ORIGINAL PAPER

Study of the selectivity of a1-adrenergic antagonists by molecularmodeling of a1a-, a1b-, and a1d-adrenergic receptor subtypesand docking simulations

Slavica Eric • Tom Solmajer • Miha Kotnik •

Mire Zloh • Danica Agbaba

Received: 13 February 2013 / Accepted: 10 March 2013 / Published online: 27 March 2013

� Springer-Verlag Wien 2013

Abstract Modeling of a1a, a1b, and a1d adrenergic

receptor subtypes has been performed using InsightII

software and bovine rhodopsin as a template. Adrenaline

and noradrenaline, as endogenous agonists, were docked

to validate the developed models, explore the putative

binding sites, and calculate relative docking scores.

a1-Adrenergic antagonists with the highest order of selec-

tivity and activity at specific receptor subtypes were then

chosen for docking into the corresponding receptor models.

Docking simulations were performed using the FlexX

module implemented in the Sybil program. PMF scoring

functions of the obtained complexes calculated as relative

to PMF scoring functions for noradrenaline-receptor sub-

type complexes were then used for correlation with

selectivity on different a1-adrenergic subtypes. Good cor-

relations were obtained for most receptor subtype-

selectivity pairs: (1) using PMF scores calculated for

ligands in complex with a1a-receptor subtype, r = 0.7503

for a1a/1b and r = 0.6336 for a1a/1d selectivity; (2) using

PMF scores calculated for ligands in complex with a1b

receptor subtype, r = 0.7632 for a1a/1b and r = 0.7061 for

a1b/1d selectivity; (3) using PMF scores for ligands in

complex with a1d receptor subtype, r = 0.7377 for a1a/1d

and r = 0.9913 for a1b/1d selectivity.

Keywords a1-Adrenergic antagonists �a1-Receptor subtype selectivity � a1-Receptor modeling �Agonists/antagonists PMF scores

Introduction

a1-Adrenergic receptors (a1-ARs) are transmembrane pro-

teins that mediate the response of various tissues to

endogenous catecholamines released by the sympathetic

nervous system. These receptors are important in maintain-

ing the homeostasis of the cardiovascular system, mainly

through regulating the contraction of smooth muscle cells of

the vascular endothelium. Three distinct a1-adrenergic

receptor subtypes—a1a, a1b, and a1d—have been charac-

terized, which share similarities in signal transduction

pathways, but have a significantly different primary structure

and varied tissue distribution [1–3]. These differences

present a basis for distinct physiological roles that the three

subtypes are believed to express [4].

Considering they mediate the autonomous contractions

of smooth muscle cells, a1-adrenergic receptors are

potential targets in treatment of hypertension and benign

prostate hyperplasia (BPH). This fact has led to the

development and clinical use of a number of a1-adrenergic

receptor antagonists, which vary in their subtype selectivity

[5]. However, the therapeutic potential of these drugs has

been limited by their side effects, principally the occur-

rence of postural hypotension, with additional concerns

being raised regarding their long-term effects on cardiac

adaptation to stress [4]. Although the specific roles of

individual a1-ARs have not been fully elucidated, design of

S. Eric (&) � D. Agbaba

University of Belgrade-Faculty of Pharmacy,

Vojvode Stepe 450, 11 000 Belgrade, Serbia

e-mail: seric@pharmacy.bg.ac.rs

T. Solmajer � M. Kotnik

National Institute of Chemistry, Hajdrihova 19,

1000 Ljubljana, Slovenia

M. Zloh

School of Pharmacy, University of London,

Brunswick Square, London, UK

123

Monatsh Chem (2013) 144:903–912

DOI 10.1007/s00706-013-0966-y

subtype-specific antagonists is believed to present a

rational approach in developing novel potent treatments for

hypertension and BPH, with minimal short- and long-term

cardiovascular side effects [5, 6].

a1-Adrenergic receptors belong to the G protein-coupled

receptor (GPCR) superfamily, for which, despite their high

significance as drug targets, high-resolution structural

information is not widely available. The first crystallo-

graphic structures of human GPCRs have been published

only very recently, including the b2-adrenergic receptor

[7–10], adenosine A2A [11–13], and chemokine CXCR4

receptors [14]. a1-Adrenergic receptors are presumed to

share the common topology of all class A GPCRs, with the

transmembrane bundle of the receptor formed by seven

antiparallel helices that are joined by a number of intra-

and extracellular loops. Although the agonist binding site

of a1-AR has been relatively well characterized, much less

is known about the way antagonists interact with the

receptor. Most mutagenesis studies have shown there is an

overlap between the two binding sites, although antagonists

are thought to bind closer to the extracellular surface [15].

Various ligand-based drug design approaches have been

used for exploring the molecular basis of antagonists’

binding to a1-AR subtypes and their potential selectivity.

In particular, a number of pharmacophoric models have

been proposed [16–19], including those for specific

receptor subtypes [20, 21]. Our group previously reported

several QSAR and pharmacophoric models capable of

predicting subtype selectivity for a diverse set of a1-AR

antagonists [22, 23]. The application of 3D-QSAR mod-

eling has also been described by different authors [24, 25].

Although ligand-based approaches have been applied with

some degree of success, the relative similarity of different

subtypes’ binding sites means that the attempt to design

subtype-selective agents would benefit from better under-

standing of receptors’ three-dimensional structure.

Structure-based exploration of ligand selectivity has been

limited, however, by the lack of adequate crystallographic

structures and has relied on the use of receptor models

developed by homology modeling methods.

Most of the published homology models and docking

studies have focused on examining key residues and

binding site characteristics that are important for ligand-

protein interactions [26, 27]. While hypotheses established

in these studies can present a basis for subsequent site-

directed mutagenesis experiments [28, 29], their applica-

bility in designing new leads tends to be limited by the

rough accuracy of any model developed using low-

homology templates, which remain the only ones available.

Conversely, such homology models have already been

shown as applicable in the screening and preliminary

evaluation of potential new subtype-selective ligands [30].

In this respect, even with presently available templates,

refinement of the way in which docking scores are inter-

preted could be useful in improving subtype selectivity

predictions. One way of achieving this is by utilizing

existing scoring functions in a new way, taking into

account the fact that binding of antagonists to the receptor

actually takes place in the presence of endogenous

agonists.

As a continuation of our previous efforts into exploring

the molecular basis for subtype selectivity of a1-adrenergic

receptor antagonists [22, 23], we present the development

of three homology models of human a1-AR subtypes and

docking studies with a diverse set of known antagonists.

We have attempted to show that the developed models and

docking results can be used to rationalize subtype selec-

tivity of analyzed ligands. To this aim, we present the use

of relative docking scores, adjusted by the scores of

endogenous agonists—adrenaline and noradrenaline—

which correlate to selectivity values better and thus

improve the usefulness of the developed models.

Results and discussion

Homology-based molecular modeling of a1-ARs (as

reviewed in Ref. [31]) can, together with docking studies,

offer insights into the interaction of antagonists with their

putative binding site. The reliability of developed models

and any results is, however, highly dependent on the choice

of template structure and incorporation of available

experimentally determined structural information into the

modeling process [32]. Initial efforts in the field were based

on bacteriorhodopsin as a template, which has since been

superseded by the crystallographic structure of bovine

rhodopsin in its dark state. Although sequence identity

between bovine rhodopsin and most human GPCRs,

including a1-ARs, is below the 20 % considered a thresh-

old for the development of reliable homology models,

structures of bovine rhodopsin seem to present a valid

template for modeling GPCRs, which has recently been

examined by several comparative analyses involving

recently published crystallographic structures [33, 34].

Different aspects of homology modeling of GPCRs based

on bovine rhodopsin have been discussed elsewhere in

greater detail [35], where for the estimation of docking

energies, the results of docking of different antagonists on

a1-ARs subtypes have been explored. However, there are

no data showing that energies of binding of endogenous

ligands on different a1-ARs subtypes were also taken into

account, which was the concept of our work. In order to

evaluate the usefulness of calculating relative docking

binding energies as described in this study, we wanted to be

certain that our results are directly comparable with pre-

vious docking studies of a1-AR antagonists. We therefore

904 S. Eric et al.

123

decided to use the bovine rhodopsin template for devel-

oping the homology models. After performing homology

modeling, we determined active sites for a1a, a1b, and a1d

receptors, presented in Fig. 1, by initially exploring the

binding modes of noradrenaline and adrenaline as ligands

whose interaction with a1-adrenergic receptor has been

well characterized experimentally. It could be observed

that binding sites of all three subtypes of receptors differ in

size and shape.

Site-directed mutagenesis studies of a1-adrenergic

receptors have established that binding of agonists involves

key interactions between D117 in the transmembrane helix

3 and a protonated amine of the agonist as well as hydro-

gen-bond formation between m- and p-hydroxyl groups of

catecholamines and serine residues S207 and S211 in helix

5 [36–38]. Complexes of adrenaline and noradrenaline

obtained by docking with a1a, a1b, and a1d-adrenergic

receptor subtypes are shown in Fig. 2. The obtained

binding modes suggest that the interactions between key

preserved residues and catecholamines are present in the

binding site of all three receptor subtype models and are in

agreement with available experimental data.

Then docking studies of selected ligands were per-

formed on a1a, a1b, and a1d receptors, as described in

Experimental. We used the same ligands, the most active

on different ARs subtypes, as we used for the pharmaco-

phore mapping described in our previous work [23]. In

Fig. 3, the complexes of abanoquile and the a1a, a1b, and

a1d receptor are illustrated.

PMF scoring functions of the obtained complexes calcu-

lated relative to PMF scoring functions for noradrenaline-

receptor subtype complexes were then used for correlation

with selectivity. This was done with the assumption that they

would better reflect the competitive binding of antagonists to

the receptor in the presence of endogenous agonists. This

approach allows the differences in affinity of endogenous

catecholamines for different receptor subtypes to be reflected

on PMF scores.

The calculated relative PMF scores and experimental

measures of antagonist subtype selectivity are shown in

Tables 1, 2, and 3 for a1a, a1b, and a1d-adrenergic receptor

subtypes. The correlation between relative scoring func-

tions and selectivity on a1a, a1b, and a1d receptors is

presented in Fig. 4.

Good correlations were obtained for most receptor

subtype-selectivity pairs. Notably, the correlations derived

using relative PMF scores were significantly better than

those calculated for unweighted PMF scores. This can

probably be attributed to the fact that by establishing a

reference value—the PMF score for a non-selective ligand-

receptor complex—the calculated relative PMF scores

better reflected variance in factors responsible for subtype

selectivity of the analyzed ligands.

In conclusion, a variety of different steric and electro-

static factors influence the interaction between the studied

ligands and a1-adrenergic receptor subtypes. We have

shown that receptor models based on homology to bovine

rhodopsin and the docking methodology used provide a

robust means for correlating relative docking scores with

the selectivity of studied ligands. The developed models, in

combination with other approaches, could be useful in

identifying novel subtype-selective antagonists of the a1-

adrenergic receptor.

Materials and methods

Homology modeling of a1-adrenergic receptors

The structure of bovine rhodopsin resolved at 2.6 A using

X-ray diffraction [39] (PDB ID:1L9H) was used as a

template for homology modeling of a1-ARs. Protein

Fig. 1 Homology models of

a1a, a1b, and a1d-adrenergic

receptor subtypes (left to right),developed using InsightII.

Opaque surfaces represent the

determined active sites, which

differ in size and shape

Molecular modeling of a1a-, a1b-, and a1d-adrenergic receptor subtypes and docking simulations 905

123

Fig. 2 Binding modes of adrenaline and noradrenaline (displayed as solid stick models) docked into active sites of a1a (top left), a1b (top right),and a1d-adrenergic receptor (bottom) models

Fig. 3 Binding mode of abanoquil (displayed as solid sticks model) docked into active sites of a1a (top left), a1b (top right), and a1d-adrenergic

receptor (bottom) models. Abanoquil was used as a reference ligand for docking of remaining compounds

906 S. Eric et al.

123

sequences of bovine rhodopsin, human a1a, a1b, and a1d

adrenergic receptors were retrieved from the UniProt

Knowledgebase (http://www.uniprot.org/) and aligned

using the multiple sequence alignment tool Clustal W [40].

Molecular modeling was carried out using the InsightII

suite of programs [41] and in accordance with a previously

described modeling procedure [42]. Initially, the structure

of the a1b-receptor subtype was modeled by a rigid-body

assembly approach. The positioning and orientation of

conserved amino acids forming the transmembrane helices

bundle of bovine rhodopsin was kept unaltered and formed

the rigid backbone of the model. Intra- and extracellular

loops were modeled de novo. Energy minimization of the

resultant model was performed, and the model was sub-

jected to a molecular dynamics run. The final a1b-AR

model was then used as a template for modeling a1a and

a1d-receptor subtypes, following the same procedure.

Molecular docking studies and scoring function

A number of structurally diverse a1-AR antagonists from a

previously published data set [16] were used in docking

studies. Antagonists with the highest order of selectivity

and activity at specific receptor subtypes were chosen for

docking into the corresponding receptor models. These are

shown in Table 4. To validate the model complex pre-

sented herein from the ligand perspective, both adrenaline

and noradrenaline, as endogenous agonists, were docked to

validate the developed models, explore the putative bind-

ing sites, and calculate relative docking scores.

Structures of all ligands were modeled and geometrically

optimized using the AM1 semi-empirical method imple-

mented in the Spartan software [43]. Optimized molecules

were then imported into Sybil [44] and preprocessed

through several steps. Docking studies were to be based on

the assumption based on literature data that the protonated

nitrogen of ligands interacts with an aspartate residue in

transmembrane helix 3 [45]. Accordingly, explicit hydro-

gens and formal charges were added to all ligand molecules

to enable detection of ionic interactions. Additional energy

minimization was performed using the TRIPOS force field.

Finally, possible interaction types were assigned to ligands

based on a database of known interactions.

Docking simulations were performed using the FlexX

module of the Sybil program. FlexX facilitates docking of

flexible ligands, including automatic ring conformer gen-

eration, to a rigid receptor structure. A prerequisite for

docking using FlexX is user definition of the protein-

binding site. Based on published data from experimental

studies, it was assumed that binding sites for a1-adrenergic

receptor agonists and antagonists essentially overlap [46]

and that the formation of an ionic bond between an

aspartate residue in helix 3 (D117) of the receptor and a

protonated nitrogen atom of the ligand is key for binding

[45]. It has also been hypothesized that a serine residue in

helix 5 (S207) of a1b-AR is involved in interactions with

the catechol phenolic group of epinephrine [47], which was

also explored.

With these assumptions regarding key amino acids

involved in protein-ligand interactions, a putative binding

site was identified in the apo form of the receptor using the

method proposed by Peters et al. [48]. Abanoquil was then

Table 1 Relative PMF scores calculated for a1a-adrenergic receptor

subtype and selectivities of the compounds of the corresponding

ligand set

Ligand Relative PMF score -logKi

(a1a/1b)

-logKi

(a1a/1d)

Prazosin 2.8897 0.0969 0.2041

Abanoquil 1.5568 0.3010 0

(?)-Niguldipine 0.8545 2.5229 2.8239

5-Methyluropidil 1.5265 1.7959 1.2007

KMD-3213 -0.5660 2.6990 1.6990

Indoramin 0.8316 1.0000 1.6020

RS-17053 1.6109 1.4318 1.4318

SNAP-1069 0.7232 1.0969 1.6990

Bromotopsentin 0.6209 1.9031 2.0969

Table 2 Relative PMF scores calculated for a1b-adrenergic receptor

subtype and selectivities of the compounds of the corresponding

ligand set

Ligand Relative PMF score -logKi (a1a/1b) -logKi (a1b/1d)

Cyclazosin -1.0769 -1.9652 1.3872

Abanoquil 1.5565 0.3010 -0.3010

REC-15/2615 1.7525 -0.8014 0.9393

AH-11110A -1.2717 -1.5171 1.5528

Spiperon 0.4497 0.0633 1.4202

Table 3 Relative PMF scores calculated for a1d-adrenergic receptor

subtype and selectivities of the compounds of the corresponding

ligand set

Ligand Relative PMF score -logKi(a1a/1d) -logKi(a1b/1d)

Abanoquil 1.3951 -0.5740 -0.3010

BMY-7378 0.7718 0.4012 -2.0000

SKF-104856 1.1775 0.1564 -1.0833

Discretamine 1.1223 -0.2332 -1.1584

SNAP-8719 0.9079 -0.1872 -2.0769

Molecular modeling of a1a-, a1b-, and a1d-adrenergic receptor subtypes and docking simulations 907

123

docked into this binding site and treated as a reference

ligand for all three receptor subtype models. This was done

with respect to the fact that abanoquil displays a balanced

activity profile at all three a1-receptor subtypes. Protein

residues located within 6.5–8 A from the docked abanoquil

were identified and were used to define the binding site

boundaries for further docking experiments. Ionic interac-

tion with a deprotonated D117 was additionally defined as

an essential interaction constraint. Water molecules were

treated implicitly during all docking simulations.

Binding modes were scored using the potentials of mean

force (PMF) function proposed by Muegge and Martin

[49]. The obtained PMF scores for each final receptor-

ligand complex were weighted with the PMF scores for the

noradrenaline-receptor complex (Eq. 1) and then correlated

to experimentally obtained measures of selectivity of

docked antagonists.

Relative PMF score ¼ PMFscoreligand�receptorcomplex

PMF scorenoradrenalin�receptorcomplex

ð1Þ

Fig. 4 Graphs presenting linear correlations between relative PMF scores and corresponding selectivity values: a1a/1b, a1a/1d, and a1b/1d.

Relative PMF scores were calculated for ligands in complex with a1a (a, b), a1b (c, d), and a1d-adrenergic receptor (e, f)

908 S. Eric et al.

123

Table 4 Structures of ligands used in molecular docking studies with different a1-adrenergic receptor subtypes

No Compound name Structure Activity Ki/nM Selectivity

a1a a1b a1d a1b/a1a a1d/a1a a1b/a1d

Ligands used in docking simulations with the a1a-AR model

1 Prazosin 0.20 0.25 0.32 1.25 1.60 0.78

2 Abanoquil 0.04 0.08 0.04 2 1 2

3 (?)-Niguldipine 0.15 55 100 367 667 0.55

4 5-Methyluropidil 0.63 40 10 63.5 15.9 4

5 KMD-3213 0.04 20 2 500 50 10

6 Indoramin 4.00 40 160 10 40 0.25

7 RS-17053 0.60 16 16 26.7 26.7 1

Molecular modeling of a1a-, a1b-, and a1d-adrenergic receptor subtypes and docking simulations 909

123

Table 4 continued

No Compound name Structure Activity Ki/nM Selectivity

a1a a1b a1d a1b/a1a a1d/a1a a1b/a1d

8 SNAP-1069 16 200 790 12.5 49.4 0.25

9 Bromotopsentin 12000 740 – 16.22 0 0

Ligands used in docking simulations with the a1b-AR model

1 Cyclazosin 12 0.13 3.20 0.01 0.27 0.04

2 Abanoquil 0.04 0.08 0.04 2 1 2

3 REC-15/2615 1.90 0.30 2.60 0.16 1.37 0.12

4 AH-11110A 6.30 2.00 2.50 0.32 0.40 0.80

5 Spiperon 7.90 0.50 13 0.06 1.65 0.04

910 S. Eric et al.

123

Acknowledgments This work is supported by the Ministry of Sci-

ence of Republic of Slovenia (Grant No. P1-0012 and P1-0340) and

the Ministry of Education and Science of Republic of Serbia (Grant

No. 172009).

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