Molecular Study Of Interactions Of Mu-Opioid Receptor With Biased And Unbiased Ligands By Molecular Dynamic Simulation.pdf

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 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
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 4. CONCLUSIONS .....................................................................................34
REFERENCES ..........................................................................................................35

ii


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

iii


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
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 5 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.


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]
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 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 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 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;
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 3. 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 ( was used to define the protonation state and charge of

all titratable residues [9]. Disulfide bonding between Cys140 and Cys217 was
created before simulation.

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Figure 3.1: µOR (red) and Gα protein (blue)