抽象：
過去幾十年來，從 HIV，HCV，DENV 病毒感染到最近的 ZIKA，流感，埃博拉病毒和當前的 COVID-19 大流行，傳染性病毒疾病一直持續影響著人類。典型的病毒性疾病（或感染）發生在生物體被病原性病毒入侵時，傳染性病毒顆粒（病毒體）附著並進入易感細胞，進一步導致病毒基因組和蛋白質組複製，隨後組裝並釋放出大量病毒副本。這些病毒儘管具有遺傳材料（DNA / RNA），進入和傳染機制，影響器官系統和傳播方式（空氣，液體或載體傳播），但仍具有一些共同的基本特徵和進入人體宿主的機制。在許多病毒中，病毒複製中的此類關鍵步驟之一是將病毒基因組翻譯的多蛋白裂解為功能性病毒蛋白。該關鍵的複制步驟由病毒蛋白酶進行。病毒蛋白酶是具有不同催化機制的催化酶（內切肽酶 EC 3.4.2），涉及絲氨酸，半胱氨酸或天冬氨酸殘基，以攻擊可裂解的肽鍵。因此，它們代表了阻斷病毒複製的有吸引力且有效的抗病毒靶標。另一方面，在當前除聚合酶抑製劑之外的抗病毒治療藥物中，一種主要批准的抗病毒藥物類別是靶向和阻斷病毒蛋白酶活性的蛋白酶抑製劑（例如：HIV 蛋白酶抑製劑藥物）。然而，目前在蛋白酶藥物的發現中，除了針對艾滋病毒和其他幾種病毒的蛋白酶抑製劑藥物之外，黃病毒科的大多數病毒（如 DENV，WNV，ZIKV 病毒）也是如此。冠狀病毒科，例如 SARS，MERS 和 COVID-19 病毒，目前尚無蛋白酶抑製劑藥物被批准。靶向病毒蛋白酶的蛋白酶藥物發現具有挑戰性，以抑制通過小分子藥物天然結合肽底物的活性位點腔。此外，儘管對病毒蛋白酶（例如黃病毒科）進行了大量的工作和研究，但缺乏系統的藥物設計和發現策略已成為阻礙。抑製劑分子還將病毒蛋白酶殘基作為靶標也更容易發生突變，這些突變很容易幫助病毒發現對抑製劑的抗性。該領域最重要但最具挑戰性的目標是最具吸引力但很難實現的泛病毒抑製劑（針對多種病毒的抑製劑）的聖杯。
為了解決這些科學問題，我們構建了一個系統的病毒蛋白酶藥物發現平台，以採用“ Pharmacophore Anchor”策略（目前主要針對黃病毒科病毒）來實現泛病毒蛋白酶抑製劑的目標（例如：DENV，WNV，JEV ，ZIKV）”和冠狀病毒科（例如：SARS-CoV-2，SARS-CoV， MERS-CoV）。藥效基團錨代表支持抑製劑分子結合的蛋白質表面上可藥用的熱點結合環境。給定活動位點腔的一組 Pharmacophore 錨點熱點包括病毒蛋白酶的“藥效基團錨點模型”。例如，對於使用藥效基團錨定靶向黃病毒 NS3 病毒蛋白酶的情況，我們建立了 HCV， DENV，WNV，JEV 和 ZIKV 感染性病毒的黃病毒蛋白酶的藥效基團錨定（PA）模型。這是通過 Big Compound 文庫對接到蛋白酶活性位點並總結殘基-化合物相互作用圖譜並對其進行分析以找到共有亞位點（殘基）-部分（化合物）藥效基團相互作用（被指定為Pharmacophore 錨點）而實現的。對於給定的病毒蛋白酶，Pharmacophore 錨（PA）模型錨描述了在空間上排列在活性位點上的所有 Pharmacophore 錨，具有以下特徵：錨類型
（E-H-V），錨殘基和部分偏好。發現的病毒蛋白酶藥理學錨和錨特徵的知識已用於虛擬篩選，基於錨的虛擬篩選用於發現病毒蛋白酶抑製劑。與傳統的基於對接能量的篩查相比，這種由藥典錨指導的計算機篩選方法具有更高的性能。此外，為使藥物重新利用，我們特意針對病毒蛋白酶篩選了 FDA 藥物。為了驗證病毒蛋白酶的 Pharmacophore 錨（例如：DENV， ZIKV）及其在錨固增強虛擬篩選中的實用性，我們對發現的抗病毒藥物候選物進行了實驗測試。我們發現–previr 類 FDA 藥物在體外抑制 DENV 和 ZIKV NS3 蛋白酶。我們通過斑塊試驗檢測到 FDA 藥物 Asunaprevir 和 telaprevir 抑制 DENV 病毒複製，其 EC50 值分別為10.4μM 和 24.5μM。此外，通過測試針對 ZIKV 的抗病毒候選物，我們發現 FDA 藥物Asunaprevir 和 Simeprevir 具有有效的抗 ZIKV 活性，EC50 值為 4.7μM 和 0.4μM。此外，發現這些–previr 藥物可直接和有效抑制 ZIKV NS3 蛋白酶，IC50 值分別為 6.0μM 和 2.6μM。對目前導致 SAV-CoV-2 大流行的 SARS-CoV-2 的冠狀病毒 3CL 蛋白酶採用了類似的方法，產生了–previr 藥物的抗病毒活性，Boceprevir 的 IC50 為 1.42 µM，EC50 為 49.89 µM，而Telaprevir 則表明–中等的蛋白酶抑製作用，IC50 為 11.47 µM。
我們針對這些特定蛋白酶的 Pharmacophore 錨也可以描述和指導研究可將錨與觀察到的抑制活性聯繫起來的結構-錨定-活性關係（SAAR）。此外，對於黃病毒蛋白酶，我們發現了核心 Pharmacophore 錨，它代表了整個蛋白酶的核心和高度保守的蛋白酶結合環境/熱點，能夠促進泛病毒蛋白酶抑製劑靶向泛病毒蛋白酶。黃病毒蛋白酶的 PA 模型和結果證明，“ 5種核心藥理錨 CEH1，CH3，CH7，CV1 和 CV3”在黃病毒科家族中提供了泛病毒蛋白酶結合，而冠狀病毒蛋白酶的 PA 模型和結果家族發現“ 3 核心藥理錨 EHV2，HV1 和 V3”在冠狀病毒科內部提供了泛病毒蛋白酶結合。對於這兩種情況，錨模型都極大地幫助發現了
“ –previr FDA 藥物”作為泛病毒蛋白酶抑製劑藥物在家庭內部和家庭中起作用。同樣，每種病毒蛋白酶的 Pharmacophore 錨模型有助於指導發現的先導分子的先導優化，從而產生具有納摩爾效率的有效抑製劑。以及目前將 FDA 藥物 Telaprevir 和 Asunaprevir 重新用於 DENV感染的用途； Asunaprevir 和 Simeprevir 用於 ZIKV 感染； Boceprevir 和 Telaprevir 用於SARS-CoV-2 感染，顯示出有望加快對病毒感染患者的治療，從而增強對抗這些病毒感染性疾病的能力。

ABSTRACT:
Infectious viral diseases have been persistently affecting humans for centuries, from HIV, HCV, DENV virus infection in the past decades to the recent ZIKA, influenza, Ebola, and the current COVID-19 pandemics. A typical viral disease (or infection) occurs when an organism's body is invaded by pathogenic viruses, and infectious virus particles (virions) attach and enter susceptible cells further leading to replication of the viral genome and proteome, followed by assembly and release of numerous viral copies. These viruses despite their genetic material (DNA/RNA), entry, and infectious mechanisms, affecting organ systems and modes of transmission (air, fluid or vector-borne) but still have some common basic features and entry mechanisms into their human host. Among many viruses, one such key step in viral replication is the cleavage of the virus genome translated polyprotein into functional virus proteins. This critical replication step is carried by the virus protease. Viral proteases are catalytic enzymes (endopeptidases EC 3.4.2) with different catalytic mechanisms involving either serine, cysteine, or aspartic acid residues to attack the scissile peptide bond. Thus they represent an attractive and effective antiviral target in blocking viral replication. On the other hand, amongst the current antiviral therapy drugs in adding to polymerase inhibitors, one major approved category of antiviral pharmaceuticals are the protease inhibitors that target and block the virus protease activity (ex: HIV protease inhibitor drugs). However currently in the protease drug discovery, apart from protease inhibitor drugs targeting HIV and few other viruses, for a majority of the viruses of the flaviviridae family like DENV, WNV, ZIKV viruses; Coronaviridae family like SARS, MERS, and COVID-19 viruses, no proteases inhibitor drugs have been approved as of yet. The protease Drug Discovery targeting the virus proteases has been challenging, to inhibit the active site cavity which naturally binds to peptide substrates by small molecule drugs. Moreover, despite much work and studies on the virus proteases (ex: flaviviridae), a lack of systematic drug design and discovery strategy had been a holdback. Also targeting the virus protease residues by the inhibitor molecules is more prone to mutations that easily can help the virus discover resistance against the inhibitors. The most important but challenging aim in the field is the holy-grail of pan-virus inhibitors (inhibitors targeting multiple viruses) that are most attractive but very difficult to achieve.
To address these scientific issues, we constructed a systematic Drug Discovery platform of the Virus proteases to achieve the Goal of Pan-virus Protease Inhibitors employing a “Pharmacophore Anchor” strategy currently focusing on the viruses of family flaviviridae (ex: DENV, WNV, JEV, ZIKV)” and the Coronaviridae (ex: SARS-CoV-2, SARS-CoV, MERS-CoV). Pharmacophore anchors represent the druggable hotspot binding environments on a protein surface that support the binding of the inhibitor molecules. A set of Pharmacophore anchor hotspots for a given active site cavity comprise “a pharmacophore anchor model” of the virus protease. For example, for the case of targeting the flavivirus NS3 virus proteases using pharmacophore anchors, we build the pharmacophore anchor (PA) models for the flaviviral proteases of HCV, DENV, WNV, JEV, and ZIKV infectious viruses. This was achieved by Big Compound libraries docking into the protease active sites and summarizing the residue-compound interaction profiles and analyzing them to find the consensus subsite (residue) –moiety (compound) pharmacophore interactions which were assigned as Pharmacophore anchors. For a given viral protease, the Pharmacophore anchor (PA) model anchors depict all the Pharmacophore anchors spatially arranged at the active site with features: anchor types (E-H-V), anchor residues, and moiety preferences. The discovered Viral Protease Pharmacophore anchors and the knowledge of the anchor features have been applied for the virtual screening, Anchor based Virtual Screening for discovering virus protease inhibitors. This in silico screening method guided by Pharmacophore anchor has improved performance over the traditional Docking Energy-based screening. Furthermore, to enable drug repurposing we specifically screened FDA Drugs against the virus proteases. To verify the Pharmacophore anchors of the virus proteases (Ex: DENV, ZIKV) and their utility in the Anchor-Enhanced Virtual Screening, we performed experimental testing of the antiviral drug candidates that have been discovered. We discovered –previr class of FDA drugs inhibiting the DENV and ZIKV NS3 proteases in vitro. We discovered FDA drugs asunaprevir and telaprevir inhibiting DENV viral replication as tested by plaque assays with EC50 values of 10.4 μM & 24.5 μM respectively. Moreover, upon testing the antiviral candidates against the ZIKV, we discovered FDA drugs Asunaprevir and Simeprevir to have potent anti-ZIKV activities with EC50 values 4.7 μM and 0.4 μM. Furthermore, these –previr drugs were found directly and potently inhibiting the ZIKV NS3 protease with IC50 values 6.0 μM and 2.6 μM respectively. A similar approach was applied on the coronavirus 3CL protease of the current SARS-CoV-2 causing COVID-19 pandemic, which yielded antiviral activities of –previr drugs, Boceprevir showing an IC50 of 1.42 µM and an EC50 of 49.89 µM, while Telaprevir showing a moderate protease inhibition only with an IC50 of 11.47 µM.
Our Pharmacophore anchors for these specific proteases could also describe and guide to study the structure-anchor-activity relationships (SAAR) that could link the anchors to observed inhibitory activities. In addition, to the flaviviral proteases, we discovered the Core Pharmacophore anchors, that represents the core and highly conserved protease binding environments/hotspots across protease that is capable to facilitate pan-virus protease targeting by pan-virus protease inhibitors. The PA models and Results for the flavivirus protease have proved that the “5 Core Pharmacophore anchors CEH1, CH3, CH7, CV1, and CV3” offering pan-virus protease binding within the flaviviridae family, and the PA models and Results for the coronavirus protease family discovers “3 Core Pharmacophore anchors EHV2, HV1, and V3” offer pan-virus protease binding within the coronaviridae family. For both the cases, the anchor model immensely aided the discovering of the “–previr FDA drugs” as Pan-virus Protease inhibitor drugs working within and across families”. Also, the Pharmacophore anchor models for each viral protease helps in guiding the lead optimization of the discovered lead molecules to give rise to potent inhibitors with nano-molar efficacies. Also the current repurposing of FDA drugs Telaprevir and Asunaprevir for DENV infection; Asunaprevir and Simeprevir for ZIKV infection; Boceprevir and Telaprevir for SARS-CoV-2 Infection, show promise to speed up the treatment of virus-infected Patients boosting up the fight against these viral infectious diseases.

CONTENTS
Abstract 4
CHAPTER 1: Introduction
1.1 Background:
1.1.1 Viral infections. 8
1.1.2 Viral Proteases as drug targets. 9
1.1.3 Introduction of protease families. 10
1.2 Current State and Challenge of Pan-Virus Protease Drugs. 13
1.3 Hypothesis and Aim 14
CHAPTER 2: Flavivirus NS3 Protease Pharmacophore Anchor (PA) Models for Anti-DENV Drug Discovery and Repurposing.
2.1 Introduction 20
2.2 Materials and Methods:
2.2.1 Overview of the strategy for targeting flaviviral NS3 proteases. 21
2.2.2 Preparation of DENV, WNV, JEV, HCV NS3 proteases & compound datasets. 22
2.2.3 Building DENV, WNV, JEV and HCV NS3 protease PA models and finding pharmacophore anchors. 23
2.2.4 Identification of flaviviral protease ‘Core and Specific anchors’. 24
2.2.5 Anchor-based virtual screening for anti-DENV drugs and repurposing. 25
2.3 Results:
2.3.1 The DENV, WNV, JEV and HCV NS3 protease PA models. 27
2.3.2 Core pharmacophore anchor (CPA) model of the flaviviral NS3 proteases. 30
2.3.3 Specific anchors of the DENV NS3 protease. 34
2.3.4. Validation of the PA/CPA model by anchor residue evolutionary conservation & mutational analysis. 35
2.3.5 DENV protease inhibitor candidates and anti-DENV FDA drugs. 42
2.4 Summary 47
CHAPTER 3: ZIKV NS3 Protease Pharmacophore Anchor (PA) Models for Anti-ZIKV Drug Discovery and Repurposing.
3.1 Introduction 50
3.2 Materials and Methods:
3.2.1 Overview of the strategy for targeting ZIKV NS3 Protease. 51
3.2.2 Preparation of ZIKV NS3 protease and compound datasets. 55
3.2.3 Mining pharmacophore anchors abd building ZIKV NS3 protease PA model. 55
3.2.4 Identification of ZIKV ‘Core and Specific anchors’. 56
3.2.5 Application in Anchor-enhanced virtual screening for anti-ZIKV drugs. 58
3.3 Results:
3.3.1 The ZIKV NS3 protease PA model. 58
3.3.2 Core anchors in ZIKV and Specific ZIKV NS3 protease anchors. 61
3.3.3 Protease substrate/inhibitor binding mechanisms explored with anchors. 64
3.3.4 ZIKV protease inhibitor candidates and anti-ZIKV FDA drugs. 70
3.4 Summary 77

CHAPTER 4: SARS-CoV-2 3CL Protease Pharmacophore Anchor (PA) Models for Anti-SARS-CoV-2 Drug Discovery and Repurposing.
4.1 Introduction 80
4.2 Materials and Methods:
4.2.1 Overview of the strategy for targeting SARS-CoV-2 3CL Protease. 81
4.2.2 Preparation of SARS-CoV-2 3CL protease and compound datasets. 83
4.2.3 Building SARS-CoV-2 3CL Protease Pharmacophore clusters (PPCs) by TSCC. 83
4.2.4 Identification of ‘Core and Consensus anchors’ of SARS-CoV-2 3CL proteases. 84
4.2.5 Application in anchor-derived virtual screening for anti-COVID-19 FDA drugs. 84
4.3 Results:
4.3.1 The SARS-CoV-2 3CL Protease Pharmacophore Clusters (PPCs). 85
4.3.2 The SARS-CoV-2 3CL PPC anchor maps. 93
4.3.3 PPC Core and Consensus anchors of SARS-CoV-2 3CL proteases. 94
4.3.4 SARS-CoV-2 protease inhibitors and COVID-19 FDA drugs. 97
4.4 Summary 107
CHAPTER 5: Conclusion:
5.1 Summary of major contributions and discoveries. 108
5.2 Future works. 113
List of Publications 115
References 116

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