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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 SIMULAT[.]
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 Hanoi, 2019 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 .1 Chapter INTRODUCTION .1 1.1 Opioid painkillers and their side effects .1 1.2 µ-opioid receptor 1.3 Biased signaling .6 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] .6 Figure 1.5: G proteins signal pathway [18] .7 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 Figure 1.2: Drug overdose deaths involving any opioid, number among all ages, by gender, 1999-2017 [5] In conclusion, despite their numerous side effects, opioids are very important for analgesics Consequently, it is necessary to develop new opioid painkillers which have diminished side effects 1.2 µ-opioid receptor The three major types of opioid receptors (OR) (µ, and ) are members of seven transmembrane spanning receptors or G-protein coupled receptor (GPCRs) They are present throughout the body but they are in high concentration in the PAG, the limbic system, the thalamus, the hypothalamus, medulla oblongata and the substaintia gelatinosa of the spinal cord Each type of OR are responsible for different function (Table 1.1) [34] Whereas, µOR is the main target of many opioids The binding of painkillers to µOR leads to clinical analgesics Table 1.1: Properties of Opioid Receptors [34] Opioid receptor Natural ligand Selective agonist Properties Mu Enkephalins Morphine, sufentanyl, DAMGO Analgesia, euphoria, Naloxone tolerance, dependence, Naltrexone immune suppression, respiratory depression, emesis β endorphins Mu-1 Antagonist Naloxonazine Mu-2 Kappa Delta Dynorphin Bremazocine β endorphins Trifluadom Enkephalins DADLE β endorphins DSLET Analgesia, myosis, dysphoria sedation, TENA, nor-B diuresis, NI Analgesia, immune Naltrindole stimulation, respiratory depression GPCR is one of the biggest family of protein It is a class of transmembrane receptor coupling with G-protein The GCPRs have seven transmembrane helices (TM), three intracellular loops (ICLs), three extracellular loops (ECLs), an extracellular N-terminal and an intracellular C-terminal domain (Figure 4) so they are also called transmembrane domain receptors (7TMR) G proteins are membrane protein binding to GDP (guanosine diphosphate) or GTP (guanosine triphosphate); including three distinct subunits, Gα, Gβ, and Gγ Depending on the nature of the Gα subunit, G proteins are divided into three major families, Gi, Gq, and Gs, and each of them shows specific functions by influencing on different intracellular effectors [22] GPCR is divided into classes, from A to F ORs are classified as class A which is the largest class of GPCR family and is targeted by 94% of drugs of GPCR (Figure 1.3) Figure 1.3: Percentage of drugs targeting GPCR family Most of Class A of GPCRs shows these features (Figure 1.4) [31]: • A disulfide bridge between the ECL2 and the upper part of TM3 • A palmitoylated cysteine in the C-terminus • A highly conserved sequence homology of an Asp-Arg-Tyr motif on the ICL2 • A sodium ions in the center of seven TMs • Binding site of small ligands like morphine and TRV130 is between the transmembrane domains of the receptor In contrast, the binding site of peptide and glycoprotein hormone receptors is located between the Nterminus, the extracellular loops and the upper part of the transmembrane domains Figure 1.4: Features of class A GPCR [31] 1.3 Biased signaling G proteins and β-arrestins are the most recognized signaling pathways of GPCRs They present different biochemical and physiological functions The G protein signaling pathway is displayed in Figure 1.5 When an agonist binds to GPCR and changes the conformation of the receptor, G-protein is activated, Ga subunit releases guanosine diphosphate (GDP) and associates with guanosine triphosphate (GTP) This leads to the dissociation of Gα from Gβγ subunits Dissociated Gα and Gβγ subunit modulate downstream effector pathways To stop signal, G-protein will be inactivated by the hydrolysis of Gα-GTP complex by GTPase, which convert GTP into GDP β-arrestin signal pathway is a downstream signal pathway (Figure 1.6) After ligand binding and G protein activation, G protein coupled receptor kinase (GRKs) phosphorylates the receptor, typically on its cytoplasmic tail β-arrestin recognized the phosphorylated sites and binds to the receptor β-arrestins mediate many receptor activities, including desensitization, downregulation, trafficking, and signaling Figure 1.5: G proteins signal pathway [18] Figure 1.6: β-arrestin signal pathway [3] After binding the GPCRs, most agonists are thought to equally activate both G protein and β-arrestins signaling pathways However, recently, a novel concept, biased agonism, has emerged, in which biased agonists are able to selectively activate the signaling pathway leading to the desired effects but not the signaling pathway causing adverse effects This concept enables to develop selective drugs with higher efficacy and reduced side effects [29] Several biased ligands have demonstrated their efficient treatment and safety in clinical trials [28] Therefore, studying GPCR biased signaling may create 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 signaling pathway responsible for side effects [24] TRV130 is a biased ligand of µOR, it activates the GPCR signal transduction with less β-arrestin recruitment [6], [24] TRV130 has been evaluated in clinical trial for severe acute pain treatment Compared with morphine, TRV130 provides similar analgesia but causes less adverse effects [6], [30], [35] (Figure 1.7) In addition, another compound, PZM21, was discovered by computational modeling and structure-based screening Similar to TRV130, PZM21 showed higher analgesia, reduced adverse effects than morphine in preclinical trial The different performance between unbiased and biased ligands might result from differences in binding conformations of ligands and receptor Therefore, it is important to understand the binding conformations of µOR with both biased and unbiased ligands to determine the important differences in µOR and ligands structures leading to the different signal pathways activations Figure 1.7: Illustration for the effects of biased and unbiased ligand when binding to protein [24] Chapter METHODOLOGY 2.1 Molecular dynamics (MD) simulation Newton‟s equation of motion is solved for a system of N atoms in MD simulation: (2.1) Where mi and ri are mass and coordinate of ith atom The formula for the forces are the negative derivatives of a potential function V (r1; r2;…; rN): (2.2) In each small time step, the equations are solved simultaneously Following the time, the system remains at a required temperature and pressure, the outputs (coordinates, force, velocity, …) are written regularly at a specific time The trajectory of the system is represented as the coordinates, which is a function of time Generally, the system will become equilibrium after a period of time Many macroscopic properties can be extracted from the average values of the equilibrium trajectory in the output file Here is some limitation of MD simulation method [2]: • The simulations are classical: Using Newton‟s equation of motion automatically means that the motion of atoms is described by classical mechanics For most atoms at normal temperatures, this is acceptable, except in some cases For example, hydrogen atoms' motion may be similar to protons motion which can have quantum mechanical (QM) characteristic and classical mechanics can not treat properly this case • Force fields are approximate: Force fields includes a set of potential equations and their parameters provide the forces These functions are used to create the potential energy and the force • The force field is pair-additive • Electrons always remain in their ground state • Long-range interactions are cut off • Boundary conditions are unnatural: when the system is small, there is a lot of undesirable interacting area with the vacuum Periodic boundary conditions are used to avoid real phase boundaries GROMACS is a free program which performs molecular dynamics simulations and energy minimization [2] 2.2 Docking by Autodock 4.2.6 and AutoDockTools Automated docking is the prediction how small molecules, such as drugs and substrates, bind to a 3D structure biomolecular [17] AutoDock is an automated docking tool which has shown an effective ability of quickly and accurately predicting bound conformations and calculating their binding energies by a semiempirical free energy force field Autodock can search the large conformational space available to a ligand around a protein by using a grid-based method which rapidly evaluates the binding energy of trial conformations This means that the target area macromolecule is located in a grid and each grid point is a probe atom whose the interaction energy with target protein is computed During the docking, this grid of energies may be used as a lookup table [17] Lamarckian genetic algorithm is primarily used in the conformational searching methods At first, a number of trial conformations are generated and, from them, successive conformations are chosen and next generations are created by mutating these individuals, exchanging conformational parameters, and competing in a similar way with the biological evolution, finally, individuals with lowest binding energy are selected In the “Lamarckian” approach, each conformation is able to search their local conformational space, find local minima, and then pass this feature to next generations AutoDock4 also provides other search methods such as Simulated annealing and a traditional genetic algorithm [17] AutoDockTools is an effective graphical user interface tool for preparing coordinate, designing experiment and analyzing the results AutoDockTools help users to format input molecule files, with a set of selection from protonation, calculating charges to specifying rotatable bonds in the ligand and the protein In addition, AutoDockTools users can simply design and prepare the docking experiments by specifying the active site and determining visually the volume of space searched in the docking simulation, specifying search parameters and launching docking calculations Finally, AutoDockTools includes a variety of novel methods for clustering, displaying, and analyzing the results of docking experiments [17] 2.3 Interaction free energy estimation by MM-PBSA method Molecular mechanics Poisson−Boltzmann surface area (MM-PBSA) approach has been widely used to compute interaction energies, especially, for biomolecular complexes In combination with molecular dynamics (MD) simulations, this method is also able to consider conformational fluctuations and entropic contribution into the binding energy [20] Generally, the binding free energy is calculated by the equation below [27]: (2.3) Where Gcomplex is the total free energy of the protein-ligand complex and Gprotein and Gligand are total free energies of the isolated protein and ligand in solvent, respectively (Figure 2.1) Figure 2.1: Illustration for ligand-protein binding However, the solvent-solvent interactions would mainly contribute to the energy and the fluctuations in total energy would be an order of magnitude larger than binding energy To avoid the inordinate amount of time to converge, it is more efficient to divide up the calculation according to the thermodynamic cycle in Figure 2.2 [27] Figure 2.2: Thermal dynamics cycle for calculation of ligand-protein binding energy Based on the above thermal dynamics cycle, the solvation binding free energy ΔGbind,solv is: ( ) (2.4) Solvation free energies are calculated by either solving the linearized Poisson Boltzmann for each of the three states (this gives the electrostatic contribution to the solvation free energy) and adding an empirical term for hydrophobic contributions [27]: (2.5) ΔGvacuum is obtained by calculating the average interaction energy between receptor and ligand and taking the entropy change upon binding into account if necessary [27] (2.6) Where temperature, is molecular mechanics poteintial energy, T is is entropy contribution obtained by normal mode analysis In practice, entropy contributions can be neglected in case of a comparison of states of similar entropy, such as two ligands binding to the same protein The reason for this is that normal mode analysis calculations are computationally expensive compared with MM-PBSA and its magnitude of standard error can significantly make the result uncertain The average interaction energies of receptor and ligand are usually obtained by performing calculations on an ensemble of uncorrelated snapshots collected from MD trajectory These structures have to come from the equilibrated MD simulation [27] Chapter RESULTS AND DISCUSSION 3.1 Setting up the simulation systems 3.1.1 Modeling Protein: There are pieces of evidence that the active conformation of GPCR can be stabilized by interactions between the receptor and its Gα protein [16] Furthermore, C-terminus of the Gα subunit is deeply bound in a pocket between the transmembrane domains Thus, getting rid of this part of the Gα and substituting by polar water molecules will result in some problems in subsequent molecular dynamics simulations [31] A model including mOR and Ga was set up from the structure of activated mOR in complex with Gi with protein data bank ID - 6DDF (Figure 3.1) [19] Because Ga in 6DDF.pdb has a lot of missing residues, a full structure of Ga was created by manually mixing Ga in 6DDF with Ga in 3UMS The origin structure of 6DDF was download from OPM website which alignment of the protein in the membrane (x/y plane) and also center the transmembrane domain (TMD) in the middle of the membrane The server PDB2PQR (http://nbcr222.ucsd.edu/pdb2pqr_2.1.1/) was used to define the protonation state and charge of all titratable residues [9] Disulfide bonding between Cys140 and Cys217 was created before simulation Tải FULL (48 trang): https://bit.ly/3BGZxuq Dự phòng: fb.com/TaiHo123doc.net Figure 3.1: µOR (red) and Gα protein (blue) Membrane: Membrane lipid composition plays an important role in structure/function relationships of µOR [14] A bilayer membrane with similar composition to synapse membrane was created by membuilder server (http://bioinf.modares.ac.ir/software/mb2/) [13] The ratio Cholesterol:DOPE:DPPC is 6:5:4 The proteins were embedded into the membrane which was well equilibrated by 50ns MD simulation Tải FULL (48 trang): https://bit.ly/3BGZxuq Dự phòng: fb.com/TaiHo123doc.net The membrane downloaded from MEMBUILDER server are not equilibrated, all molecules are arranged orderly (Figure 3.3) and there is a gap between the upper and lower membrane layer (Figure 3.4) Therefore, the membrane was equilibrated by running 50ns MD simulation The area of the membrane showed that the membrane was in equilibrium from 10ns (Figure 3.2) and the average area is 97.4 nm2 The gap between two layers of the membrane was filled (Figure 3.4) Figure 3.2: The area of membrane during 50ns MD simulation Figure 3.3: the membrane downloaded from MEMBUILDER server from top view (left) (red:cholesterol, purple: DPPC, lime: DOPE) and side view (right) Figure 3.4: the membrane after 50ns MD simulation from top view (left) (red:cholesterol, purple: DPPC, lime: DOPE) and side view (right) Ligands: Morphine is metabolised mainly into morphine-3-glucuronide (M3G) (90%) and morphine-6-glucuronide (M6G) (10%) [11] M6G is pharmacologically active and contributes to the analgesic effect [12] Morphine-6-glucuronide (M6G) is a strong µOR agonist with higher affinity than morphine itself Instead of morphine, M6G was chosen as the first ligand TRV130, unlike conventional opioids such as morphine, has no known active metabolites (Figure 3.5) 6793507 ... 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... between unbiased and biased ligands might result from differences in binding conformations of ligands and receptor Therefore, it is important to understand the binding conformations of µOR with. .. 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,