Computational Analysis of the Beta2 Adrenergic Receptor: Insights into Potential

Treatments for Congestive Heart Failure

Name

Department of Chemistry, City University of New York at the College of Staten Island, 2800 Victory

Boulevard, Staten Island, NY 10314, USA

Abstract

The first crystalized G-protein coupled receptor (GPCR) was the B2 adrenergic receptor (B2-

AR). Since that point the B2-AR has become a prototype system for studying GPCRs. Fenoterol

a very well-known agonist of the B2-AR has proven to bind with differing affinity based on its

stereochemical configuration ((R,R’)>(R,S’)=(S,R’)>(S,S’)). Our aim is to study why the

stereoisomers of fenoterol and some of its derivatives (N = 32 molecules) have differences in

their binding affinities to the B2-AR. Our current study exploits computational techniques to

obtain the free energy of binding involved in the formation of fenoterol derivative B2-AR

complexes. The molecular models of B2-AR used in the current study were taken from the

crystal structures of a nanobody-stabilized (active state) of the B2-AR with the bound agonist

BI-167107 (PDB ID: 3P0G) and the human (inactive state) B2-AR T4 lysozyme fusion protein

with bound (S)-carazolol (PDB ID: 2RH1). One computational technique, known as the docking

procedure allowed us to analyze the binding sites for fenoterol and its derivatives. The

docking studies revealed homology between the active residues Ser203, Ser207, Asp113,

Lys305, Asn312, Tyr308, Asp192 and the 3-D space occupied the all ligands, which was

between TM III, TM V, TM VI and TM VII. A second computational technique known as

molecular dynamics, was used to study the atom stability between two ligands ((R,R’)- and

(S,S’)-fenoterol) and the B2-AR. The molecular dynamics stimulations reaffirmed the B2-AR

bias towards the (R,R’) stereoconfiguration of fenoterol. Ultimately, the analysis of these

computation techniques, leaves the scientific world with a new awareness about

stereoselective interactions ligands face with the B2-AR. This new awareness will allow for the

improved design of B2-AR linked medications, which will lead to better ways of treating and

preventing congestive heart failure.

1. Introduction

1.1- GPCRs & G Proteins General Background

G-protein coupled receptors (GPCR’s) are defined as a diverse class of cell-surface receptors

which upon stimulation serve as an activator of G proteins, through guanine nucleotide

exchange factor (GEF) activity. The diversity of ligands (binding partners) for these receptors is

massive and includes neurotransmitters, hormones, light, taste, and odor. With a great diversity

of ligands, it should come as no surprise that GPCR genes are ubiquitous, with over 800 genes

[1] in the human genome coding for this critical class of receptors. One surprising aspect to

GPCR genes is that are predominately intronless [2], which are non-expressed sections of the

DNA/RNA within a protein sequence. Just for comparison, 95% [3] of the human genome that

encodes for proteins contain one or more introns. In terms of the geometric shape or topology

of the receptor we see a highly conserved serpentine heptahelical shape. GPCRs have seven

alpha helical transmembrane domains, while also having the appearance of a snake when seen

from the extracellular face.

GPCRs are separated into many three major families. It has been

reported that the three families contain 25% amino acid sequence

homology [1]. Family A is by far the largest and is known as the

rhodopsin-like family. Family A’s most notable GPCRs include

rhodopsin, the olfactory GPCRs, and the adrenergic receptors (α &

β). Family B is remarkably smaller with approximately 25 members,

and is known as the secretin-like family. Family B is almost uniquely

composed of gastrointestinal hormones GPCRs including ones for

secretin and glucagon. Family C is similar in size to Family B, and is

known as the glutamate-like family. Family C’s most notable GPCRs

include the metabotropic glutamate receptor and some taste

receptors. All of these families couple to key signaling molecules

known as G-proteins.

G-proteins are a class of polypeptides named for their high binding affinity for the nucleotide

guanine. They are generally assorted into two categories: heterotrimeric and small G-proteins.

Heterotrimeric G-proteins consist of three subunits (α, β, γ) while the small G-proteins consist

of one subunit which resembles the α subunit found in heterotrimeric G-proteins. The

interesting property of G-proteins is that their active and inactive state depend on the type of

guanine nucleotide bound. If GTP is bound the G-protein is active, and if GDP is bound then the

G-protein will be inactive. The nucleotide bound is controlled by accessory proteins, which are

generally classified into two classes: guanine nucleotide exchange factors (GEF) and GTPase

activator proteins (GAP). GEF’s promote the dissociation of GDP from the inactive G-protein,

Figure 1- Shows the three

major families of GPCRs and

their binding partners

which allows for the activation [4] of the G-protein through the binding of GTP, one of the

largest class of GEFs are GPCRs. GAP’s promote the hydrolysis of GTP to GDP, leading to the

inactivation [5] of an activated G-protein, one of the largest class of GAPs are regulator of G-

protein signaling (RGS) proteins.

1.2- Heterotrimeric G Proteins (Gs,Gi,Gq,G0) General Signaling Pathway

As discussed earlier, GPCRs exclusively couple with heterotrimeric G-proteins, which consist of

three subunits (α, β, γ). There are generally 4 major classes of heterotrimeric G-proteins, which

include: Gs, Gi, Gq, and G0. The signaling pathway for all 4 major classes begin in a similar

fashion. The initial event involves a ligand binding to a GPCR, once associated with the ligand

the GPCR undergoes a conformational change. That conformational change gives the GPCR the

ability to act as a GEF, which leads to the activation of the associated heterotrimeric G-protein

(GDP gets swapped out for GTP). Once GTP is bound to the α subunit, the heterotrimeric G-

protein undergoes a dissociation, which will leave a β-γ complex with the GPCR and a diffusing

Figure 2- Shows a simple

picture of what GEFs [4] and

GAPs [5] do when they

interact with G-Proteins.

α subunit. The primary role of the α subunit is to be an allosteric modulator of an effector

protein. Up until this point, all of the four major classes of heterotrimeric G-proteins follow the

same mechanism, after this point they will diverge depending on the effector protein.

The major effector of the Gs and Gi classes of heterotrimeric G proteins is adenylyl cyclase [1,6],

a huge 12 TM domain protein that converts intracellular ATP into cAMP. Gs’s α subunit activates

adenylyl cyclase, thus leading to an increase in intracellular cAMP. The increased levels of cAMP

can lead to the activation of many important proteins including protein kinase A (PKA). As with

all kinase proteins, PKA phosphorylates proteins with a certain consensus sequence. Ultimately,

the effects of PKA can be felt at the genetic level with transcriptional regulation. Gi’s α subunit

inactivates adenylyl cyclase, leading to a decrease in intracellular cAMP, which will also lead to

changes in gene expression.

The major effector of the Gq class of heterotrimeric G proteins is phospholipase C, an enzyme

that cleaves phospholipid in the sn3 position [1]. One major phospholipid that phospholipase C

cleaves is phosphatidylinositol-4,5-bisphosphate (PIP2) . Once PIP2 is cleaved there is an

increase in two second messengers, diacylglycerol (DAG) and inositol-triphosphate (IP3). The

role of the second messenger IP3 is to bind to IP3 receptors found on the endoplasmic

reticulum, promoting the release of a tertiary messenger Ca2+ from the endoplasmic reticulum.

The role of the second messenger DAG is to activate protein kinase C (PKC). As with all kinase

proteins, PKC phosphorylates proteins with a certain consensus sequence. Ultimately, the

effects of PKC can be felt at the genetic level with transcriptional regulation.

The major effectors of the G0 class of heterotrimeric G proteins are potassium (K+) channels,

adenylyl cyclase, and more [7]. It is key to note that most cells have sodium potassium pumps,

which bring K+ into the cells against its concentration gradient. Once the K+ channels open from

the G0 activation they pour potassium out of the cell, leaving the cell more negative on the

inside when compared to resting, which is known as hyperpolarization. When G0 inactivates

adenylyl cyclase, it leads to a decrease in intracellular cAMP, which will also lead to changes in

gene expression.

1.3- Introduction to the B2-AR

As alluded to earlier, the beta adrenergic receptors are found in family A of GPCRs. There are 3

subtypes of the beta adrenergic receptor, and they include: B1-AR, B2-AR, B3-AR. The three

subtypes are unequally distributed [8] among the human body. The B1-AR is in higher ratio in

the heart, the B2-AR is in higher ratio in smooth muscle, and the B3-AR is in higher ratio in

adipocytes. The natural ligand (or binding partner) for the 3 subtypes of the beta adrenergic

receptors are epinephrine and norepinephrine. The major structural difference between the

two is the methylated nitrogen on epinephrine, seen below.

Figure 3- The molecule on the left is epinephrine, while the molecule on the right is norepinephrine.

The primary role of the beta adrenergic receptor is

found in the autonomic nervous system, or the

unconscious nervous system. The autonomic

nervous system is generally broken up into two

classes: sympathetic or parasympathetic. Adrenergic

receptors ( α, β) play the premier role [9] in the

sympathetic nervous system’s fight or flight

response, while muscarinic receptors play the

premier role in the parasympathetic nervous

system’s rest or digest response. As a result of being

a part of the sympathetic nervous system, the B-ARs

are involved with the recruitment of glucose through gluconeogenesis and glycogenolysis. One

of the more interesting paradoxes [8] is the B2-AR involvement with muscular contractions in

the heart, while also being involved with muscular relaxation in smooth muscle cells. This

paradox will be discussed more in the coming section.

1.4- B2-AR Signaling Pathway

The initial event in the B2-AR signaling pathway involves a ligand binding to the B2-AR, once

associated with the ligand the B2-AR undergoes a conformational change. The change gives the

B2-AR the ability to act as a GEF, which leads to activation of the associated Gs heterotrimeric G-

protein (GDP gets swapped out for GTP). Once GTP is bound to the α subunit, the

Figure 4- Shows the effects [9] of the

sympathetic nervous system on various

organs, found in the human body.

heterotrimeric G-protein undergoes a dissociation, which will leave a β-γ complex with the

GPCR and a diffusing α subunit. The primary role of the α subunit is to be an allosteric

modulator of adenylyl cyclase.

Adenylyl cyclase, a huge 68 kilo-Dalton protein which converts intracellular ATP into cAMP. Gs’s

α subunit activates adenylyl cyclase, thus leading to an increase in intracellular cAMP. The

increased level of cAMP can lead to activation of many important proteins including protein

kinase A (PKA). As with all kinase proteins, PKA phosphorylates proteins with a certain

consensus sequence, which in PKA’s case is RRX(S/T)Y . Ultimately, the effectors of PKA are

diverse and depend on the proteins available in that specific cell.

Figure 5- Shows the common initial steps [10] of B2-AR signaling pathway in all cells

This brings us back to the paradox found in the B2-AR pathway, which has an effect of

constriction in the heart, while having an effect of relaxation in smooth muscle found in the

lungs. First, let’s look at the targets of PKA that can be found in lung’s smooth muscle cells; the

primary two proteins are phospholamban [11] and MAXI-K (K+) channels [12]. Phospholamban

is an enzyme that blocks a calcium pump found in the ER, known as SERCA. By blocking SERCA,

phospholamban promotes increased levels of intracellular calcium, which is required for

contraction. When phospholamban is phosphorylated by PKA, phospholamban can no longer

block SERCA, meaning decreased levels of intracellular calcium, which leads to relaxation.

MAXI-K channels are K+ channels, meaning they can promote the flow of potassium through an

electrochemical gradient. It is key to note that most cells have sodium-potassium pumps, which

bring K+ into the cells against its concentration gradient. Once the MAXI-K channels open from

PKA phosphorylation they pour potassium out of the cell, leaving the cell more negative on the

inside then resting, which is known as hyperpolarization. Once hyperpolarized the muscle cell

relaxes. Next, let’s look at the targets of PKA that can be found in heart’s muscle cells; the

primary protein is the L-type calcium channels [13]. It is key to note that most cells have a low

intracellular calcium concentration. Once the L-type calcium channels open from the PKA

phosphorylation, calcium pours into the cell, leaving the cell more excited and more positive on

the inside then resting, which is known as depolarization. Once depolarized the muscle cell can

contract.

1.5- B2-AR Signaling Pathway Regulation

All critical intracellular processes whether they are related to metabolism or cell signaling are

highly regulated. In the B2-AR signaling pathway there are three major forms of regulation, each

which regulate a unique point in the cascade. At the level of G-protein stimulation there is an

intrinsic GTPase function [14], which the α subunit can take part in. Once the GTPase function

turns on, the α subunit can no longer modulate adenylyl cyclase, effectively ending the B2-AR

signaling pathway. It is key to note that the intrinsic GTPase activity of the α subunit is weak

and normally needs GAPs (section 1.1) to help aid in the process.

A second level of regulation takes place at the level of second messenger formation. Adenylyl

cyclase converts ATP into cAMP, but cAMP levels can be regulated by protein known as

phosphodiesterase. The role of phosphodiesterase, is to convert a cyclic mononucleotide

(cAMP) into a straight chain mononucleotide (AMP) with the help of a molecule of water.

The third major level of regulation exerts its effects directly on the B2-AR, through a process

known as internalization. This regulation’s initial step involves proteins known as G-protein

receptor kinases (GRKs), which phosphorylate the GPCR [1] at certain locations. Once the B2-AR

is phosphorylated, a class of proteins known as arrestins bind to the receptor. Arrestins

promote receptor endocytosis, which leads to an alternative ERK signaling pathways.

1.6- Fenoterol & Congestive Heart Failure

Figure 6- Shows the reaction catalyzed by phosphodiesterase [15].

Congestive heart failure has had a devastating impact on America, with some estimating a $32

[16] billion dollar yearly cost from the disease. Such costs have made the gov’t prioritize

research aimed at curbing the disease [17]. Recently, there has been an interesting twist, which

involves the B2-AR, once an afterthought to the B1-AR in the heart, it has been discovered

through various articles [8], that it might play a critical cardioprotective role in the heart. That

cardioprotective discovery lead a group of researchers [18] to a B2-AR agonist known as

fenoterol. The molecule fenoterol

consists of 2 chiral centers, meaning that

there are in total four stereoisomers

(fig.7). That same group of researchers

[18] devised a strategy that involved

synthesizing 90 derivative compounds of

fenoterol, by using various chemical

modifications to the aminoalkyl tail and

stereo-chemical configurations.

That group of researchers were able to

come up the various binding affinities (pKi) [18] of the modified ligands to the B2-AR, seen in

table 1. The interesting component of the findings was the B2-AR stereoselectivity for the (R,R’)

form of fenoterol and its derivatives. Also seen in table 1, there is moderate affinity for the

(R,S’) & (S,R’) forms and low affinity for the (S,S’) forms. A last thing the table gave us was the

binding affinities for various R1 and R2 derivatives of fenoterol.

Figure 7- Shows fenoterol and its stereoisomers. Also

seen is the R1 and R2 positions of modification.

First, we will use computational analysis,

specifically a docking procedure to investigate the

similarities and differences in the ligand binding

site for fenoterol and it’s derivatives on an active

and inactive form of the B2-AR from the PDB.

Second, we will use computational analysis,

specifically a molecular dynamics stimulation to

help determine the difference in ligand binding

stability involved in the binding of (R,R’) and (S,S’)

fenoterol to the B2-AR active and inactive receptor.

The receptor models selected in this experiment

carry quite a bit of history. For the inactive state,

the receptor selected was the first GPCR crystal

model ever done [19] (PDB ID: 2RH1). This inactive

state features a co-cyrstalized inverse agonist

known as (S)-carazolol. For the active state, the

receptor selected was the first ever activated

GPCR with nanobody stabilization ever done [20]

(PDB ID: 3P0G). This active state features a co-

cyrstalized agonist known as BI-167107.

2. Materials & Methods

Table 1- Shows fenoterol & derivatives

with the binding affinities [18].

2.1- Docking Methodology

The first step involved taking fenoterol and its derivatives with their stereoisomers (presented

in Table 1), and preparing them for docking procedure. This preparation was done using

HyperChem 6.03 (HyperCube Inc., Gainesville, FL) which contains a Model Build procedure, also

included is an electron optimization which uses the AM1 quantum method to properly give the

ligand’s electrons quantum effects. The next step involved was manually adding the ligand’s

positive charge, which was done in Molegro Virtual Docker software (MVD v. 2010.4.0.0) [21].

Following the optimization of the ligands, they were docked into the binding pocket of the two

high resolution X-ray crystal structures of B2-AR [19, 20] (PDB IDs: 2RH1 and 3P0G). The

optimization of these receptors is discussed in the modeling B2 adrenergic receptors section

(2.1.1). It is key to note that optimization of the receptors included removal of co-crystalized

ligands and other non-essential molecules. After receptor optimization, the ligands were placed

into a sphere with a radius 11 Å, covering the area of the ligands originally co-crystallized with

B2-AR ((S)-carazolol and BI-167107). Next, the MolDock SE search algorithm was implored with

the number of searching runs set to 100. Apart of the search was the following parameters:

population size = 50, maximum iteration = 1500, energy threshold = 100.00, max steps = 300,

the maximum number of poses to generate which is normally 5 was increased to 10. The results

of the docking study were described by the MVD implemented scoring function [22] (MolDock

Score). The lower the MolDock score, the greater the free energy of binding. Critical to any

computational technique validation was completed by running docking simulations of the

molecules originally co-crystallized with their respective protein model. ((S)-carazolol was

docked to PDB ID: 2RH1/Inactive B2-AR, & BI-167107 was docked to PDB ID: 3P0G/Active B2-

AR).

2.1- Molecular Dynamics

2.1.1- Modeling the B2-AR

First, it is key to note that modeling was based off the two selected crystal structure of the B2-

AR [19, 20], which include an inactive crystal structure of human B2-AR-T4 lysozyme fusion

protein co-crystalized with inverse agonist (S)-carazolol and an active crystal structure of a

nanobody-stabilized active state of the β2-AR co-crystallized with the agonist BI-167107 . Next,

we had to use an the ab initio approach to predict the conformations of N-/C- termini and the

second intracellular loop connecting TM V and TM VI, which were not seen in the crystal

structures. To predict the missing amino acid residues of the crystal structures, a human B2-AR

amino acid sequence was obtained from the Swiss-Prot database (code P07550). The modeling

of the N- and C-terminal domains of the receptor (which include residues Met1 to Glu30 and

Cys341 to Leu413 respectively) was completed using an I-TASSER server [22]. The other

predicted component of the B2-AR, was the second intercellular loop of the receptor (which

included residues Leu230 to Leu266), which was estimated using a CABS program [23]. It’s

important to note that the rest of the crystal structure was considered ready for computational

analysis. The next step involved bringing together the predicted components with the accepted

crystal structure through a simulated annealing routine implemented in GROMACS (v. 3.3) [24].

While still in the GROMACS (v. 3.3) program package the PDB2GMX procedure was used to

apply the proper atomics charges for all receptor protein atoms. It was found that the total

charge of the receptor proteins were +2. Lastly the charges were assigned to be in agreement

with a modified GROMOS96 force field [25].

2.1.2- Stimulating the Receptor Models in Membrane

Initially, applying Inflategro procedure [26] the two B2-AR models were inserted into an

equilibrated palmitoyl-oleoyl-phosphatidylcholine (POPC) cell membrane. Next, the addition of

solvated water molecules and ions to the POPC lipid bilayer took place. The two separate

systems consisted of the B2-AR protein (either active or inactive), 125 POPC lipid molecules (due

to computational strength), 16271 water molecules (including 16 water molecules seen in the

B2-AR crystal structures) and two sodium ions. To constrain energy, we applied 2000 steps of

steepest decent algorithm followed by 2000 steps of l-bfgs algorithm. Following that, a four

step molecular dynamics (MD) simulation was executed. At the first step, each system was

simulated for 100 picoseconds with no pressure coupling and constraints to all the protein

atoms to maintain initial positions with the help of a “freeze” option. The next step included a

MD simulation lasting 1 nanosecond with the Berendsen method [27] for pressure coupling and

similar position restraints imposed on all backbone atoms of the receptor model. The third step

of MD simulations lasted 2 nanoseconds, and involved the removal of position restraints from

the loops connecting the seven trans-membrane helices and the N- and C-terminal domains of

the B2-AR model. The 4th and final step included a 40 nanoseconds run with no restraints. The

force field, which is the main calculator of atomic potential energy was a modified GROMOS96

force field (ffG53a6 parameters set) [25] with extra parameters for POPC molecules taken from

Kukol [28]. For the water molecules the SPC water model [29] was used and for treatment of

the long-range electrostatic interactions the PME method [30] was applied. Every bond

involving a hydrogen atom was constrained with the LINCS algorithm [31]. In terms of

temperature the MD was performed at 310 K, and the pressure was at 1013 hPa. Lastly, the

calculations and data analysis were completed by using a GROMACS (v. 3.3) program.

2.1.3- Modeling the Receptor-Ligand Complexes

Initially, we used a PRODRG server [32] to obtain the force field parameters for the MD

simulation and the ligand structures for (R,R’)- and (S,S’)-fenoterol. Next came the geometry

optimization for two ligands (in their protonated-nitrogen forms), which was completed using

Hartree–Fock procedure employing the 6–31G* basis set in Gaussian (v.03 rev. C.02, Gaussian

Inc.) [33]. Also, we obtained the partial charges for the two molecules using the R.E.D.III

procedure [34]. Next, we analyzed our docking data for the lowest-energy structures obtained

during the docking simulations. After that analysis, ligands were inserted into the lowest-energy

structure of the inactive B2-AR model and the active B2-AR model. All receptor-ligand complexes

had similar starting structures during the restrained MD simulation lasting 200 picoseconds. In

that case, the protein backbone atoms were constrained to its initial positions using the

“freeze” option and weak harmonic distance restraints were applied (force constant of 1000 kJ

mol-1 nm-1 and the distance parameters: r0 = 0.0 nm, r1 = 0.3 nm and r2 = 0.5 nm) on three

receptor-ligand atom pairs (Pair 1: An oxygen atom of hydroxyl group of Ser203 and the oxygen

atom of the first meta-OH group of 3,5-dihydroxyphenyl moiety; Pair 2: The oxygen atom of the

hydroxyl group in Ser207 and the oxygen atom of the second meta-OH group of 3,5-

dihydroxyphenyl moiety; Pair 3: The Cγ atom of Asp113 and the protonated nitrogen atom of

the ligand). This was done in order to conserve similar starting position for the two ligands

inside the binding cavity. Next, a two step MD simulation of the receptor-ligand complexes was

performed. The first step lasted 2 nanoseconds, had weak harmonic position restraints imposed

on backbone atoms of trans-membrane helices of the receptor only and ligand-receptor

restraints were released. The second step lasted 5 nanoseconds with no restraints. The two

step MD simulation was repeated 44 times applying random starting velocities for every atom,

11 times for the inactive B2-AR-(S,S’)-fenoterol complex, 11 times for the inactive B2-AR-(R,R’)-

fenoterol complex, 11 times for the active B2-AR-(S,S’)-fenoterol complex and 11 times for the

active B2-AR-(R,R’)-fenoterol complex, respectively. The stimulation parameters were a replica

to that of the MD simulations of the B2-AR models (Section 2.2.2).

3. Results

3.1- pKi vs Docking Scores- Insights into the Native form of the B2-AR

The docking method’s MolDock scoring helped to show us the free energy of binding in both

the active and inactive state of the receptor (table 2). While, the pKi gave us a look into the

binding affinity from previous work with the native B2-AR. By plotting the two (MolDock & pKi)

we will get insights into how the active and inactive states of the B2-AR compare with the native

state of the receptor.

Looking at figure eight, we obtain two major pieces of information. The first major piece of

information can be seen on the left graph, which looks at the correlation coefficient (R2)

between the Moldock scores and pKi. With a correlation coefficient of 0.6251, there seems to

be a reasonable correlation between the

inactive state of the B2-AR and the native

state. On the right side of the graph, we get a

visual of the various stereo-chemical

configurations and their MolDock scores. The

right side confirms that the (R,R’)-stereo

configurations have the greatest magnitude of

binding free energy.

Looking at figure nine, we obtain three major

pieces of information. The first major piece of

information, which can be seen on the left

graph, is the correlation coefficient (R2)

between the Moldock scores and pKi. With a

correlation coefficient of 0.3116 there

Table 2- Shows fenoterol & derivatives with the

MolDock scores.

Figure 8- Graphs which measure the dependency of the experimentally determined affinity (pKi)

values and the MolDock Score function values for all the chemicals found on table 1 on the inactive

B2-AR.

seems to be little correlation between the active state of the B2-AR and the native state. On the

right side of the graph, we get a visual of the various stereochemical configurations and their

MolDock scores. The right side confirms that the induced fit model of receptor-ligand

interactions is correct, due to the increase in the magnitude of binding free energy. Also, the

third piece of information figure 9 seems to show is that the stereochemical configurations of

the ligands matter less when the receptor is active.

To compare, the major results from figure 8 & 9 show that the inactive receptor is more closely

aligned to the native receptor. Also, based off the MolDock scores, binding is tighter with the

ligand when the receptor is in the active state.

3.2- Experimentally Proposed Binding Sites (Inactive Receptor & Active Receptor)

A second result that can be concluded from the docking method’s MolDock scoring is the

binding pocket for the B2-AR in the active and inactive state. Weak interactions drive binding,

Figure 9- Graphs that measure the dependency of the experimentally determined affinity (pKi)

values and the MolDock Score function values for all the chemicals found on table 1 on the active

B2-AR.

by knowing the key weak interactions, we can design a more selective drug for the B2-AR. The

binding pocket study focused on the conserved residues.

Looking at figure ten alone, we obtain no major pieces of information, but when combined with

figure 11 and figure 12, we obtain major clinically significant results. In the case of both B2-AR

models (figures 10,11), the 3,5-dihydroxyphenyl and amine moieties of the docked ligands lie in

the orthosteric site, which is located between TM III, TM V, TM VI and TM VII. The orthosteric

Figure 10- Computed binding site and scheme for the possible interactions in the (R,R’)-fenoterol

and inactive B2-AR complex. Red lines denote hydrogen bonds and green lines denote hydrophobic

interactions ( Π-Π interactions).

site is simply, the location where epinephrine and norepinephrine would bind in an non-

modulated form of the receptor. Interestingly, the largest homology between the two proposed

binding sites exists in the orthosteric pocket. One key homologous weak interaction in ligand

binding is the hydrogen bonding that occurs with the 3,5-dihydroxyphenyl moiety, in both cases

with the B2-AR, the hydrogen bonding takes place with residues found on TM domain V. The

conserved interactions include Ser203 & Ser207 H-bonding, but in the case of the inactive

receptor Thr118 can play a role in the H-bonding. Another significant weak interaction involves

Figure 11- Computed binding site and scheme for the possible interactions in the (R,R’)-fenoterol

and active B2-AR complex. Red lines denote hydrogen bonds and green lines denote hydrophobic

interactions ( Π-Π interactions).

the protonated nitrogen found on all of the ligands, forming a salt bridge with Asp113 in both

the active and inactive state of the B2-AR. That very same Asp113 also plays a role in stereo-

specificity, by only hydrogen binding with the β-OH moiety when the ligands have an (R,*)

confirmation. The major key difference with the proposed orthosteric site between the active

and inactive B2-AR, exists with Asn312 side chain’s ability to form dual hydrogen bonds with

both the β-OH and the protonated amine moiety found on all ligands. The additional dual

hydrogen bonding may account for some of the considerable difference in binding between the

active and inactive state of the B2-AR.

The extended orthosteric pocket includes interactions with the ligand molecule‘s methyl moiety

located on the α’ carbon atom of the compound (the second chiral center) and the ligand

molecule’s phenyl/naphthyl rings. In the case of both the active and inactive state of the B2-AR

model, the ligand molecule’s 4'-hydroxy-/4'-methoxy-/4'-amino-phenyl and methoxynaphthyl /

naphthyl moieties of fenoterol derivatives can potentially assume four possible hydrogen-

bonding interactions (Fig. 9 & 10), which can be with Cys191 or Asp192 (from ECL2) (IIA site) or

Thr110 (TM III) (IIB site) or Tyr308 (TM VII) (IIC site). One key difference in the hydrogen binding

between the active and inactive state is the presence of a Lys305 in the active state of the

receptor which forms stronger hydrogen bonding with the 4'-hydroxy-/4'-methoxy-/4'-amino-

phenyl and methoxynaphthyl / naphthyl moieties of the fenoterol derivatives. Lastly, all ligands

with an aromatic group in the orthosteric extended pocket also enjoy pi-pi interactions with

either Tyr308 (TM VII) or Phe193 residue (ECL2).

3.3- Confirmation of a Similar Binding Pocket for the B2-AR

While the experiment’s primary focus was on fenoterol and its derivatives, we were also able to

identify the position of binding with respect to the originally co-crystallized ligands. Using the

docking study’s greatest magnitude of binding energy, we superimposed images from the co-

crystalized ligand on top of an image of (R,R’)-fenoterol (Fig. 12). The significance of this finding

is that the binding pocket for the B2-AR is conserved for a diverse group of ligands. Figure 13

clearly shows the major structural differences between (R,R’)-fenoterol, (S)-carazolol, and BI-

167107. This finding has significant ramifications for drug design in combating congestive heart

failure.

Figure 12- These images superimpose the positions of the original ligands co-crystallized with β2-AR

(in gray), (S)-carazolol (PDB ID: 2RH1) (a) and BI-167107 (PDB ID: 3P0G) (b) with (R,R’)- fenoterol

docked to the inactive B2-AR (a) and the active B2-AR model (b). (R,R’)- Fenoterol takes a similar

position to that of (S)-carazolol and BI-167107 present in the crystal structures of B2-AR. The

positions of (R,R’)-fenoterol, is the lowest scoring function values for (R,R’)-fenoterol.

Figure 13- Structural diversity of ligands is seen in this figure. On the left, the structure of fenoterol,

in the middle the structure of (S)-carazolol, and on the right it’s the structure of BI-167107.

3.4- Confirmation of Stereo-chemical Preference (Docking)

One of the primary reasons we are investigating the B2-AR is the interesting experimental

affinities of previous work which claims that stereochemistry plays a crucial role in the

interactions with the receptor [18]. When combining Fig. 12’s (R,R’) binding with Fig. 14’s (S,S’)

binding, we can clearly see that when comparing the docking of (S,S’)-fenoterol to the inactive

B2-AR model to the docking of (R,R’)-fenoterol, there is little difference. In fact, the only

difference lies in the reorientation of moieties associated with the two chiral centers, which are

the β-OH and –CH3 groups. These orientations are in the opposite directions. As a result, the β-

OH moiety of (S,S’)-fenoterol does not hydrogen bond with Asp113 in the inactive B2-AR and

the methyl moiety does not exhibit favorable orientation towards the hydrophobic surface of

the binding site formed by TM III (Trp109) and ECL2 (Phe193). These inverted positions cause a

difference in 9.0 kJ/mol between the (R,R’) and (S,S’) states.

Interestingly, the docking of (S,S’)-fenoterol to the active B2-AR reveals more significant

differences when compared to the docking of (R,R’)-fenoterol to the active B2-AR. First, the 3,5-

Figure 14- Shows a direct comparison between the structural models of (S,S’)-fenoterol binding to

the inactive B2-AR (a) and the (S,S’)-fenoterol binding to the active B2-AR (b). Note the position of

K305 in the inactive and active receptor (S,S’)-fenoterol binding.

dihydroxyphenol moiety of (S,S’)-fenoterol is significantly reoriented, so that only one meta-OH

moiety is able to form a hydrogen bond with Ser203, versus the (R,R’)-fenoterol which has both

meta-OH groups interacting with Ser207 and Ser203, respectively. Moreover, the β-OH moiety

of (S,S’)-fenoterol doesn’t form hydrogen bonds with Asp113 in the active state B2-AR, while

the (R,R’)-fenoterol’s β-OH does form hydrogen bonds with Asp113. Strangely enough, these

significant differences between docked (R,R’)-fenoterol to the active B2-AR and (S,S’)-fenoterol

docked to the active B2-AR, aren’t noticeably in the MolDock Score values, which are equal to -

133.5 and -134.6 kJ/mol, respectively (Table 2). The inactive B2-AR crystal structure better

predicts the stereoselective binding effects, when compared to the active B2-AR crystal

structure.

3.5- Influence of the R1 & R2 substituents on Binding

To study the influence of R1 and R2 substituents we selected two fenoterol derivatives, which

are (R,R’)-52 and (R,R’)-54 (Table 2). For each, we compared the binding modes with the

inactive and active form of the receptor (Fig. 15, 16 (a, b)). First, on the (R,R’)-52 we had an R1

substituent that replaced fenoterol’s methyl moiety for an ethyl group attached to the second

chiral center. The effects of this change, include a reorientation of the 4'-hydroxyphenyl moiety

when bound to the active state of the B2-AR and compared to (R,R’)-fenoterol docked to the

active B2-AR. The reorientation of the 4'-hydroxyphenyl moiety, leads to a disruption in its

interaction with Lys305, therefore lowering the magnitude of the binding free energy. All other

significant interactions that are present in the (R,R’)-fenoterol’s docking to the active B2-AR is

parallel to the docked (R,R’)-52. When being docked to the inactive receptor, no significant

difference between (R,R’)-52 and (R,R’)-fenoterol is found.

When it comes to the (R,R’)-54 molecule, we see an R2 substituent, which contains a 4’-

methoxy-1-naphthyl moiety at the aminoalkyl tail. Despite the much larger group this

modification does not produce very significant changes in the position of the molecule within

the active state of the B2-AR when compared to the position of (R,R’)-fenoterol. Almost all of

the principal interactions outlined in section 3.2 are conserved for the docking of (R,R’)-54 into

the active state of the B2-AR, Fig.16 (b). Interestingly, more significant differences can be

noticed for (R,R’)-54 docked into the inactive B2-AR, Fig.16 (a). In this case, reorientation of the

ligand molecule takes place which prevents the β-OH group from creating a hydrogen bond

with Asp113. Another difference when docked to the inactive state involves the bulky 4-

methoxy-1-naphthyl moiety, which gets pushed into the center of the binding site due to its

strong interaction with Tyr308. The obtained results confirm the influence of R1 and R2

substituents in the fenoterol molecule (Table 1) on its binding to B2-AR, which lines up very well

with the experimentally determined pKi values.

Figure 15- Shows a direct comparison between the structural models of the R1 ethyl substituent,

(R,R’)-52 & inactive B2-AR complex (a) and the (R,R’)-52 & active B2-AR complex (b). Note that the

K305 H-bonding has been disrupted in (b).

3.6 - Confirmation of Stereo-chemical Preference (Molecular Dynamics)

Molecular dynamics was selected to give us more information about the stereo-chemical

preference of the two models for the B2-AR. To maintain project integrity, figure 17 was

included, which shows that both ligands were inserted in the middle of the receptor binding

site. During the 44 MD simulations with no restraints (11 MD simulations for each fenoterol

stereoisomer–receptor complex), we obtained differences in the conformational stability that

fenoterol stereoisomer had inside the receptor binding sites.

Figure 16- Shows a direct comparison between the structural models of the R2 methoxy -1 –

naphthyl substituent, (R,R’)-54 & inactive B2-AR complex (a) and the (R,R’)-54 & active B2-AR

complex (b). The K305 H bonding has been strengthened (b).

Figure 17- Shows the starting positions of (R,R’)-fenoterol (red), and (S,S’)-fenoterol (blue), inside the

inactive B2-AR (A) and active B2-AR (B).

A thorough investigation of the 44 MD simulations, displayed a higher conformational stability

for the (R,R’)-fenoterol when compared to the (S,S’)-fenoterol stereoisomer. Stability of the two

ligands was monitored in the form of RMSD autocorrelation function. In both, the case of the

active and inactive B2-AR, we observe that the averaged RMSF values are lower for (R,R’)-

fenoterol isomer. RMSF is simply the calculation of root mean square fluctuation (RMSF) for the

position ligand atoms.

The blue and green boxes

indicate specific

fragments of two

fenoterol isomers

showing the highest

difference in RMSD

fluctuation (Fig. 18).

Figure 18- The charts show the average movement (RMSF values) of ligand atoms during 5 ns of MD

simulation of ligand & inactive B2-AR (top) and ligand & active B2-AR (bottom). The (R,R’)-fenoterol

values are in red, and the (S,S’)-fenoterol values are in black. The green and blue rectangles

represent the greatest delta in RMSF values. Charts confirm that (R,R’) has is more stable when

interacting with both forms of the receptor.

4. Discussion

4.1- General Conclusions

Computational analysis of the B2-AR has left us with a far better understanding of the native

state, binding pocket, and stereochemical preference of this valuable GPCR. When it comes to

the native state, we were assisted by previous work [18], which primarily focused on the

binding affinities of Table 1’s various derivatives of fenoterol. By plotting those various binding

affinities against our docking scores (Fig.8 & 9) we are able to conclude that the native state of

the receptor more closely resembles the inactive state of the B2-AR. From the same data (Fig.8

& 9) we were also able to reaffirm from the delta in the docking scores between the active and

inactive state that ligand-receptor interactions are based off an induced fit model, not the more

traditionally accepted lock-key model.

Our most critical findings were the proposed binding pockets (Fig.10 & 11) for the active and

inactive B2-AR. First, we were pleased to discover that (S)-carazolol, BI-167107, fenoterol, and

its derivatives (table 1) took similar positions in the binding cavity of the B2-AR, especially in the

orthosteric site (Fig. 10/11/12/13). Since we were dealing with two states of the receptor, our

key interests lied in the conserved residues involved with binding in both states, which probably

play a huge role in the native B2-AR binding of any ligand. One similarity (Fig.10 & 11) include

the meta-OH groups of the tested compounds, which create hydrogen bonds with Ser203 found

on TM V. A second similarity involves a salt bridge interaction between the protonated amine

of the ligands and the Asp113 sidechain (TM III). These similarities could directly affect how the

next generation congestive heart failure and asthma medication will be designed.

Stereochemistry and its effects on the binding of ligands to the B2-AR, is quite possibly the

largest question we wanted to answer in this experiment. To better understand why certain

stereoisomers of fenoterol have differing binding affinities to the B2-AR we used docking

studies. Through those studies we were able to see several substantial patterns between ligand

R/S configurations and binding free energy. The molecular docking scores (table 2) are in

accordance with the experimental data and indicate that the most potent agonists are those

with (R,R’)-configuration. Docking studies also indicate that the hydroxyl group at the first chiral

center of ligand creates HB with Asp113 or/and Asn312 in the case of (R,R’)-stereoisomers

mainly. The stereochemistry was also studied in a molecular dynamics stimulation, which pitted

(R,R’)-fenoterol against (S,S’)- fenoterol. Molecular dynamics shows a stark difference between

fenoterol ‘s (R,R’) and (S,S’) stereoisomers, when it comes to the stability of the atoms that are

involved. Overall, molecular dynamics simulations reveal that the change in atomic space is

larger in the case of (S,S’)-fenoterol when compared to the of (R,R’)-fenoterol (Fig.16). The

largest differences between (R,R’)- and (S,S’)-fenoterol were observed in the region containing

the β-OH, (– NH2+ –), and 4’-OH functional groups, which interact the strongest with the

receptor (Fig. 18).

4.2- Future Studies

Despite all these computational analysis, we are still left with very real questions. One of these

questions is just how selective fenoterol and its derivatives are for the B2-AR. It is key to note

that the B1-AR promotes congestive heart failure [8], thus agonizing the B1-AR, would be

counterproductive to the B2-AR cardioprotective role. To test this question one will have to look

to the PDB and find models for the B1-AR, such as PDB ID: 3ZPQ. Once obtained that receptor

will be modified to remove any non-receptor co-crystalized molecules, then docking studies

similar to the ones included in this experiment will be run with fenoterol and its derivatives.

Running this experiment will give us an idea of the selectivity of these ligands for the B2-AR.

A second question, can we design an agonist with a higher binding affinity to the B2-AR with the

results from these computational studies? I believe we can, first by looking at making a

derivative of fenoterol. It is clear from the results (table 2) of the docking studies that any R1

substituent change will lead to lower free energy of binding, thus I would leave a methyl on the

R1 component of the derivative. On the R2 side of things, I would replace the phenol with a

hydroxylated- napthyl moiety. I believe that the OH side chain will bind more tightly to the

receptor then the methoxy found on compound 54. Then docking studies similar to the ones

included in this experiment will be run with the newly synthesized fenoterol derivative and the

B2-AR.

A last question, can we apply these computational techniques to get further understanding of

other clinically significant GPCR. The one limitation would be the number of non-crystalized

GPCR’s would not be studied. Taking PDB models for critical GPCRs, such as the muscarinic

receptor (PDB ID: 4MQS). Next, we would model agonist such as iperoxo using HyperChem.

Lastly, we would run a docking study using the PDB GPCR and agonist, to obtain a model of the

binding pocket for the GPCR, which will lead to improved drug design.

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