Molecular study of interactions of mu opioid receptor with biased and unbiased ligands by molecular dynamic simulation

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN KY THANH MOLECULAR STUDY OF INTERACTIONS OF MU-OPIOID RECEPTOR IN BINDING WITH BIASED AND UNBIASED LIGANDS BY MOLECULAR DYNAMIC SIMULATION MASTER THESIS MASTER PROGRAM IN NANOTECHNOLOGY Hanoi - 2019 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN KY THANH MOLECULAR STUDY OF INTERACTIONS OF MU-OPIOID RECEPTOR IN BINDING WITH BIASED AND UNBIASED LIGANDS BY MOLECULAR DYNAMIC SIMULATION MAJOR: NANOTECHNOLOGY CODE: PILOT RESEARCH SUPERVISOR: Assoc Prof Dr NGUYEN THE TOAN ACKNOWLEDGMENT I wish to express my sincere gratitude and thanks to my supervisor Assoc Prof Dr Toan The Nguyen for all his patient and supportive instructions and encouragement as well as the necessary facilities for the research His enthusiasm, patience and immense knowledge have helped me greatly I also wish to thank Prof Kei Yura, Ochanomizu University, Japan, for his guidance during the internship period I also would like to extend my special thanks to the professors and Ms Nguyen Thi Huong of Master program of Nanotechnology at Vietnam Japan University for your kindness assistance at all times I kindly acknowledge the Japan International Cooperation Agency (JICA) and VNU Vietnam Japan University for providing me the financial support to complete my study Beside, I am so greatful for my friends in VNU Vietnam Japan University and VNU Key Laboratory on Multiscale Simulation of Complex Systems for sharing expertise, valuable guidance and encouragement extended to me Finally, I would like to express my sincere thanks to Mr Lam for all his patience and kindness Hanoi, 10 June 2019 Tran Ky Thanh Contents Contents i LIST OF TABLES iii LIST OF FIGURES iv LIST OF ABBREVIATIONS vi OVERVIEW Chapter INTRODUCTION 1.1 Opioid painkillers and their side effects 1.2 µ-opioid receptor 1.3 Biased signaling 1.4 Biased and unbiased ligand of mu opioid receptor Chapter METHODOLOGY 10 2.1 Molecular dynamics (MD) simulation 10 2.2 Docking by Autodock 4.2.6 and AutoDockTools 11 2.3 Interaction free energy estimation by MM-PBSA method 12 Chapter RESULTS AND DISCUSSION 15 3.1 Setting up the simulation systems 15 3.1.1 Modeling 15 3.1.2 Docking 18 3.1.3 MD simulation 20 i 3.2 Rout mean square deviation (RMSD) 21 3.3 Cluster analysis 23 3.4 Binding free energy calculated by g_mmpbsa 24 3.5 Root mean square fluctuation (RMSF) 25 3.6 Binding sites 27 3.7 Conformations of mORs 29 3.8 Interaction with Gα protein 32 Chapter CONCLUSIONS 34 REFERENCES 35 ii LIST OF TABLES Table 1.1: Properties of Opioid Receptors [34] Table 3.1: Binding free energy in terms of Experiment and g_mmpbsa estimation 25 iii LIST OF FIGURES Figure 1.1: Countries consuming the most opioids [33] Figure 1.2: Drug overdose deaths involving any opioid, number among all ages, by gender, 1999-2017 [5] Figure 1.3: Percentage of drugs targeting GPCR family Figure 1.4: Features of class A GPCR [31] Figure 1.5: G proteins signal pathway [18] Figure 1.6: β-arrestin signal pathway [3] Figure 1.7: Illustration for the effects of biased and unbiased ligand when binding to protein [24] Figure 2.1: Illustration for ligand-protein binding 12 Figure 2.2: Thermal dynamics cycle for calculation of ligand-protein binding energy 13 Figure 3.1: µOR (red) and Gα protein (blue) 15 Figure 3.4: The area of membrane during 50ns MD simulation 16 Figure 3.3: the membrane after 50ns MD simulation from top view (left) (red:cholesterol, purple: DPPC, lime: DOPE) and side view (right) .17 Figure 3.2: the membrane downloaded from MEMBUILDER server from top view (left) (red:cholesterol, purple: DPPC, lime: DOPE) and side view (right) 17 Figure 3.5: Structure of M6G (left) and TRV130 (right) 18 Figure 3.6: Histogram of clustering analysis of M6G 19 Figure 3.7: Histogram of cluster (left) and cluster (right) with respect to binding energy The red line represents the mean value of binding energy of that cluster 19 Figure 3.8: Histogram of clustering analysis of TRV130 20 Figure 3.9: Histogram of cluster (left) and cluster (right) with respect to binding energy The red line represents the mean value of binding energy of that cluster 20 Figure 3.10: The system including proteins, membrane, ligand, ions from top view (left) and side view (right) 21 Figure 3.11: RMSD of backbone µOR 22 Figure 3.12: RMSD of ligands 23 Figure 3.13: The cluster size diagrams of TRV130 (left) and M6G (right) system 24 Figure 3.14: Cluster ID during simulation time of TRV130 (left) and M6G (right) complex 24 Figure 3.15: RMSF of µOR 26 Figure 3.16: RMSF of Gα 26 Figure 3.17: The interaction between M6G (left) and TRV130 (right) with µOR (plot by LigPlot+ [21]) 27 Figure 3.18: Map of residues interacting with M6G (pink) and TRV130 (green) 28 Figure 3.19: Number of hydrogen bonds between M6G and µOR 29 iv Figure 3.20: Number of hydrogen bonds between TRV130 and µOR 29 Figure 3.21: The difference in structure of TM1 (a), TM5 (b), TM6 (c), TM7 (d) 30 Figure 3.22: Secondary structure of µOR in M6G (left) and TRV130 (right) complexes analyzed by DSSP [33] 31 Figure 3.23: Conformation of µORs in binding with M6G (left) and TRV130 (right) (forcusing on TM6 and TM7, green-colored residue is PRO295 and GLY325) 32 Figure 3.24: Difference in Helix of Gα (other parts of Gα were hiden) 33 v LIST OF ABBREVIATIONS GPCR G protein coupled receptor OR Opioid receptor 7TMR Seven transmembrane receptor TM Transmembrane helix ICL Intracellular loop ECL Extracellular loop GDP Guanosine diphosphate GTP Guanosine triphosphate GRK G protein coupled receptor kinase MD Molecular dynamics LGA Lamarckian genetic algorithm MM-PBSA Molecular mechanics Poisson−Boltzmann surface area M6G Morphine-6-glucuronide M3G Morphine-3-glucuronide RMSD Root mean square deviation RMSF Root mean square fluctuation vi OVERVIEW Every year, millions of pain relief drugs prescriptions are written, and many of them are opioids Opioids are among the most strong pain relief in clinical use, but their analgesic effect is accompanied with many serious adverse effects, such as constipation, nausea, vomiting, respiratory depression, and addiction Opioids overdose has been resposible for thousands of deaths every year These severe issues have been the driving force behind the development new effective painkillers which create less side effects Opioids creates their effects mainly by binding to mu-opioid receptors (µOR) They are considered as unbiased µOR ligands which non-selectively activate µOR in both the β-arrestin signaling pathway inducing side effects and the G-protein signaling pathway responsible for analgesia A novel drug, TRV130, is a biased µOR ligand so activates G-protein signal transduction with less β-arrestin recruitment Consequently, TRV130 provides higher pain relief and reduces side effects Due to interaction with morphine and TRV130, µOR adopt different conformations, this lead to the different performance of these two drugs To elusidate the mechanism of biased signaling, we discovered the conformational difference of µOR in binding with morphine (unbiased ligand) and TRV130 (biased ligand) by performing MD simulation This research calculated the binding free energy of ligands and protein, revealed the interaction of µOR with biased and unbiased ligands These results would be beneficial for future research, the design of painkillers targeting µOR Table 3.1: Binding free energy in terms of Experiment and g_mmpbsa estimation Van der Waal interaction (kJ/mol) Electrostatic interaction (kJ/mol) g_mmpbsa ΔG (kJ/mol) * Experimental ΔG (kJ/mol) (*) Experimental binding energy is extracted from koff/kon in Supplemental Table -1 -1 where R = 8.314 J mol K or of this paper [7] by the formula 0.008314 kJ mol-1 K-1 and T is the temperature 3.5 Root mean square fluctuation (RMSF) Root mean square fluctuation (RMSF) measures the fluctuation of each residue during simulation [ th Where ri(tj) is the coordinate of i atom in time tj, ri ∑|() ref is the coordinate of reference structure, T is period of time we want to calculate RMSF of µOR shows that the ICL3 (intracellular loop 3), TM6 (transmembrane helix 6), ECL3 (extracellular helix 3), TM7 of µOR in M6G-µOR fluctuate much more than µOR in TRV130-µOR (Figure 3.13) Figure 3.15: RMSF of µOR Figure 3.16: RMSF of Gα 3.6 Binding sites Two ligands have different binding sites M6G interacts with TM3 (TYR148, MET151), TM5 (GLU229, LYS233), TM6 (TYR299, VAL300, LYS303), ECL3 (GLN310), TM7 (GLN314, TRP318) While TRV130 interact with TM2 (Gln124, Asn127), ECL1 (Trp133), TM3 (Asp147), TM6 (Trp293, His297), TM7 (Trp318, Ile322, Gly325, Tyr326) (Figure 3.13, Figure 3.13) Figure 3.17: The interaction between M6G (left) and TRV130 (right) with µOR (plot by LigPlot+ [21]) Figure 3.18: Map of residues interacting with M6G (pink) and TRV130 (green) During simulation time, M6G creates about to hydrogen bonds/a frame with mOR (Figure 3.19), whereas hydrogen interaction between TRV130 and mOR is to bonds each frame (Figure 3.20) This is resulted from the differences in the number of acceptors and donors of ligands, M6G has pairs of donors and 10 acceptors, those of TRV130 are and 4, respectively Notably, the hydrogen bond between M6G with residue GLU310 decreases considerably the fluctuation of this residue The hydrogen bond between M6G and GLU310 existed during about 70% the simulation time, and for 40.34% of this period was about hydrogen bonds It can be seen from RMSF graph where the whole ECL4 of mOR in M6G system fluctuates much larger than that of TRV130 but RMSF value of residue GLU310 in two systems are approximate (Figure 3.15) In addition, M6G created hydrogen bond with GLN314 almost 60% the simulation time TRV130 also had one hydrogen bond with TYR316 during nearly 62.19% of the trajectory frames Figure 3.19: Number of hydrogen bonds between M6G and µOR Figure 3.20: Number of hydrogen bonds between TRV130 and µOR 3.7 Conformations of mORs Upon interacting with different ligands, GPCRs change their conformations which result in their specific effects Superposition of backbone of mORs shows root mean square deviation of 2.5834 Å The extracellular part of TM5, TM6 and direction (Figure 3.21) transmembrane helixes TM1, TM7, the ECLs show considerable differences in the (a) (b) (c) (d) Figure 3.21: The difference in structure of TM1 (a), TM5 (b), TM6 (c), TM7 (d) The secondary structures of the two systems were shown in Figure 3.22 It is easily to see that there were significant differences in TM6 and TM7 In TM6 of M6GmOR, residues from 289 to 293 are in π-helix structure, residue THR294 are bend Similarly, TM7 of M6G-mOR is quite disordered with the appearance of 10 helix structure in residue 322-324 (after 30ns) and 334-336 (after 300ns), hydrogen bonded turn in residue 325-326 (after 30ns) Whereas, the secondary structure of TM6 and TM7 of TRV130-mOR are π-helix structure during almost all the simulation period (Figure 3.22) Generally, the structures like π-helix, bend, hydrogen bonded turn are much more flexible than α-helix As a result, TM6 and TM7 of M6G-mOR fluctuate more than that of TRV130-mOR, and thus, lead to the higher fluctuation of neighbor residues (ICL2, ECL2) Figure 3.22: Secondary structure of µOR in M6G (left) and TRV130 (right) complexes analyzed by DSSP [33] Notably, the disordered structure of M6G-mOR is next to/around the residue PRO295 (TM6) and GLY325 (TM7) Proline which lacks backbone hydrogen bond donor and glycine whose its side chain is highly flexible are well-known as α-helix breakers Moreover, it is worth mentioning that the residues interacting with M6G is near the extracellular part and far from PRO295 and GLY325, while TRV130 interacts with residues locating in both sides of PRO295 and GLY325 Therefore, the interactions between TRV130 and residues around PRO295 and GLY325 lead to lower fluctuation of TM6 and TM7 of TRV130-mOR Especially, in mutation experiment, in δOR, TRP294 regulate β-arrestin recruitment but not G protein signaling [32] In addition, DAMGO lost its µOR‟s β-arrestin response at mutation of TYR326 to PHE [15] Both TRP294 and TYR326 are next to PRO295 and GLY325 in the intracellular side, respectively Therefore, by stabilizing TM6 and TM7, the hydrophobic interaction between TRV130 with residues TRP294 and TYR326 might be the key point of activating biased signaling Indeed, β-arrestin bind to GPCR in their C-terminus tail so TM7 which directly connects to the Cterminus play an important role in beta-arrestin recruitment During simulation time, due to different interaction with ligands, the conformations of TM7 in two complex are considerably different Figure 3.23: Conformation of µORs in binding with M6G (left) and TRV130 (right) (forcusing on TM6 and TM7, green-colored residue is PRO295 and GLY325) 3.8 Interaction with Gα protein The contacts between helix (H5) of G-alpha with µOR mainly in TM3, TM5, TM6, ICL2, and ICL3 play an important role in Gα activation Due to contacting with active-state GPCR, H5 undergo a disorder-to-order transition, then “pulled” H5 triggers GDP release [10] In both systems, H5s are in ordered state, however, their contacts with mOR is a bit different When superimposing the backbone of mOR, H5 of M6G‟s complex are tilted about 10 with H5 of TRV130‟s (Figure 3.24) Moreover, H5 of M6G complex fluctuate a bit more than TRV130‟s (Figure 3.16) this might be resulted from the higher fluctuation of TM6, TM7 and ICL2 of M6G-mOR Figure 3.24: Difference in Helix of Gα (other parts of Gα were hiden) Chapter CONCLUSIONS In the work of this thesis, we mentioned opioid painkillers, including their importance in analgesic treatment as well as their severe side effects This is the main reason why biased signaling of mu-opioid receptor enabling the development of new drugs with selective effects attracts many medicine and pharmaceutical studies We perform MD simulation of two systems of µOR, each in its binding state with morphine (unbiased ligand) and TRV130 (biased ligand), then analyzed the conformational differences of mOR in these two systems The results showed significant differences in the conformations of µORs in two complexes such as their directions of TMs, fluctuation, and secondary structures These differences are caused by the distinct binding sites of the two ligands From MD simulation analysis and comparison with mutation experiments, we believe that the contact between TRV130 with residue TRP293 (TM6) and TYR326 (TM7) stabilizes TM6 and TM7 and this might be one of the key factors leading to biased signaling The result also suggested that the conserved proline and glycine residues play an important role in creating various states of GPCRs In addition, binding energy calculated with MM-PBSA method showed that the affinity of TRV130 toward µOR is higher than that of morphine, this is in agreement with experiment Besides, the hydrophobic property of TRV130 leads to fewer hydrogen bonds 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VIETNAM JAPAN UNIVERSITY TRAN KY THANH MOLECULAR STUDY OF INTERACTIONS OF MU- OPIOID RECEPTOR IN BINDING WITH BIASED AND UNBIASED LIGANDS BY MOLECULAR DYNAMIC SIMULATION MAJOR: NANOTECHNOLOGY CODE:... INTRODUCTION 1.1 Opioid painkillers and their side effects 1.2 µ -opioid receptor 1.3 Biased signaling 1.4 Biased and unbiased ligand of mu opioid receptor Chapter... a new generation of drugs 1.4 Biased and unbiased ligand of mu opioid receptor Morphine is an unbiased ligand, signaling both the GPCR signaling pathway to create analgesia, and the β-arrestin