Structural prediction of the adenosine A2A receptor and comparison of its resting state and active state conformations

在最近，活化的腺甘酸A2A受體（adenosine A2A receptor）被確認為是治療亨丁頓舞蹈症(Huntington's Disease)的目標受體之ㄧ。在分類上，腺甘酸A2A受體屬於G蛋白質結合受體(GPCR)中視紫質(rhodopsin)相似家族的一個受體。 腺甘酸A2A受體在結構上由７個α－螺旋組成，每一個螺旋的長度大約為２５個胺基酸的長度。直到現今，腺甘酸A2A受體的立體結構都還沒有被用實驗的方法解出來。因為在結構生物學的領域中，用實驗的方法解出膜蛋白（membrane protein）的結構仍然是一件挑戰的工作。除了實驗以外解蛋白質結構的方法是利用同源性模擬法。在本研究中，我們使用牛的視紫質結晶結構（PDB code: 1U19, 2.2Å）當作同源性模擬法的結構模板來建構腺甘酸A2A受體（1-301）。除了C-tail（302-410），腺甘酸A2A受體的結構是使用軟體MODELLER9V1來建構的。在同源性模擬法的序列排序的步驟中，我們把視紫質的序列和腺甘酸受體家族（A1, A2A, A2B, A3）一起排序，為的是把演化的關係考慮在排序中以增進排序的準確度。腺甘酸A2A受體的C-tail(302-410)是使用TASSER-Lite網站,使用摺疊辨識法(Fold recognition)來建構的。當得到腺甘酸A2A受體的全長的結構後，我們把受體結構放進脂質－水的系統中進行2ns的分子動力學模擬使用AMBER9。同時，我們也使用Catalyst建立腺甘酸A2A受體的增效劑(agonist), 對抗劑(antagonist)的藥效基團(pharmacophore)的模型。然後，和藥效基團疊合的最有藥效的增效劑和對抗劑的構形被分別選出並使用Autodock3把這2種構形的化合物和腺甘酸A2A受體嵌合。最後，我們進行10ns的分子動力學模擬在腺甘酸A2A受體-對抗劑，腺甘酸A2A受體-增效劑，腺甘酸A2A受體-ＮＭＲ－構形限制對抗劑，腺甘酸A2A受體-ＮＭＲ－構形限制對抗劑。詳細的腺甘酸A2A受體-配位體的交互作用力分析將可以幫助設計更有藥效的腺甘酸A2A受體的抑制劑或是活化劑。The activation of human adenosine A2A receptor has recently been identified as a candidate target for designing therapeutics for the Huntington disease. Adenosine A2A receptor belongs to the GPCR, rhodopsin-like superfamily. It consists of 7 transmembrane α-helices, and each is about 25 residues in length. Nowadays, the three-dimensional structure of adenosine A2A receptor obtained from experimental methods is still unavailable. The determination of membrane protein structures via experimental approaches remains a major challenge in the field of structural genomics. An alternative approach to building a molecular model of a protein is from homology modelling procedure. We used bovine rhodopsin crystal structure (PDB code: 1U19, 2.2Å) as homology modelling template to construct the adenosine A2A receptor (1-301) except its c-tail using MODELLER9v1. In the alignment step, we aligned all the rat adenosine receptor family sequences (A1, A2A, A2B, and A3) with rhodopsin sequence to take evolutionary relationship into account and improve the alignment accuracy by using CLUSTALW. The exceptional long c-tail structure of adenosine A2A receptor (302-410) was modeled from best model of TASSER-Lite web server using the fold-recognition approach. After the full-length adenosine A2A receptor structure has been constructed, we put the receptor into lipid-water environment to run 2-ns molecular dynamics simulation refinement using AMBER9. We also made the pharmacophore model of adenosine A2A receptor agonist and antagonist, respectively, using Catalyst®. Then, the most potent agonist and antagonist conformations which fitted its pharmacophore model best were selected. The chosen agonist and antagonist conformations were docked with adenosine A2A receptor using Autodock3. Finally, we made 1０-ns molecular dynamics simulations of receptor with an antagonist, receptor with a NMR-restraint antagonist, receptor with an agonist, and receptor with an NMR-restraint agonist, to analyze the receptor-ligand binding interactions. The detailed knowledge of the binding locations will help to design more potent inhibitors for this receptor.Table of Contents Figure Lists I Table Lists V Abbreviations: VII 中文摘要 VIII Abstract: X Chapter 1: Introduction 1 1.1 Adenosine as endogenous ligand of A2AR 1 1.2 Classification of A2AR in the GPCR superfamily 1 1.2.1 Structural topology in rhodopsin-like family A 2 1.2.2 Adenosine receptors subfamily 3 1.2.3 Putative A2AR binding pocket 4 1.4 Proposed activation mechanism of GPCRs 6 1.4.1 Molecular mechanisms involved in the activation of GPCRs 6 1.4.2 Two-state model 6 1.4.3 Sequential binding and conformational stabilization model 7 1.5 Adenosine A2A receptor as therapeutically target 8 Chapter 2: Materials and Methods 10 2.1 Modeling flowchart and screw-in dynamics scheme 10 2.2 Generation of pharmacophore models 12 2.2.1 Structure-activity relationship of A2AR ligands in the training sets 13 2.2.2 Generation of A2AR antagonist pharmacophore model 17 2.2.3 Generation of A2AR agonist pharmacophore model 20 2.3 Pharmacophore model validation 23 2.3.1 Cost analysis 23 2.4 Rat A2AR modeling 24 2.4.1 Comparison of current available rat A2AR model 25 2.4.2 Build full-length structure of rat A2AR 35 2.5 Insert ligands into rat A2AR models 41 2.6 Rat A2AR MD refinement 41 2.6.1 System preparation 42 2.6.2 Standard MD simulation scheme 44 2.6.3 Screw-in MD simulation scheme 44 2.7 Clustering trajectories of MD simulations 45 Chapter 3: Results and Discussions 47 3.1 Results of pharmacophore models 47 3.1.1 Results of antagonist pharmacophore model 47 3.1.2 Results of agonist pharmacophore model 51 3.2 Results of A2AR structure modeling validation 55 3.3 Molecular docking 56 3.3.1 Results of antagonist docking with A2AR 56 3.3.2 Results of agonist docking with A2AR 58 3.4 Results of MD refinements 60 3.4.1 Antagonist-bound rat A2AR with standard MD simulation 60 3.4.2 Agonist-bound rat A2AR with standard MD simulation 68 3.4.3 Antagonist-bound rat A2AR with screw-in MD simulation 78 3.4.4 Agonist-bound rat A2AR with screw-in MD simulation 89 3.5 Principle component analysis of MD trajectories 98 3.5.1 Essential dynamics of antagonist-bound A2AR screw-in MD simulation 100 3.5.2 Essential dynamics of agonist-bound A2AR screw-in MD simulation 101 3.6 Ligand binding mode analysis 101 Chapter 4: Conclusions 108 References: 10