Computational Ligand Modeling for Kinases and COVID-19 Main Protease using Various Methods: Docking, Molecular Dynamics, Free Energy Calculation, 3D-QSAR, and Virtual Screening
- Issued Date
- 2021
- Abstract
- Protein kinases are enzymes that play important role in various cell signaling pathways. Because of their important role in various cellular processes, the dysregulation of kinases has been associated with various cancers and autoimmune diseases. Hence, kinases are regarded as important drug targets. So far 52 kinase inhibitors have been approved by the Food and Drug Administration (FDA) for pharmaceutical use. Despite the success in the development of kinase inhibitors, their use has been limited due to their off-target activity. Hence, the development of potent and selective kinase inhibitors has been an interesting area in drug design. To understand the structural and physicochemical properties that drive the inhibition of protein kinases, the G-protein coupled receptor kinase 2 (GRK2), stem cell factor receptor (c-KIT), and platelet derived growth factor receptor alpha (PDGFRα) have been selected as model targets for computational study. Molecular docking and molecular dynamics simulation were performed to study the protein-ligand binding interactions. 3D-Quantitative structure-activity relationship (3D-QSAR) models were developed to study the relationship between the structure of the compounds and their inhibitory activities. Contour maps generated based on the 3D-QSAR models provided crucial information regarding various favorable and unfavorable substituents. The result of the computational study provided valuable insights that could be used as guidelines in the future development of potent and selective inhibitors.
Following the outbreak of the COVID-19 pandemic in December 2019, the scientific community was faced with the challenging task to develop therapy against the disease. The 3CLpro protease of the SARS-COV-2, the causative agent of COVID-19, has been identified as a drug target due to its unique role in the viral replication. Hence, 3CLpro inhibitors were considered to be promising agents for COVID-19 treatment. We have performed virtual screening of the protease inhibitor database MEROPS for potential 3CLpro inhibitors. Based on the virtual screening, 32 compounds that showed high binding energy values were further accessed for pharmaceutical use. We found 15 potential 3CLpro inhibitors with high binding affinity. Among them, Saquinavir (an approved drug for HIV-1 treatment) and three other investigational drugs namely aclarubicin, TMC-310911, and Faldaprevir could be suggested as potential 3CLpro inhibitors. Aclarubicin is an anthracycline drug used in cancer chemotherapy. TMC-310911 is an antiviral drug and Faldaprevir is an experimental drug under clinical trial for the treatment of hepatitis C disease. Further experimental validation of the compounds is recommended.
The outcome of this study gave a comprehensive understanding of the structural factors important for the inhibition of kinases and COVID-19 main protease.
|단백질카이네이즈는 다양한 세포 신호전달과정에 중요한 역할을 하는 효소이다. 다양한 세포의 과정에 중요한 역할을 하기 때문에, 카이네이즈의 작용을 방해하는 것은 다양한 암과 자가면역질환과 관련되어 있다. 카이네이즈는 중요한 신약개발 타깃이다. 현재까지 미국식품안전청에서 52개의 카이네이즈억제제가 약품의 용도로 승인되었다. 카이에니즈 억제제의 개발의 성공에도 불구하고 부작용 때문에 실제사용은 제한적이다. 따라서, 효능이 우수하고 선택적인 카이네이즈 억제제의 개발은 신약설계에서 중요한 분야이다. 단백질카이네이즈의 억제를 일으키는 구조적이고 물리화학적인 요인을 이해하기 위하여, G-단백질과 연결된 수용체 카이네이즈2(GRK2), 줄기세포인자 수용체(c-KIT), 혈소판유래 성장인자수용체 알파(PDGFRα)등을 계산연구에 대한 타깃으로 선정하였다. 분자 도킹과 분자동력학 시뮬레이션을 이용하여, 단백질과 리간드 결합 상호작용을 연구하였다. 3차원 구조활성상관관계 모델을 만들어서 화합물의 구조와 억제능력간의 관계를 연구하였다. 3차원 QSAR 모델로부터 얻을 수 있는 컨투어 맵을 사용하여 다양한 치환체에 대한 정보를 알아볼 수 있었다. 계산연구의 결과는 더 강력하고 선택적인 억제제를 만드는데 중요한 방향성을 제공한다.
2019년 12월의 COVID19의 발발과 관련하여 과학계에서는 이를 치료해야하는 도적적인 과업에 직면하게 되었다. COVID19를 일으키는 원인물질인 SARS-COV-2의 3CLpro 단백질은, 바이러스의 증식에 대단히 중요한 역할을 하기 때문에 중요한 타깃으로 확인되었다. 따라서, 3CLpro 억제제는 COVID19를 치료하는데 효과적일 것이라고 추정되고 있다. 우리는 이 작용점에 대하여, 단백질 억제제 데이터베이스인 MEROPS를 사용하여 가상검색을 실행하였다. 가상검색의 결과, 32개의 화합물이 강한 결합력을 보였으며, 계속적인 실험으로 검증할 필요가 있다고 생각되었다. 최종적으로 높은 결합에너지를 가지는 15개의 가능성이 큰 3CLpro 억제제를 제안할 수 있었다. 이들 중, Saquinavir (인증된 HIV-1치료제)와 3개의 다른 연구중의 약품들, 즉, aclarubicin, TMC-310911, Faldaprevir 등이 potential 3CLpro 의 억제에 가능성이 큰 것으로 나타났다. Aclarubicin은 화학적 항암요법에 쓰이는 anthracycline 약이고, TMC-130911은 항바이러스 약이다. Faldaprevir은 현재 임상시험중인 C형 간염에 쓸 약이다. 이 화합물들에 대한 계속적이고 실험적인 검증이 필요하다.
본 연구의 결과는 카이네이즈와 COVID 19 주 단백질 분해효소에 대한 억제제의 구조적인 요인을 이해하는데 많은 도움을 주었다.
- Alternative Title
- 다양한 방법론을 사용한 카이네이즈와 Covid19 주단백질 효소에 대한 계산과학적 리간드 모델링: 도킹, 분자동력학, 자유에너지 계산, 3차원 QSAR 및 가상검색
- Alternative Author(s)
- Seketoulie Keretsu
- Department
- 일반대학원 의과학과
- Advisor
- Seung Joo Cho
- Awarded Date
- 2021-02
- Table Of Contents
- CONTENTS
CONTENTS I
ABBREVIATIONS VIII
LIST OF TABLES X
LIST OF FIGURES XIII
ABSTRACT (KOREAN) XVIII
ABSTRACT (ENGLISH) XX
PART I Overview of Protein Kinases 2-4
1. Introduction 2
2. Structure 2
3. Classification of Kinases 3
4. Kinase as Drug Target 3
5. Kinase Inhibitors 4
Part II Docking and 3D-QSAR Studies of Hydrazone and Triazole Derivatives for Selective Inhibition of GRK2 over ROCK2 6- 13
1. Introduction 6
2. Methodology 7
2.1. Dataset 7
2.2. Molecular Docking 8
2.3. CoMFA and CoMSIA 12
2.4. Model Validation 13
3. Results and discussion
3.1. Molecular Docking 14
3.2. 3D-QSAR 16
3.2.1.CoMFA and CoMSIA models for GRK2 17
3.2.2.CoMFA and CoMSIA models for ROCK2 20
3.3 Contour map analysis 21
3.3.1. GRK2 CoMFA contour maps 22
3.3.2 GRK2 CoMSIA contour maps 24
3.3.3. ROCK2 CoMFA contour maps 25
3.3.4. ROCK2 CoMSIA contour maps 26
4. Conclusion 29
PART III Computational study of paroxetine-like inhibitors reveals new molecular insight to inhibit GRK2 with selectivity over ROCK1 41- 73
1. Introduction 41
2. Methodology 44
2.1. Dataset 44
2.2. Protein preparation 48
2.3. Molecular Docking 49
2.4. Molecular Dynamics (MD) simulation 50
2.5. Free energy calculation 50
2.6. 3D-QSAR 51
2.7 Model validation 52
3. Results 53
3.1. Molecular Docking 53
3.2. Molecular Dynamics (MD) Simulation 55
3.3. MM/PBSA based free energy calculations 60
3.4. 3D-QSAR 61
3.5. Contour Map Analysis 64
4. Discussions 66
5. Conclusion 73
PART IV Molecular Modelling Study of c-KIT/PDGFRα Dual Inhibitors for the Treatment of Gastrointestinal Stromal Tumors 76- 111
1. Introduction 76
2. Methodology 78
2.1. Data Preparation 78
2.2. Molecular Docking 79
2.3. Molecular Dynamics Simulation 79
2.4. Evaluation of Binding Energy 80
2.5. 3D-QSAR 81
3. Results 82
3.1. Molecular Docking 87
3.2. Molecular Dynamics Simulation 87
3.3. Evaluation of Binding Energy 89
3.4. 3D-QSAR 93
3.5. Analysis of Contour Map 95
3.6. Designed Compounds 97
4. Discussion 101
5. Conclusion 112
PART V Rational Approach toward COVID-19 Main Protease Inhibitors via Molecular Docking, Molecular Dynamics Simulation and Free Energy Calculation 115- 142
1. Introduction 115
2. Methods 118
2.1. Data preparation 118
2.2. Virtual Screening 119
2.2.1. Surflex-Dock 119
2.2.2. Autodock vina 120
2.3. Molecular docking 120
2.4 Molecular dynamics simulation 120
2.5. Calculation of binding free energy 121
3. Results 122
3.1. Virtual screening 124
3.2. Molecular docking 127
3.3. Molecular Dynamics Simulation 128
3.4. Calculation of binding free energy 129
4. Discussion 132
5. Conclusion 142
PART VI Conclusion 144
REFERENCES 147- 163
APPENDIX
A. List of Publications 163- 164
B. Acknowledgement 165
- Degree
- Doctor
- Publisher
- 조선대학교 대학원
- Citation
- 세게투래 캐렛수. (2021). Computational Ligand Modeling for Kinases and COVID-19 Main Protease using Various Methods: Docking, Molecular Dynamics, Free Energy Calculation, 3D-QSAR, and Virtual Screening.
- Type
- Dissertation
- URI
- https://oak.chosun.ac.kr/handle/2020.oak/16772
http://chosun.dcollection.net/common/orgView/200000369885
- Appears in Collections:
- General Graduate School > 4. Theses(Ph.D)
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