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 MOLECU

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

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 1 INTRODUCTION 1

1.1 Opioid painkillers and their side effects 1

1.2 µ-opioid receptor 3

1.3 Biased signaling 6

1.4 Biased and unbiased ligand of mu opioid receptor 8

Chapter 2 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 3 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

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 4 CONCLUSIONS 34

REFERENCES 35

LIST OF TABLES

Table 1.1: Properties of Opioid Receptors [34] 4

Table 3.1: Binding free energy in terms of Experiment and g_mmpbsa estimation 25

LIST OF FIGURES

Figure 1.1: Countries consuming the most opioids [33] 2

Figure 1.2: Drug overdose deaths involving any opioid, number among all ages, by gender, 1999-2017 [5] 3

Figure 1.3: Percentage of drugs targeting GPCR family 5

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] 7

Figure 1.7: Illustration for the effects of biased and unbiased ligand when binding to protein [24] 9

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 1 (left) and cluster 5 (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 1 (left) and cluster 2 (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

Figure 3.20: Number of hydrogen bonds between TRV130 and µOR 29Figure 3.21: The difference in structure of TM1 (a), TM5 (b), TM6 (c), TM7 (d) 30Figure 3.22: Secondary structure of µOR in M6G (left) and TRV130 (right)

complexes analyzed by DSSP [33] 31Figure 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) 32Figure 3.24: Difference in Helix 5 of Gα (other parts of Gα were hiden) 33

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

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

Chapter 1 INTRODUCTION

1.1 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

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]

Mu Enkephalins

β endorphins

Morphine, sufentanyl, DAMGO

Analgesia, euphoria, tolerance, dependence, immune suppression, respiratory depression, emesis

Naloxone Naltrexone

Analgesia, sedation, myosis, diuresis, dysphoria

Analgesia, immune stimulation, respiratory depression

Naltrindole

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, 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 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

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 N-terminus, 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 2 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;

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

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]:

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)

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

(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 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 [27]