 being one of the drug targets collected in Druggable DynOmics, I also develop a protocol to select suitable experimentally determined structures for simulations and survey active sites for a portion of the drug targets in Druggable DynOmics. In the thesis, several RNA substrates were tested, an Adenine-free but Guanine-rich substrate was found to best promote the catalysis of Nsp16/Nsp10, quantified by luminescence intensity. The step secured a proper efficacy measurement for repurposed drugs suggested by DRDOCK and a positive control. Our findings showcase the effectiveness of the DRDOCK platform in predicting the inhibitory potential of old drugs targeting Nsp16/10. This method represents an advancement in drug discovery pipelines repurposing existing drugs. The conservation of the Nsp16 protein across a range of SARS-CoV-2 variants suggests the potential of drugs targeting the Nsp16/10 complex for virus suppression. These drugs hold a promise to combat not only the current COVID19 pandemic but also newly emerging pathogenic coronaviruses.

Table of contents
ABSTRACT i
中文摘要 ii
ACKNOWLEDGMENT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
1.INTRODUCTION 1
2.METHOD 7
2.1 PROTEIN STRUCTURE SELECTION AND IDENTIFY ACTIVE SITES 7
2.2 MD SIMULATIONS 11
2.2.1 Preparation of Initial Systems for MD Simulations 11
2.2.2 Energy Minimization 11
2.2.3 Heating Simulation 12
2.2.4 NVT Equilibrium Simulation 13
2.2.5 NPT Equilibrium Simulation 13
2.2.6 Production MD Simulation Run 13
2.3 PROTEIN DYNAMICS ANALYSIS 15
2.3.1 Residues within the drug-binding region 15
2.3.2 Principal Component Analysis (PCA) 15
2.3.3 Silhouette Coefficient 16
2.3.4 K-means clustering 16
2.4 DSDNA PREPARATION FOR RNA SYNTHESIS 18
2.5 RNA SUBSTRATE PREPARATION 20
2.6 NSP16/10 METHYLTRANSFERASE (MTASE) ACTIVITY ASSAY 22
3.RESULTS 26
3.1 DEVELOPING METHODOLOGIES FOR PROTEIN STRUCTURE SELECTION AND ACTIVE SITE IDENTIFICATION: EXEMPLIFIED CHALLENGES AND SOLUTIONS 26
3.1.1 Druggable proteins analysis 26
3.1.2 Integration of Proteins from Multiple Clusters into one MD Simulation 30
3.1.3 Membrane Inclusion for Some Soluble Proteins 34
3.2 PROTEIN STRUCTURE SELECTION AND IDENTIFY ACTIVE SITES OF NSP16/10 36
3.3 LOD SCORE FROM DOCKING RESULTS OF NSP16 37
3.4 MD SIMULATION 39
3.5 CONSERVATION ANALYSIS OF NSP16/10 44
3.6 SSDNA ANNEALING ANALYSIS 50
3.7 DETERMINING DNA TEMPLATES FOR RNA SYNTHESIS 52
3.8 RNA PRODUCT ANALYSIS 55
3.9 ENZYME INHIBITION BY DRDOCK-PREDICTED CANDIDATE DRUGS 57
3.10 CELL TOXICITY BY DRDOCK-PREDICTED CANDIDATE DRUGS 59
3.11 SUPPRESSION OF THE SARS-COV-2 VIRUS BY DRDOCK-PREDICTED CANDIDATE DRUGS 61
4.DISCUSSION 63
5.REFERENCE 65

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