INTRODUCTION
Opioid painkillers and their side effects
Opioids have been used to relieve pain for thousands of years Opium is extracted from the dried milky juice of a species of poppy, called Papaver somniferum
During human history, opium is considered as “God‟s own medicine” and its trade and use have been involved in many discreditable commercial, social, moral and political events, for example, the Opium War Opium is the mixture alkaloids whose major components are morphine, codeine, and papaverine Whereas, the analgesic effect of opium is mainly caused by morphine [26]
Nowadays, the use of opioids is different in each country, the United State and Canada are the two countries consuming the most opioids (Figure 1.1) [33] It is worth noting that there is a dramatic increase in prescribing opioids in many countries According to WHO, in the year of 2016, nearly 34 million people used opioids and that number for opiates is 19 millions [36] Furthermore, around 90% of patients have chronic pain use opioids The proportion of the population suffering substance abuse disorder is 8% which is even more than the percentage of patients having chronic pain [1], [8]
Figure 1.1: Countries consuming the most opioids [33]
Opioids are highly effective analgesics used to alleviate acute, surgical and cancer pains, however, they have many side effects The side effects like nausea, vomiting, constipation, sedation, respiratory depression lead to the limitation of dose and effectiveness of opioids [4] In addition, another common effect of opioids is tolerance, the diminish of analgesic response to drug when opioids are used repeatedly and patients‟ body adapt with their presence [5] This side effect causes a need of increasing dose, and then, higher dose results in more serious side effects and supports the addiction A quarter of people taking opioids long-term become addicted Drug deaths from opioids tend to rapidly increase From 1999 to 2017, in the United State, the number of death due to opioid analgesic increased significantly in all gender (Figure 1.2) [5] Besides, opioids may also create several less common side effects, such as immunologic and hormonal dysfunction, increased pain sensitivity, myoclonus, muscle rigidity and so on
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.
à-opioid receptor
The three major types of opioid receptors (OR) (à, 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, àOR is the main target of many opioids The binding of painkillers to àOR leads to clinical analgesics
Table 1.1: Properties of Opioid Receptors [34]
Analgesia, euphoria, tolerance, dependence, immune suppression, respiratory depression, emesis
Analgesia, sedation, myosis, diuresis, dysphoria
Analgesia, immune 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 7 transmembrane domain receptors (7TMR) G proteins are membrane protein binding to GDP (guanosine diphosphate) or GTP (guanosine triphosphate); including three distinct subunits, G α , G β, and G γ Depending on the nature of the G α subunit, G proteins are divided into three major families, G i , G q , and G s , and each of them shows specific functions by influencing on different intracellular effectors [22]
GPCR is divided into 6 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
• 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 N- terminus, the extracellular loops and the upper part of the transmembrane domains
Figure 1.4: Features of class A GPCR [31]
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 G α from G βγ subunits
Dissociated G α and G βγ subunit modulate downstream effector pathways To stop signal, G-protein will be inactivated by the hydrolysis of G α -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
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.
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 àOR, 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 àOR with both biased and unbiased ligands to determine the important differences in àOR 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]
METHODOLOGY
Molecular dynamics (MD) simulation
Newton‟s equation of motion is solved for a system of N atoms in MD simulation:
(2.1) Where m i and r i are mass and coordinate of i th atom
The formula for the forces are the negative derivatives of a potential function V (r 1 ; r 2 ;…; r N ):
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].
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 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].
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 G complex is the total free energy of the protein-ligand complex and G protein and
G ligand are total free energies of the isolated protein and ligand in solvent, respectively (Figure 2.1)
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 ΔG bind,solv is:
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) ΔG vacuum is obtained by calculating the average interaction energy between receptor and ligand and taking the entropy change upon binding into account if necessary
Where is molecular mechanics poteintial energy, T is temperature, 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
RESULTS AND DISCUSSION
Setting up the simulation systems
Protein: There are pieces of evidence that the active conformation of GPCR can be stabilized by interactions between the receptor and its G α protein [16] Furthermore, C-terminus of the G α 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://nbcr-222.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
Membrane: Membrane lipid composition plays an important role in structure/function relationships of àOR [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
The membrane downloaded from MEMBUILDER server are not equilibrated, all molecules are arranged orderly (Figure 3.3) and there is a gap between 2 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 nm 2 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 àOR 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)
Figure 3.5: Structure of M6G (left) and TRV130 (right)
The smile formats of two ligands were created and added hydrogen atoms by the UCSF Chimera program [25] Then, AutoDockTools is used to do coordinate file preparation, AutoGrid Calculation The protein was kept rigid while the ligand was allowed to rotate and explore more flexible binding pockets Docking of the ligands onto the mOR was performed using AutoDock4 version 1.5.6 with Lamarckian genetic algorithm (LGA) The grid box was put between the transmembrane domains of the receptor which was the binding pocket of small ligands like M6G and TRV130 Two hundred independent docking runs were completed for each ligand
These docking positions were clustered by Autodock4 with the clustering RMSD tolerance 2 Å Firstly, Autodock arranges all the docked conformations from the lowest energy (the best docking) to highest The best docking is used as the representative for the first cluster Then the root mean square deviation (RMSD) between the first and second best conformations are compared If the calculated RMSD value is smaller than 2 Å, that conformation is added to the same cluster with the best conformation If not, the second becomes the representative for a second cluster Then the RMSD between the third conformation and the „best‟ is computed If smaller than 2 Å, it is added to the first cluster If not it compared with the seed of the second cluster and so on… [23]
The chosen clusters had not only low binding energy and high frequency but also good precision in terms of binding energy
Docking analysis of M6G: Histogram of docking results showed that cluster 1 and cluster 5 had much higher frequency compared with the others, 49 and 42 conformations respectively (Figure 3.6) However, histogram based on binding energy of cluster 5 expressed better distribution and higher average binding energy than cluster 1 (Figure 3.7) Therefore, the lowest interaction energy conformation of cluster 5 was employed for MD simulation
Figure 3.6: Histogram of clustering analysis of M6G
Figure 3.7: Histogram of cluster 1 (left) and cluster 5 (right) with respect to binding energy The red line represents the mean value of binding energy of that cluster
Docking analysis of TRV130: from the Figure 3.8, it is obvious that cluster 2 with distribution in terms of energy and average binding energy are better than cluster 1 which had 25 conformations (Figure 3.9) Hence, the conformation which performed the lowest interaction energy of cluster 2 was employed for next steps
Figure 3.8: Histogram of clustering analysis of TRV130
Figure 3.9: Histogram of cluster 1 (left) and cluster 2 (right) with respect to binding energy The red line represents the mean value of binding energy of that cluster
Protein including àOR and Gα protein was embedded in the equilibrated membrane (Figure 3.10) Water and ions (NaCl 0.15M) were also added
Figure 3.10: The system including proteins, membrane, ligand, ions from top view (left) and side view (right) (green: protein, cyan: DPPC, pink: DOPE, purple:
All simulations were performed by using Gromacs 2016.1 with the mixture of amber99sb-ildn force field and AMBERSLIPID force field (for the membrane) The energy of each system is minimized to 1000.0 kJ/mol/nm by steepest decent algorithm Then, the systems with constrained protein and ligands were subjected to a 50ns equilibration at constant pressure and temperature (310 K and 1 atm semi- isotropic pressure) Finally, MD simulations were performed for 500 ns under the same conditions of NPT ensemble, with a Nose Hoover thermostat and Parrinello Rahman pressure coupling The integrator leap-frog algorithm was used, and the time step was set as 2 fs.
Rout mean square deviation (RMSD)
Root mean square deviation was calculated after backbone of protein had been fit to a reference frame
Where m i , r i are the mass and coordinate of i th atom, r i ref is the coordinate of reference structure, M is the total mass of the structure
RMSD of backbone/ligand gives information about the rotation and translation of backbone/ligand with respect to a referent structure
The RMSD of backbone mOR shows that mOR in TRV130‟s complex is more stable than mOR of M6G‟s complex (Figure 3.11) Besides, from RMSD of ligands, we can see that, during 0-30ns, TRV130 moves quick from docking site to another site and locates stably there during the rest of the time Whereas, M6G is very flexible until 300ns (Figure 3.12) ff
Figure 3.11: RMSD of backbone àOR
Cluster analysis
Cluster analysis was carried out by gromos method with cutoff 0.12nm àOR was chosen to do least square fit There were 11 clusters and 13 clusters in TRV130 and M6G systems, respectively Figure 3.13 showed the cluster size of two systems In terms of TRV130 system, the first cluster took account for nearly 79% the frames
When that number for M6G complex is about 66% Figure 3.14 displayed the cluster ID during the simulation time of both systems If the cutoff was decreased to 0.11nm, the number of cluster in TRV130 system is 30 clusters, which was less than that of M6G, 46 clusters As a result, the cluster analysis also showed that àOR in binding with TRV130 is more stable than àOR in binding with M6G
The structure which had least RMSD compared with the average structure of cluster
1 was used for the next analysis related to conformation of àOR and binding site
Figure 3.13: The cluster size diagrams of TRV130 (left) and M6G (right) system
Figure 3.14: Cluster ID during simulation time of TRV130 (left) and M6G (right) complex.
Binding free energy calculated by g_mmpbsa
g_mmpbsa is written in the programming language C and inherits functions from GROMACS as well as APBS to calculate energy components [20]
Because after 300ns both systems are in equilibrium, the binding free energy are calculated from 300ns-500ns by MM-PBSA method The calculation shows that the binding free energy between TRV130 and mOR is -118.21 kJ/mol and lower than that of M6G, -64.92 kJ/mol (Table 3.1) In experiments, ΔG between morphine and mOR (-47.6 kJ/mol) is also more positive than ΔG of TRV130 and mOR (-55.2 kJ/mol) [7] The higher affinity of TRV130 toward mOR also explain why TRV130 is more stable in binding with mOR, compared with M6G
Table 3.1: Binding free energy in terms of Experiment and g_mmpbsa estimation
Van der Waal interaction (kJ/mol) -152.41 +/- 15.43 -199.46 +/- 9.08 Electrostatic interaction (kJ/mol) -124.79 +/- 30.52 1.78 +/- 6.85 g_mmpbsa ΔG (kJ/mol) -64.92 -/+ 13.90 -118.21 +/- 12.83
(*) Experimental binding energy is extracted from k off /k on in Supplemental Table 3 of this paper [7] by the formula where R = 8.314 J mol -1 K -1 or 0.008314 kJ mol -1 K -1 and T is the temperature.
Root mean square fluctuation (RMSF)
Root mean square fluctuation (RMSF) measures the fluctuation of each residue during simulation
Where r i (t j ) is the coordinate of i th atom in time t j , r i ref is the coordinate of reference structure, T is period of time we want to calculate
RMSF of àOR shows that the ICL3 (intracellular loop 3), TM6 (transmembrane helix 6), ECL3 (extracellular helix 3), TM7 of àOR in M6G-àOR fluctuate much more than àOR in TRV130-àOR (Figure 3.13)
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 àOR
Figure 3.18: Map of residues interacting with M6G (pink) and TRV130 (green)
During simulation time, M6G creates about 2 to 4 hydrogen bonds/a frame with mOR (Figure 3.19), whereas hydrogen interaction between TRV130 and mOR is 0 to 2 bonds each frame (Figure 3.20) This is resulted from the differences in the number of acceptors and donors of 2 ligands, M6G has 5 pairs of donors and 10 acceptors, those of TRV130 are 2 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 2 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 àOR
Figure 3.20: Number of hydrogen bonds between TRV130 and àOR
Conformations of mORs
square deviation of 2.5834 Å The transmembrane helixes TM1, TM7, the extracellular part of TM5, TM6 and 3 ECLs show considerable differences in the direction (Figure 3.21)
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 M6G- mOR, residues from 289 to 293 are in -helix structure, residue THR294 are bend
Similarly, TM7 of M6G-mOR is quite disordered with the appearance of 3 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 -helix structure during almost all the simulation period (Figure 3.22) Generally, the structures like -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 àOR 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 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 OR, TRP294 regulate β-arrestin recruitment but not G protein signaling [32] In addition, DAMGO lost its àOR‟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 C- terminus 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 àORs in binding with M6G (left) and TRV130 (right) (forcusing on TM6 and TM7, green-colored residue is PRO295 and GLY325)
Interaction with Gα protein
The contacts between helix 5 (H5) of G-alpha with àOR mainly in TM3, TM5, 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 0 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 5 of G α (other parts of G α were hiden)
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 àOR, 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 àORs 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 àOR is higher than that of morphine, this is in agreement with experiment Besides, the hydrophobic property of TRV130 leads to fewer hydrogen bonds with àOR than morphine
In conclusion, this research has discovered the conformations of àOR in binding with biased and unbiased ligands and important residues for biased ligand Based on these results, for the next step, we plan to implement structure-based drug screening to looking for àOR biased-ligand candidates
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