Understanding the ligand interactions in the cancer therapy and Toll-like receptors
Understanding the ligand interactions in the cancer therapy and Toll-like receptors
- 주제(키워드) Medical Science (Specialization in Bioinformatics)
- 발행기관 아주대학교
- 지도교수 Sangdun Choi
- 발행년도 2017
- 학위수여년월 2017. 8
- 학위명 박사
- 학과 및 전공 일반대학원 분자과학기술학과
- 실제URI http://www.dcollection.net/handler/ajou/000000025772
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Cytarabine, daunorubicin, doxorubicin, and vincristine are used as a chemotherapy medication used to treat acute lymphocytic leukemia, chronic myelogenous leukemia, and non-Hodgkin's lymphoma. But, the metabolism of these drugs as well as the binding modes with cytochrome P450 are unexplored both computationally as well as in other wet-lab experiments. Hence, we utilized protein-ligand docking to predict the productive as well as nonproductive binding modes. We used both rigid (Autodock) and flexible docking (MOE) in our analysis. We also used SmartCyp web server to predict the plausible metabolic sites in the drugs. Based on the consensus docking results as well as the plausible metabolic sites we divided the binding modes into productive as well as nonproductive binding modes. Moreover, we selected both productive and nonproductive binding modes for MD simulations. We determined Ser119, Arg212, and Arg72 were the critical residues that interact with CYP34A. The significants of the hydrophobic forces in the drug interactions were observed in the analysis. Further, the predicted amino acid residues were observed to be essential in site-directed experiments. The productive and nonproductive binding modes of the drugs may broaden the understanding as well as provide the way to design drugs with less toxicity. Additionally, our analysis adds the knowledge of metabolism of the selected cancer drugs. High mobility group box protein 1 (HMGB1) plays a critical role in autoimmune diseases. It is an adequate, conserved nuclear protein. HMGB1 has an intracellular function as well as in few circumstance (e.g. necrosis) it plays a critical role in cytokine activation. The activity of HMGB1 regulated (extracellular) by the redox-sensitive cysteines namely Cys23, Cys45, and Cys106. We have built models as well as did Molecular Dynamic simulations for different states of HMGB1 including the mutants. Also, we did protein-protein interactions, to understand the interaction of HMGB1 with TLR4. The simulations reveal that the redox states affects the domain movement of the protein. The change in the domain movement, in turn, has an impact on the cytokine activity. The Free energy landscape suggests the lowest energy structure for the active and inactive redox states. Moreover, we docked the inactive as well as active HMGB1 with Toll-like receptor 4 to understand the structural role. These interactions provided the significant insights that might be helpful in several autoimmune diseases. Toll-like receptor 8 (TLR8) modulators are an attractive target for treating cancer as well as other inflammatory diseases. Several agonists and antagonists have been reported in the literature. We have used and built the QSAR models using the reported agonists to screen novel modulators. The models were built using the rigorous approach and did an external five-fold cross-validation. We screened around eight million compounds and selected 2000 small molecules based on similarity fingerprints. The selected small molecules were used to screen against the built models. The consensus small molecules from six models were carefully selected. We tested 15 compounds and found one antagonists (named as X5) in the range of 50 µM without any toxicity. This lead molecule will be helpful in designing drug molecule for various inflammatory diseases. TNF-α level was measured and the compound X5 significantly repressed TNF-a production at 50 μM..
more목차
CHAPTER 1 Predicting the productive and non-productive binding modes of cancer drug interaction with cytochrome P450 (CYP3A4) through docking and molecular dynamic simulation 1
1.1 Abstract 2
1.2 Introduction 3
1.3 Results 4
1.4 Discussion 11
1.5 Method 14
1.6 Conclusion and future prospects 15
CHAPTER 2 Understanding the TLR4/HMGB1 interactions using docking and molecular dynamic simulations 27
2.1 Abstract 28
2.2 Introduction 29
2.3 Results and Discussion 30
2.4 Method 35
2.5 Conclusion and future prospects 35
CHAPTER 3 A machine-learning approach to predict novel TLR8 antagonists 52
3.1 Abstract 53
3.2 Introduction 54
3.3 Results 56
3.4 Discussion 58
3.5 Method 60
3.6 Conclusion and future prospects 62
4. References 76
5. Publications 82
