Molecular Modelling ofEstrogen-Producing Enzymes CYP450Aromatase and 17β-Hydroxysteroid

Dehydrogenase Type 1

Sampo Karkola

Laboratory of Organic ChemistryDepartment of Chemistry

Faculty of ScienceUniversity of Helsinki

Finland

Academic Dissertation

To be presented with the permission of the Faculty of Science of the University ofHelsinki for public criticism in Auditorium A110 in the Department of Chemistry,

February 28th, at 12 o’clock.

Helsinki 2009

SupervisorProfessor Kristiina WahalaLaboratory of Organic ChemistryDepartment of ChemistryFaculty of ScienceUniversity of HelsinkiFinland

ReviewersProfessor Wolfgang SipplInstitute of PharmacyFaculty I of Natural Science–Biological ScienceThe Martin Luther University of Halle–WittenbergGermany

Professor Jerzy AdamskiGenome Analysis CentreInstitute of Experimental GeneticsGerman Research Center for Environmental HealthGermany

OpponentDr. Hugo Kubinyiass. Professor of Pharmaceutical ChemistryUniversity of HeidelbergGermany

ISBN 978-952-92-4942-8 (paperback)ISBN 978-952-10-5186-9 (PDF)

Edita Prima OyHelsinki 2009

ii

Contents

1 Introduction 1

2 Estrogen biosynthesis 5

3 CYP450 aromatase 9

3.1 Substrate recognition sites . . . . . . . . . . . . . . . . . . . . . 12

3.2 Mechanism of the catalytic reaction . . . . . . . . . . . . . . . . 13

3.3 Homology modelling of aromatase . . . . . . . . . . . . . . . . 15

3.4 Mutagenesis studies on aromatase . . . . . . . . . . . . . . . . 20

3.5 Phytoestrogens as aromatase inhibitors . . . . . . . . . . . . . 23

3.6 Commercial aromatase inhibitors . . . . . . . . . . . . . . . . . 28

4 17β-Hydroxysteroid dehydrogenase type 1 31

4.1 Structure and catalytic mechanism of 17β-HSD1 . . . . . . . . 32

4.2 Molecular modelling of 17β-HSD1 . . . . . . . . . . . . . . . . 34

5 Methods used in molecular modelling 39

5.1 Force fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.2 Homology modelling . . . . . . . . . . . . . . . . . . . . . . . . 43

iii

5.3 Molecular dynamics simulations . . . . . . . . . . . . . . . . . 46

5.3.1 Periodic boundary conditions . . . . . . . . . . . . . . . 47

5.3.2 Long-range interactions . . . . . . . . . . . . . . . . . . 47

5.3.3 Temperature and pressure monitoring . . . . . . . . . . 48

5.4 MDS trajectory analysis and protein model validation . . . . . 48

5.5 Ligand–protein docking . . . . . . . . . . . . . . . . . . . . . . 52

Search methods and scoring functions . . . . . . . . . . . . . . 53

5.6 QSAR methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.7 Pharmacophore modelling and virtual screening . . . . . . . . 57

6 Aims of the study 59

7 Results and discussion 61

7.1 The aromatase model and the bindingof phytoestrogens (I and II) . . . . . . . . . . . . . . . . . . . . 61

7.2 Molecular modelling of 17β-HSD1and its inhibitors (III and IV) . . . . . . . . . . . . . . . . . . . . 69

8 Conclusions 79

Bibliography

Structures of the essential amino acids

Original publications

iv

Abstract

Breast cancer is the most common cancer in women in the western coun-

tries. Approximately two-thirds of breast cancer tumours are hormone de-

pendent, requiring estrogens to grow. Estrogens are formed in the human

body via a multistep route starting from cholesterol. The final steps in the

biosynthesis include the CYP450 aromatase enzyme, converting the male

hormones androgens (preferred substrate androstenedione ASD) into estro-

gens (estrone E1), and the 17β-HSD1 enzyme, converting the biologically

less active E1 into the active hormone 17β-hydroxyestradiol E2. E2 is bound

to the nuclear estrogen receptors causing a cascade of biochemical reactions

leading to cell proliferation in normal tissue, and to tumour growth in can-

cer tissue. Aromatase and 17β-HSD1 are expressed in or near the breast tu-

mour, locally providing the tissue with estrogens. One approach in treating

hormone dependent breast tumours is to block the local estrogen produc-

tion by inhibiting these two enzymes. Aromatase inhibitors are already on

the market in treating breast cancer, despite the lack of an experimentally

solved structure. The structure of 17β-HSD1, on the other hand, has been

solved, but no commercial drugs have emerged from the drug discovery

projects reported in the literature.

Computer-assisted molecular modelling is an invaluable tool in modern

drug design projects. Modelling techniques can be used to generate a model

of the target protein and to design novel inhibitors for them even if the tar-

get protein structure is unknown. Molecular modelling has applications in

predicting the activities of theoretical inhibitors and in finding possible ac-

tive inhibitors from a compound database. Inhibitor binding at atomic level

can also be studied with molecular modelling.

To clarify the interactions between the aromatase enzyme and its substrate

and inhibitors, we generated a homology model based on a mammalian

CYP450 enzyme, rabbit progesterone 21-hydroxylase CYP2C5. The model

was carefully validated using molecular dynamics simulations (MDS) with

and without the natural substrate ASD. Binding orientation of the inhibitors

was based on the hypothesis that the inhibitors coordinate to the heme iron,

and were studied using MDS. The inhibitors were dietary phytoestrogens,

which have been shown to reduce the risk for breast cancer. To further val-

idate the model, the interactions of a commercial breast cancer drug were

studied with MDS and ligand–protein docking. In the case of 17β-HSD1, a

3D QSAR model was generated on the basis of MDS of an enzyme complex

with active inhibitor and ligand–protein docking, employing a compound

library synthesised in our laboratory. Furthermore, four pharmacophore

hypotheses with and without a bound substrate or an inhibitor were devel-

oped and used in screening a commercial database of drug-like compounds.

The homology model of aromatase showed stable behaviour in MDS and

was capable of explaining most of the results from mutagenesis studies. We

were able to identify the active site residues contributing to the inhibitor

binding, and explain differences in coordination geometry corresponding

to the inhibitory activity. Interactions between the inhibitors and aromatase

were in agreement with the mutagenesis studies reported for aromatase.

Simulations of 17β-HSD1 with inhibitors revealed an inhibitor binding mode

with hydrogen bond interactions previously not reported, and a hydropho-

bic pocket capable of accommodating a bulky side chain. Pharmacophore

hypothesis generation, followed by virtual screening, was able to identify

several compounds that can be used in lead compound generation. The

visualisation of the interaction fields from the QSAR model and the phar-

macophores provided us with novel ideas for inhibitor development in our

drug discovery project.

vi

Acknowledgements

This study was carried out in the Laboratory of Organic Chemistry, Depart-

ment of Chemistry, University of Helsinki. I am most grateful to my super-

visor, Professor Kristiina Wahala for introducing me to the field of molecu-

lar modelling. She has provided me with excellent tools to work with, from

computer hardware to state-of-the-art software. Moreover, her positive atti-

tude during the whole research project has encouraged me to never give up

in finding solutions to the problems encountered during the work. She has

also provided me with several interesting side projects, including organising

conferences, building the NetLab website and generating the EChemTest

database. I want to express my gratitude also to Professor Emeritus Tapio

Hase for accepting me as a graduate student to the Laboratory and for al-

ways being available to guide me through chemical problems.

My first steps in the field of molecular modelling were taken in the Heinrich

Heine University, Dusseldorf, Germany, under the supervision of Professor

Doctor h.c. Hans-Dieter Holtje. I am ever so grateful to him for accepting me

as a member of his research group and teaching me the basics of molecular

modelling. His excellent advice have helped me during my research work.

Professor Wolfgang Sippl and Professor Jerzy Adamski are thanked for re-

viewing this manuscript and for their constructive comments, and Doctor

Kathleen Ahonen is thanked for revising the language in this manuscript.

The research work and several trips to conferences would not have been

possible without The Finnish Academy, The National Graduate School of

Organic Chemistry and Chemical Biology, The University of Helsinki Re-

search Fund and the University of Helsinki Chancellor’s travel grant.

CSC – IT Center for Science Ltd. is thanked for supercomputer time and

excellent scientific and technical advice during the work.

I would like to thank the personnel at the Laboratory of Organic Chemistry

for always providing me with advice whenever I needed it. Special thanks

go to Dr. Jorma Koskimies for being a casual supervisor at the teaching

laboratory and a colleague in ECTN projects. Dr. Leena Kaisalo has been

an excellent colleague in organising conferences and Anna-Maaret Jarvikivi

has helped me in many administrative issues.

All the former and present members of our Phyto-Syn research group have

always made the working environment very pleasant, both on and off the

working hours. Special thanks go to Nanna and Antti for being such good

friends and giving at least one good laugh per day. Valtteri has helped me in

several computer-related problems, and despite my initial reluctance, talked

me into using Linux and LATEX.

My parents Liisa and Kape are, of course, thanked for their endless sup-

port and encouragement in every decision I have ever made in my life. My

siblings Petri, Venla, Eliisa and Teppana, together with their spouses, my

friends and cousins, and godparents Malla and Lasu, have always let me re-

lax and forget molecular modelling by not asking too many questions about

my work. Lauri Karmila / citronacid.com is thanked for designing the cover

of this book.

Finally and most importantly: Minttu, Aada and Lassi, you are the world to

me.

Helsinki, January 2009

Sampo Karkola

viii

List of original publications

This thesis is based on the following original publications, which are re-

ferred to in the text by their Roman numerals, and on unpublished results

described in the text.

I Sampo Karkola, Hans-Dieter Holtje and Kristiina Wahala, A three-

dimensional model of CYP19 aromatase for structure-based drug de-

sign. The Journal of Steroid Biochemistry and Molecular Biology 105, 63-70,

2007.

II Sampo Karkola and Kristiina Wahala, The binding of lignans, flavo-

noids and coumestrol to CYP450 aromatase: A molecular modelling

study. Molecular and Cellular Endocrinology, in press.

DOI:10.1016/j.mce.2008.10.003

III Sampo Karkola, Annamaria Lilienkampf and Kristiina Wahala, A 3D

QSAR model of 17β-HSD1 inhibitors based on a thieno[2,3-d]pyrim-

idin-4(3H)-one core applying molecular dynamics simulations and li-

gand–protein docking. ChemMedChem 3, 461-472, 2008.

IV Sampo Karkola, Sari Alho-Richmond and Kristiina Wahala, Pharma-

cophore modelling of 17β-HSD1 enzyme based on active inhibitors

and enzyme structure. Molecular and Cellular Endocrinology, in press.

DOI:10.1016/j.mce.2008.08.030

The author’s contribution to the publications is as follows:

I Model building, validation, analyses and manuscript preparation.

II Simulations and analyses of the complexes, dockings and their analy-

sis and manuscript preparation.

III Simulations and analyses, dockings and their analysis, building and

analysis of the 3D QSAR model and most of the manuscript prepara-

tion. This publication was also part of Dr. Annamaria Lilienkampf’s

thesis (University of Helsinki, 2007) concerning the synthesis of 17β-

HSD1 inhibitors.

IV Pharmacophore generations, virtual screening experiments, analyses

and manuscript preparation.

x

List of Abbreviations

17β-HSDcl . . . . . . 17β-hydroxysteroid dehydrogenase

from Cochliobolus lunatus

17β-HSD1 . . . . . . . human 17β-hydroxysteroid dehydrogenase

type 1 enzyme

AI . . . . . . . . . . . . . . . aromatase inhibitor

ANF . . . . . . . . . . . . α-naphthoflavone

ASD . . . . . . . . . . . . . androstenedione

CoMFA . . . . . . . . . . comparative molecular field analysis

CoMSIA . . . . . . . . . comparative molecular similarity indices analysis

CYP2C5 . . . . . . . . . rabbit cytochrome P450 2C5, progesterone 21-hydroxylase

CYP2C9 . . . . . . . . . human cytochrome P450 2C9, a drug

metabolising enzyme

CYP450 . . . . . . . . . cytochrome P450

CYP450arom . . . . . . cytochrome P450 aromatase

CYP450BM−3 . . . . . cytochrome P450 fatty acid monooxygenase

from Bacillus megaterium

CYP450cam . . . . . . . cytochrome P450 camphor monooxygenase

from Pseudomonas putida

CYP450eryF . . . . . . cytochrome P450 6-deoxyerythronolide B hydroxylase

from Saccaropolyspora erthrea

CYP450scc . . . . . . . cytochrome P450 side-chain cleavage enzyme

CYP450terp . . . . . . . cytochrome P450 α-terpineol monooxygenase

from Pseudomonas putida

xi

DFT . . . . . . . . . . . . . density functional theory

DHEA . . . . . . . . . . . dehydroepiandrosterone

DHEA-S . . . . . . . . . dehydroepiandrosterone sulfate

END . . . . . . . . . . . . enterodiol

ENL . . . . . . . . . . . . . enterolactone

E1 . . . . . . . . . . . . . . . estrone

E2 . . . . . . . . . . . . . . . 17β-estradiol

GA . . . . . . . . . . . . . . genetic algorithm

HTS . . . . . . . . . . . . . high-throughput screening

IC50 . . . . . . . . . . . . . inhibition concentration required to reduce

the biological activity by half

Ki . . . . . . . . . . . . . . . equilibrium dissociation constant

KM . . . . . . . . . . . . . . Michaelis-Menten constant

MAT . . . . . . . . . . . . matairesinol

MDS . . . . . . . . . . . . molecular dynamics simulation

NADPH . . . . . . . . . reduced form of nicotinamide adenine dinucleotide

phosphate

NADP+ . . . . . . . . . nicotinamide adenine dinucleotide phosphate

NDGA . . . . . . . . . . nordihydroguaiaretic acid

PDB . . . . . . . . . . . . . The Protein Data Bank

PLS . . . . . . . . . . . . . partial least squares analysis

PME . . . . . . . . . . . . particle-mesh Ewald method

QSAR . . . . . . . . . . . quantitative structure-activity relationship

SDR . . . . . . . . . . . . . short-chain dehydrogenase/reductase enzyme

SERM . . . . . . . . . . . selective estrogen receptor modulator

SI . . . . . . . . . . . . . . . secoisolariciresinol

STS . . . . . . . . . . . . . . steroid sulfatase

ST . . . . . . . . . . . . . . . steroid sulfotransferase

T . . . . . . . . . . . . . . . . testosterone

Vmax . . . . . . . . . . . . . the maximal reaction velocity of an enzyme

xii

Chapter 1

Introduction

Molecular modelling has become an essential tool in several fields of sci-

ence, including chemistry, physics, drug discovery, and biochemistry, to

name a few. The first efforts to model the structure of molecules were the

hand-drawn sketches of atoms joined together with bonds that appeared

in 1860s. Understandably, these models provided little (and then proba-

bly false) information about the actual 3D structure or the physico-chemical

properties of the molecule. In 1874, the Dutch chemist Jacobus Henricus

van’t Hoff and the French chemist Joseph Le Bel independently identified

the tetrahedral carbon as responsible for phenomena observed during their

studies on optical activity and stereochemistry. Since then, modelling has

developed to the Dreiding stick models used in solving the structure of

DNA, and now to computer-assisted studies. Today, modelling is done

solely on computers, and as the computational resources and method de-

velopment continue to expand, increasingly sophisticated models can be

built, simulated and analysed. With multi-core supercomputers and par-

allel computing, highly accurate calculations can be performed on small

molecules and larger biological systems, such as membrane-bound proteins

and lipid membranes containing large biomolecules totalling as many as

million atoms, can be modelled an simulated.

1

Chapter 1. Introduction

Breast cancer is the most common cancer in women in Western countries.

More than 180,000 new cases and over 40,000 deaths are expected to occur

in the United States in 2008 [1]. Approximately 60 % of pre-menopausal

and 75 % of post-menopausal cancers are hormone dependent [2]. In these

cancers, the cell proliferation and, therefore, tumour formation and devel-

opment are dependent on the availability of endogenous estrogens. These

are produced mainly by the ovaries, but also in adipose tissue, breast, skin

and bone [3]. Estrogen receptor-positive breast cancer can be treated by in-

terfering with either estrogen production or estrogen action [4]. To block

estrogen production, it is necessary to inhibit the enzymes responsible for

producing the estrogens: namely, CYP450 aromatase by using aromatase

inhibitors (AI), or 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1).

Inhibitors for 17β-HSD1 have not yet been developed. To prevent estrogen

action, the estrogen receptors in the tumour tissue cells are blocked with

selective estrogen receptor modulators (SERM). While SERMs can be used

in treating the breast cancer of both pre- and post-menopausal women, AIs

are only effective administered to post-menopausal women since they do

not stop the estrogen production in the ovaries of pre-menopausal women

[1] but only the local estrogen production in the cancer tissue. AIs are also

used to induce ovulation in pre-menopausal women [5]. When estrogen

production in the ovaries of pre-menopausal women is reduced, the levels

of gonadotropins become elevated and follicular growth is stimulated.

This thesis describes molecular modelling studies on two estrogen-produc-

ing human enzymes, aromatase and 17β-HSD1. The structure of aromatase

has yet to be solved, and therefore homology modelling (comparative mod-

elling) was used to predict its 3D structure. The model of aromatase was

built using the first crystallised mammalian CYP450 enzyme, rabbit CYP2C5,

as template. The model was validated with exhaustive molecular dynamics

simulations (MDS) with and without the natural substrate, androstenedione

(ASD). With the model in hand, the binding of dietary phytoestrogens and

their metabolites was studied.

2

The crystal structure of 17β-HSD1 was solved in 1995, and it was used in

the modelling work to study the interactions between the 17β-HSD1 en-

zyme and inhibitors synthesised in our laboratory. The dynamic process of

inhibitor binding to 17β-HSD1 was studied by MDS with use of the crys-

tal structure of the enzyme. Plausible binding modes of our 17β-HSD1 in-

hibitors were predicted with ligand–protein docking. A 3D QSAR model of

the inhibitors was created using biological data measured at Hormos Ltd.

and the alignment obtained from the dockings. Additionally, the important

interactions between the enzyme and the inhibitors were identified by phar-

macophore modelling. The resulting pharmacophore models were used as

a query in virtual screening searches aimed at finding new lead compounds

for our drug discovery project.

The topics reviewed in the following chapters 2-5 concern estrogen biosyn-

thesis and the roles of aromatase and 17β-HSD1 in it, previous molecular

modelling studies performed on these two enzymes, the role of phytoe-

strogens as aromatase inhibitors, and methods used in modern computer-

assisted molecular modelling.

3

Chapter 1. Introduction

4

Chapter 2

Estrogen biosynthesis

Estrogens are endogenous female hormones having a tetracyclic structure

with three six-membered rings and one five-membered ring fused together

to form a rigid and planar molecular skeleton. Estrogens differ from male

hormones, androgens, in having an aromatic ring A and lacking the C19

methyl group. Figure 2.1 shows the structures and stereochemistry of an-

drostenedione 1 (ASD) and estradiol 2 (E2).

22

33

4455

1010

11

66

77

88

99 1414

1313

1212

1111

1515

1616

1717

1818 O

O

an androgen(androstenedione ASD 1)

1919

OH

HO

an estrogen(17β-estradiol E2 2)

H

H

H H

H

H

Figure 2.1 – The structures of an androgen and an estrogen withstereochemistry and atom numbering shown. For clarity, hydro-gens connected to carbon atoms are omitted from the 3D models.Oxygen atoms are coloured red.

5

Chapter 2. Estrogen biosynthesis

Estrogens are produced in a multistep route starting from cholesterol 3,

which is first converted to pregnenolone 4 by cytochrome P450 side-chain

cleavage enzyme (CYP11A1, CYP450scc) (Figure 2.2). [6, 7] From pregnen-

olone 4, two major routes are possible. The ∆5 pathway, preferred in hu-

mans, proceeds via conversion to 17α-hydroxypregnenolone 5 by CYP17

(17α-hydroxylase), and to dehydroepiandrosterone 6 (DHEA) by the same

enzyme. DHEA 6 is converted to ASD 1 by 3β-hydroxysteroid dehydro-

genase (3β-HSD) and can be further converted to testosterone 7 (T) by 17β-

hydroxysteroid dehydrogenase (17β-HSD) enzymes. The ∆4 pathway, dom-

inant in rat, hamster and guinea-pig [8], proceeds via the conversion of preg-

nenolone to progesterone, 17α-hydroxyprogesterone and ASD. Either way,

the aliphatic ring A of the androgen (ASD 1 or T 7) is finally aromatised

by the aromatase enzyme complex to produce the estrogen (estrone E1 8 or

estradiol E2 2) (Figure 2.3). E1 8, the less potent estrogen, is converted to

the biologically active estrogen E2 2 by 17β-HSD1. After menopause, the

main sites for estrogen production are the peripheral tissues such as adi-

pose tissue, breast tissue and skin. [9] Here, estrogens, not to be delivered to

the circulation, but to be used in situ, are produced from adrenal precursors

DHEA 6 and its 3-sulfate (DHEA-S).

HO

cholesterol 3

HO

O

pregnenolone 4

CYP11A1 CYP17

HO

O

dehydroepiandrosterone DHEA 6

HO

O

17α-hydroxypregnenolone 5

OH

CYP17

O

O

androstenedione ASD 1

3β-HSD

Figure 2.2 – The formation of androstenedione from cholesterol.

6

Aromatase and 17β-HSD1 enzymes are the key players in the final steps of

estrogen biosynthesis leading to the inactive precursor E1 8 and the active

estrogen E2 2, respectively. The storage forms of estrogens (estrone-3-sulfate

9 and estradiol-3-sulfate 10) are formed by linkage of a sulfate group to the

phenolic 3-hydroxy group by steroid sulfotransferase (ST) (Figure 2.3). [10]

Most of the circulating estrogens are, in fact, sulfoconjugates, cleaved by

the steroid sulfatase enzyme (STS) when needed.[11] Estrogens bind to the

nuclear estrogen receptors (α or β) and cause a cascade of reactions lead-

ing to cell proliferation and, in cancer tissue, to tumour development [10].

Estrogens also exert extra-nuclear effects via kinases, ion channels, G pro-

teins and growth factor receptors, in addition to the genomic effects caused

by nuclear receptor binding.[12] Interfering with either estrogen formation

or its cellular effects has been a target for controlling hormone-dependent

tumour formation and development. The interference of estrogen forma-

tion involves blocking the actions of the endogenous enzymes producing

estrogens. Estrogen-producing enzymes include aromatase, which converts

O

HO

estrone E1 8

O

O

androstenedione ASD 1

aromatase

OH

O

testosterone T 7

OH

HO

estradiol E2 2

17β-HSD217β-HSD417β-HSD817β-HSD10

17β-HSD117β-HSD517β-HSD7

17β-HSD217β-HSD8

17β-HSD317β-HSD5

aromatase

O

O

OH

O

estradiol-3-sulfate 10

S

O

OHO

S

O

OHO

estrone-3-sulfate 9

STS

ST

STS

ST

Figure 2.3 – The final steps and the enzymes involved in estrogenbiosynthesis.

androgens to estrogens; 17β-HSD1, which converts estrone E1 8 (the prod-

uct of aromatase) to the biologically active estradiol E2 2, and STS, which

7

Chapter 2. Estrogen biosynthesis

releases estrogens from their sulfate storage forms. Interfering with the cel-

lular effects of estrogen, when it binds to cell nuclear receptors, involves

blocking of the estrogen receptor functions. This can be achieved with se-

lective estrogen receptor modulators (SERMs), which bind to the estrogen

receptors in place of the actual hormone. Commercial drugs representing

both aromatase inhibitors and SERMs are now widely used in the treatment

of hormone-dependent cancers.

8

Chapter 3

CYP450 aromatase

CYP450 enzymes catalyse a wide range of reactions from xenobiotic me-

tabolism to drug metabolism and steroid biosynthesis [13, 14]. Despite the

marked variation in sequence identities, the CYP450 enzymes share a com-

mon tertiary fold consisting of 12 – 15 α-helices (A through L) and four

β-sheets (1 through 4) (Figure 3.1) [15]. The eucaryotic CYP450 enzymes are

bound to the endoplasmic reticulum via a transmembrane helix located at

the N-terminus of the sequence. Because of this binding, solubilisation of

the enzymes requires the use of detergents, which easily destroy the del-

icate protein fold. A catalytically crucial protoporphyrin IX group with a

bound iron atom (the heme group, Figure 3.2) is located in the active site

of the enzymes. The iron atom is responsible for the oxidations performed

to the substrates. The iron in the heme ring has an octahedral coordina-

tion geometry with six coordination sites, with four of them occupied by

the nitrogens of the planar protoporphyrin ring. In contrast to other cy-

tochromes having a nitrogenous base coordinating to the heme iron as a

fifth ligand, the CYP450 enzymes have a thiolate anion, the side chain of

a cysteine, as fifth ligand (C437 in aromatase). The thiolate anion is essen-

tial for the activity, as is shown by mutating the coordinating cysteine to

a histidine for CYP450 camphor monooxygenase (CYP450cam): the mutant

9

Chapter 3. CYP450 aromatase

enzyme catalyzes the hydroxylation of camphor but at a much lower rate

than the wild-type enzyme [16]. The sixth coordination site is part of the

functionality of the active site and is occupied by a water molecule in the

steady-state form of the enzyme [17]. Before the catalytic reaction, the iron

binds a diatomic oxygen molecule, which provides the oxygen with which

the substrate is hydroxylated. The sixth coordination site is also the tar-

get for inhibitor design, providing the site for the inhibitor’s coordinative

counterpart (usually a basic nitrogen). The catalytic reaction it performs has

caused the CYP450 family to be referred to as the biological equivalent of a

blow-torch [18]. In the laboratory, the hydroxylation of an aliphatic carbon

requires drastic reaction conditions.

Figure 3.1 – The overall structure of a CYP450 enzyme (CYP2C5,PDB entry 1N6B). α-Helices are coloured red, β-sheets yellow andthe random coil and loop areas green. Heme is represented assticks and heme iron as a magenta sphere. The transmembranehelix is omitted from the Protein Data Bank structure file.

Aromatase (estrogen synthetase, P-450AROM, CYPXIX, EC 1.14.14.1) is a

single member of the CYP19 family, coded by the CYP19A1 gene and con-

sisting of 503 amino acid residues with total molecular weight of 57.9 kDa.

10

The catalytic complex consists of the aromatase enzyme and a nicotinamide

adenine dinucleotide phosphate (NADPH) cytochrome P450 reductase, a

ubiquitous flavoprotein, which provides the reaction with the required mo-

lar amount of NADPH (Figure 3.2) [19]. The whole complex is bound to

the endoplasmic reticulum membrane in the cell. Aromatase is mostly ex-

pressed in the ovaries of pre-menopausal women, in the placenta of preg-

nant women and additionally in the peripheral adipose tissue, breast tis-

sue and brain [20]. It is overexpressed in or near breast cancer tissue and

is responsible for the local estrogen production and proliferation of breast

cancer tissue [21]. The 3D structure of aromatase has yet to be solved due

to the difficulties in crystallisation associated with the membrane-bound

character of the enzyme. The active enzyme without the N-terminal trans-

membrane helix has been produced by recombinant expression techniques

[22, 23, 24, 25, 26]. Unless otherwise indicated, residue numbering in the

following text is from aromatase .

N

N N

N

HOOC COOH

Fe

N

N

NH2

N

N

OP

OO-

O-

O PO

O-O P

O

O-O N

O

NH2

H H

OH

O O

OHOH

heme group NADPH

Figure 3.2 – Left: The structure of the prosthetic group proto-porphyrin IX with a bound iron atom (the heme group) in aro-matase. Right: The structure of the cofactor NADPH, the reducedform of nicotinamide adenine dinucleotide phosphate, in CYP450-NADPH reductase and in 17β-HSD1.

11

Chapter 3. CYP450 aromatase

3.1 Substrate recognition sites

The active site of the CYP450 enzymes consists of the heme ring at one

wall and six common substrate recognition sites (SRS), as defined by Go-

toh [27] for CYP2 family enzymes (Figure 3.3). SRS-1 is located between the

B- and C-helices, where an additional B’-helix is observed in some CYP450

enzymes. SRS-2 and SRS-3, or the C-terminus of the F-helix and the N-

terminus of the G-helix, are located on the opposite wall of the active site

from the heme ring. This area varies among CYP450 family members show-

ing helices and loops of different lengths and with none, one or two addi-

tional helices (F’ and G’).[28] SRS-4 is located in the I-helix, which protrudes

through the whole enzyme and includes residues that are highly conserved

among the CYP450 enzymes. An interesting residue at SRS-4 is P308, which,

due to the inability to form a stabilising backbone hydrogen bond to the ad-

jacent L304, causes a kink in the I-helix. The active-site wall opposite the

I-helix consists mainly of SRS-5, the hydrophobic coil between the K-helix

and the β1-4-strand. SRS-6 is located in the β-sheet 4 or, more accurately, in

the hairpin turn between the two strands β4-1 and β4-2. This site includes

amino acids thought to be important not only to substrate recognition but

also to the catalytic activity (see Section 3.4 on page 20).

Figure 3.3 – A stereo image of the structural elements correspond-ing to the substrate recognition sites (SRS) of CYP2C5. SRS-1 iscoloured red, SRS-2 green, SRS-3 blue, SRS-4 yellow, SRS-5 cyanand SRS-6 orange. Heme is represented as sticks and the hemeiron as a magenta sphere.

12

3.2. Mechanism of the catalytic reaction

3.2 Mechanism of the catalytic reaction

Human aromatase catalyses a reaction in which the aliphatic ring A of an an-

drogen (ASD 1 or T 7) is converted to the aromatic ring A of an estrogen (E1 8

or E2 2). ASD is the preferred substrate (Figure 3.4) [29]. The overall reaction

requires three moles of oxygen and three moles of NADPH per one mole of

the substrate. The conversion from an aliphatic C19 androgen to an aromatic

C18 estrogen occurs in a sequence of three hydroxylation steps. Before the

O

O

androstenedione ASD 1

O

O

HO

O

O

HOOH

19-hydroxyandrostenedione 11

19-gem-dihydroxyandrostenedione 12

O2, NADPH

aromatase

O

HO

estrone E1 8

+ HCOOH + H2O

C19

O2, NADPH

aromatase

O2, NADPH

aromatase

Figure 3.4 – The overall aromatisation reaction of androgens. TheC19 is hydroxylated in three consecutive steps producing the es-trogen, formic acid and water.

catalytic reaction, the hydroxylating agent iron(IV)-oxo porphyrin radical

cation is formed by the addition of an electron pair, an oxygen molecule

and two protons to the heme iron (Figure 3.5) [17]. The catalysis starts with

the hydroxylation of C19 of ASD to give C19-hydroxyandrostenedione 11

by hydrogen abstraction–oxygen rebound mechanism [17, 30], followed by

another hydroxylation to the same carbon producing C19-gem-dihydroxy-

androstenedione 12. Although the mechanism of the third step has not

yet been solved, density functional theory (DFT) calculations and ab initio

dynamics simulations [31] suggest that first the 1β-hydrogen is abstracted

by the oxygen atom bound to the iron, facilitated by the enolisation of the

3-carbonyl. Subsequently, an electron is transferred from the alkyl radical

13

Chapter 3. CYP450 aromatase

FeIIIIII

FeIIII

e- O2Fe

IIIIII

OO

e-Fe

IIIIII

OO

H+

FeIIIIII

OOH

H+,-H2OFe

VV

O

Fe N

N N

NFe

COOHHOOC

=

FeIVIV

O

+

iron(V)-oxo species

Figure 3.5 – The formation of the iron(V)-oxo species, redrawnfrom reference [17].

FeIVIV

O

+

H

OH

HOOH

FeIVIV

O

OH

HOOH H

e- transfer

FeIIIIII

O

OH

HOO

H

H

FeIIIIII

OHH

-HCOOH

1β-H abstraction

OH

Figure 3.6 – The third step of the aromatase catalysed reaction pro-ducing the estrogen, formic acid and water, redrawn from refer-ence [31].

14

3.3. Homology modelling of aromatase

to the iron, and the gem-diol 12 is deformylated to the estrogen, formic acid

and water (Figure 3.6).

3.3 Homology modelling of aromatase

The lack of an experimental structure for aromatase requires other meth-

ods to be used in studying the overall protein fold and the structure of the

active site. These methods include in silico structure prediction tools such

as homology modelling and threading, as well as mutagenesis studies. Aro-

matase is a distant relative of other CYP450 enzymes, and the amino acid se-

quence identity to other CYP450 enzymes is approximately 20 % [32]. Build-

ing a homology model on the basis of a template with such a low sequence

identity is usually considered to be difficult and the results to be unreliable.

Despite the low sequence identity, however, all CYP450 enzymes possess a

common tertiary fold [28] (see Figure 3.1 on page 10), which makes it possi-

ble to derive the structure of an unknown CYP450 protein from on a known

one. Certain residues and the secondary structures are conserved in vary-

ing degrees throughout the CYP450 enzymes, although α-helices, β-strands,

turns and loops vary in length and amino acid constitution.

Differing from mammalian CYP450 enzymes, bacterial CYP450 enzymes

are not bound to the endoplasmic reticulum but are soluble. This prop-

erty makes it easier to crystallise them and to determine the 3D structure by

X-ray diffraction methods. Once the first bacterial CYP450 enzyme struc-

ture, cytochrome P450 camphor monooxygenase from Pseudomonas putida

(CYP450cam), was solved, in 1985 [33], the homology modelling of CYP450

enzymes could commence. The first aromatase models were based on this

and other bacterial crystal structures. Graham-Lorence et al. [34] created

an active site model by aligning the sequences of aromatase and CYP450cam.

The residues in the heme binding region and the I-helix of CYP450cam were

replaced by residues from aromatase. The other parts of the protein were

15

Chapter 3. CYP450 aromatase

deleted and energy minimisations were performed. Examination of the

model and the orientation of camphor in the CYP450cam crystal structure

suggested the orientation of the substrate androstenedione (ASD). The ac-

tive site model was also used to study the effects of mutagenesis experi-

ments performed on aromatase (see Section 3.4 on page 20).

Zhou et al. [35] developed an active site model for aromatase using a similar

approach, that is, replacing the residues of CYP450cam in the I-helix with

those of aromatase. The resulting I-helix was then energy minimised and

the substrate ASD was placed in the active site. A similar positioning of

ASD as in the work of Graham-Lorence [34] was suggested.

Laughton et al. [36] in 1993, were the first to use molecular dynamics sim-

ulations (MDS) in relaxing and validating the aromatase model. The se-

quences of aromatase and CYP450cam were aligned using an automated mul-

tiple alignment procedure [37], and the secondary structure elements were

predicted by the algorithm developed by Zvelebil et al [38]. The backbone

coordinates of the core parts of aromatase were assigned from the template,

and the loop backbone coordinates were generated with the loop-fitting pro-

cedure in QUANTA software [39]. The coordinates for the side chains were

assigned from the template, where applicable, and the rest of the side chains

were assigned arbitrary conformations. The system was energy minimised

starting from the least-well-defined parts and gradually moving to the bet-

ter predicted parts. Simulated annealing (a series of heating and cooling

of the system) was applied to the loops and undetermined core structures.

ASD was inserted into the active site cavity, and a 10 ps MDS at 300 K was

performed. As a result, a model for the active site and the orientation of the

substrate were suggested.

Later, in 1993 and 1994, two more bacterial CYP450 enzyme crystal struc-

tures were solved (cytochrome P450 bacterial fatty acid monooxygenase

CYP450BM−3 from Bacillus megaterium [40] and cytochrome P450 α-terpineol

monooxygenase CYP450terp from Pseudomonas putida [41]). CYP450BM−3 was

16

3.3. Homology modelling of aromatase

the first class II CYP450 enzyme, that is, a CYP450 enzyme interacting with

an NADPH-P450 reductase, whose structure was solved. These new struc-

tures provided more information on the sequence alignments between aro-

matase and the templates, and therefore, on the 3D structure of aromatase.

On the basis of the new sequence alignment, a new core structure was sug-

gested for aromatase [42]. A de novo structure construction was combined

with replacement of CYP450BM−3 residues by ones from aromatase. Min-

imisations and MDS for a few picoseconds in vacuum were applied to relax

the model. The orientation of the substrate ASD was also suggested.

Koymans et al. [43] developed a model of aromatase using CYP450cam as

the template. After sequence alignment, ASD and several non-steroidal

aromatase inhibitors were manually placed inside the active site and the

complexes were energy minimised. Analyses of the minimised structures

indicated that the C-terminus of the aromatase sequence (471-486) is an im-

portant part of the active site.

A CYP450BM−3 based model was generated by Kao et al. [44] using an

alignment from Nelson [45]. The alignment was manually refined and main

chain coordinates of the core region were taken from the template. The loop

areas were searched from a database and the side-chain conformations were

predicted on the basis of a local homology relationship [46] followed by min-

imisation. The generated model was compared with the CYP450cam -based

model of Laughton et al. [36]. Because of differences in the lengths of helices

F and G, and thus, difficulties in positioning the substrate ASD, the authors

preferred the model based on CYP450cam.

Lewis and Lee-Robichaud [47] generated homology models for four steroido-

genic CYP450 enzymes (cholesterol side-chain cleavage enzyme, 17-α-hy-

droxylase, aromatase and 21-hydroxylase) based on the crystal structure of

CYP450BM−3. After a computer-assisted sequence alignment, a manual re-

finement was performed and the models were generated by the Biopolymer

module implemented in the Sybyl software [48]. The crude models were en-

17

Chapter 3. CYP450 aromatase

ergy minimised, and substrates and inhibitors were docked into the models.

The results were in broad agreement with mutagenesis data at hand.

Cavalli et al. [49] generated an aromatase model based on an alignment

taken from Graham-Lorence et al [42] that was refined by aromatase sec-

ondary structure predictions from the PsiPred server [50, 51]. The authors

used the structure of CYP450cam with a bound phenylethylimidazole deriva-

tive because they were generating a model for the interaction between a

specific class of inhibitors and aromatase. SCR regions of aromatase were

generated from the template, while the SVR regions were built de novo. Be-

fore the optimisation of the generated structures, two inhibitors were placed

in the active site, using their crystal coordinates and interacting them with

the heme iron. After energy minimisations, the complexes were subjected to

molecular dynamics simulations for 100 ps at physiological temperature to

relax and optimise the models. Average structures from the stable simula-

tion trajectory were used to analyse the interactions between the inhibitors

and aromatase. The interactions were found to be similar to those presented

earlier by Koymans [43]. Additionally, a 3D QSAR model was generated by

the comparative molecular field analysis method (CoMFA).

Auvray et al. [52] also used the alignment from Graham-Lorence et al [42].

The core region coordinates were taken from the template (CYP450BM−3)

and the loops were searched from a database. The crude models were en-

ergy minimised, after which the substrate ASD was placed inside the active

site and the complex was minimised. The effect of several mutations of aro-

matase was studied with the help of the model.

The first mammalian CYP450 enzyme structure was solved in 2000 [13]. This

was CYP2C5, rabbit 21-progesterone hydroxylase, which catalyses the hy-

droxylation of C21 of progesterone. Because both aromatase and CYP2C5

are membrane-bound mammalian CYP450 enzymes with a steroid as a sub-

strate, it was clear that the newly solved structure provided a better tem-

plate for homology modelling of human aromatase than the soluble bac-

18

3.3. Homology modelling of aromatase

terial CYP450 enzymes. The first homology model based on this template

was reported by Chen et al. [53], though neither the sequence alignment be-

tween CYP2C5 and aromatase nor the details of the modelling process were

published. Loge et al. [32] also built a model based on CYP2C5, but used

a crystal structure with a bound inhibitor (PDB entry 1N6B [54]). The se-

quence alignment was generated with ClustalW [55] and refined using the

PsiPred server [50, 51]. The actual aromatase model was generated with the

Nest program implemented in Jackal software [56, 57]. The crude model

was energy minimised gradually from the core side chains to the core main

chains, after which two inhibitors were docked into the active site of the

enzyme using GOLD [58]. The results from the docking studies were in

agreement with previously reported results [59, 60, 61, 53, 62].

The crystal structure of the first human cytochrome P450 enzyme, CYP2C9,

was solved in 2003 [63]. CYP2C9, a drug-metabolising CYP450 enzyme, was

subsequently used by Favia et al [64] as a template for aromatase homology

modelling. The sequence alignment was done with the LALIGN server [65],

and secondary structure elements were predicted with the PsiPred server

[50, 51]. Several models were generated using the MODELER software [66].

The top ranking model was validated with MDS (10 ns simulation time) us-

ing the AMBER8 software [67]. In addition to the aromatase model, the au-

thors used DFT calculations to develop parameters for the protoporphyrin

IX ring with bound iron and cysteine coordination.

A model based on CYP2C5 was developed by Murthy et al. [68] to study

the acidic residues located in the active site of aromatase. However, the se-

quence alignment was not published. The model was built with the MOD-

ELER software [66] and energy minimised for a starting structure for MDS.

The simulation was performed in water for 1 ns using AMBER6. The acid-

ity of certain active site residues was predicted with and without natural

substrates.

The model described in detail in publication I was built on CYP2C5, a mam-

19

Chapter 3. CYP450 aromatase

malian CYP450 enzyme that has a steroid as a substrate. The crystallised

human CYP2C9 was also available, but as it is not a steroid-metabolising

CYP450 enzyme, its use as a template was rejected.

3.4 Mutagenesis studies on aromatase

The aromatase enzyme has been a target for nearly 80 mutagenesis studies.

Most of these were performed to identify the residues contributing to the

active site and the catalytic mechanism, while some involved mutations to

residues outside the active site to clarify the effect on the overall tertiary fold

(and the function) of the enzyme. In view of the large number of studies per-

formed, only the results for the most important residues are discussed in the

following. Most mutagenesis investigations have been performed in whole-

cell assays, in which the kinetic constants KM (Michaelis-Menten constant;

the substrate concentration at which the enzymatic reaction velocity is half

of its maximal value) and Vmax (the maximal reaction velocity) may be af-

fected by intracellular actions. Although microsomal preparations in vitro

would, thus, be more reliable in determining kinetic constants, in vitro as-

says have the disadvantage of not representing living cells. Unless other-

wise indicated, the substrate in the assays discussed below is androstene-

dione (ASD). All the mutagenesis studies performed to date are listed in

Table 3.1.

Most of the mutated residues are located at SRS-1 and SRS-4. SRS-1 is the

B–C loop (Figure 3.3 on page 12) forming one wall of the active site and

including a B’-helix in some of the CYP450 enzymes.[28] There are two hy-

drophobic residues that are thought to interact directly with the substrate,

namely I133 and F134. The mutation of I133 to bulkier residues (I133W

and I133Y, [44]) indeed leads to a dramatically reduced Vmax and an in-

creased KM in microsomal assay, indicating that the volume of the active

site is diminished and, therefore, less favoured for the substrate. Amarneh

20

3.4. Mutagenesis studies on aromatase

et al. [69] found that mutant F134E, where a bulky hydrophobic residue is

changed to a negatively charged one, has similar KMbut higher Vmax value

than the wild-type enzyme. This indicates that the F134E mutation leads

to a more active enzyme, although the affinity for the substrate remains ap-

proximately the same. It was suggested that a negatively charged residue at

this position takes part in the catalytic mechanism. The group also designed

a double mutant F134E|E302A to find out if a negatively charged residue at

134 could replace the functionality of a negatively charged residue at 302.

However, the results of this study have not yet been published.

Table 3.1 – Mutagenesis studies reported to date for the aromataseenzyme.

Mutationa SRSb Ref. Mutationa SRSb Ref. Mutationa SRSb Ref.-10Nc – [69] L228E 2 [69] R365A – [70]-20Nd – [69, 70] K230A 3 [62] R365K – [70]T14A – [69] K230R 3 [62] V388E – [69]F116E – [69] F235L 3 [44] I395F – [44, 71]K119E 1 [52] F235N 3 [72] F406R – [73, 74]K119T 1 [52] C299A 4 [35] H457N – [52]K119V 1 [52] E302A 4 [52] S470N – [52]K119Y 1 [52] E302D 4 [34, 62, 75] I471M – [52]G121A 1 [72] E302Q 4 [34] I474F – [72]Q123E 1 [70] E302V 4 [34] I474M – [44]Q123H 1 [70] E302A 4 [34, 52] I474N – [44]C124Y 1 [52] E302L 4 [35] I474T – [52]I125M 1 [52] GAGVe 4 [34] I474W – [44]I125N 1 [72] A307G 4 [34] I474Y – [44, 71]H128A 1 [35] P308F 4 [73, 76, 74, 71] H475A – [52]H128Q 1 [35] P308V 4 [34] H475E – [52]K130N 1 [52] D309A 4 [35, 76, 74, 71, 52, 75] H475R – [52]I133M 1 [77] D309N 4 [35, 76, 74, 69] D476A – [52]I133W 1 [44] D309V 4 [69] D476E – [52]I133Y 1 [44, 71] T310A 4 [69] D476K – [52]F134E 1 [69] T310C 4 [70] D476L – [52]K216A 2 [62] T310S 4 [70, 69, 44] D476N – [52]K216R 2 [62] S312C 4 [35] S478A 6 [62]Q225A 2 [69] F320C – [52] S478T 6 [62, 75]Q225N 2 [69] Y361F – [73] H480K 6 [62]L228A 2 [69] Y361L – [73] H480Q 6 [62, 75]

aFor a list of amino acids and their abbreviations, see page 97bDefined for CYP2 family enzymes [27]cDeletion of the first 10 N-terminal residuesdDeletion of the first 20 N-terminal residueseI305G, A306A, A307G, P308V

21

Chapter 3. CYP450 aromatase

The residues located at SRS-4 (Figure 3.3 on page 12), that is, the centre of the

I-helix, form an important part of the active site and most likely participate

in the catalytic mechanism. Not surprisingly they have been the subject of

several mutagenesis studies. The mutation of the negatively charged E302 to

hydrophobic residues Ala, Leu or Val or to a polar residue Gln led to unsta-

ble or inactive enzymes.[34, 35, 52] Moreover, E302D exhibited dramatically

decreased activity compared with that of the wild-type enzyme, suggesting

that even an Asp cannot properly replace the functionality of a Glu at this

position.[34, 62] P308 is an interesting residue located at the centre of the

I-helix. Because it cannot form a a hydrogen bond from the backbone nitro-

gen to the adjacent carbonyl oxygen of L304, it creates a bend in the helical

structure. The P308F mutant decreased the Vmax for ASD and T suggest-

ing an altered active site.[73] KM was decreased significantly for ASD and

slightly for T, indicating that ASD binds more strongly to the mutant than

to the wild-type enzyme. In another study [76], however, the same muta-

tion P308F reportedly increased the KM value for ASD 4-fold rather than

reducing it. The mutant P308V, in turn, had a Vmax value for ASD half that

of the wild-type enzyme, and the KM was increased 4-fold.[34] If a valine

at this position does not cause a bend in the I-helix, as is suggested by the

secondary structure prediction programs, then a bend in the I-helix is nec-

essary for the optimal function of the enzyme. The necessity of the bend is

hard to prove, however, owing to the diverse results from different mutage-

nesis studies.

D309 has been the subject of several mutagenesis studies, aimed at revealing

the importance of a negatively charged residue at this position. The muta-

tion of D309 to a hydrophobic residue Ala or Val led to almost total inactivity

of the enzyme, as shown by the decrease in Vmax values.[35, 76, 74, 44, 52, 69]

However, widely ranging KM values for mutants D309A and D309V have

been obtained by different research groups. Vmax for the D309N mutant was

not as dramatically decreased as that for the D309A or D309V mutant. Also

the KM for D309N was lower than for the wild-type enzyme, suggesting that

22

3.5. Phytoestrogens as aromatase inhibitors

a polar residue can serve the function of a negatively charged residue at this

position, at least to some extent.[70] It should be mentioned that although

mutant D309E has not been constructed, a Glu is located at this position in

CYP450BM−3 and in human and bovine CYP45017α, showing that a negative

charge is important at this position. T310 is conserved in all CYP450 en-

zymes except CYP450eryF[78], being located in SRS-4 and immediately next

to the heme group. T310 is thought to take part in the catalytic mechanism

and/or to provide a hydrogen bond to the backbone carbonyl of A306, thus

stabilising the bend formed by P308.[70, 71] Mutants T310A and T310C re-

sulted in the decrease or total loss of the activity.[69, 70]. A conservative

mutant T310S showed decreased activity compared with that of the wild-

type enzyme, but it is still functional.[70, 69, 44]

Residues at SRS-6 are part of a hairpin turn of β-sheet 4 and in aromatase

include residues S478 and H480. The Vmax values for the mutants S478A,

S478T and H480Q were significantly reduced as compared with tha of the

wild-type enzyme in both in-cell and microsomal assays.[62, 75] However,

in microsomal assay, H480K had closely similar activity to that of the wild-

type enzyme, suggesting that a positively charged residue is needed at this

position.[62]

As described above, much research has been done to identify the residues

contributing to the aromatase active site in an important way. Some of the

results are conflicting, probably because the groups used different test con-

ditions. A great deal of valuable information about the active site of aro-

matasehas nevertheless been aqcuired. In time, an experimentally solved

aromatase structure will prove the theories right or wrong.

3.5 Phytoestrogens as aromatase inhibitors

Phytoestrogens are polyphenolic compounds widely present in nature. Many

plants included in the human diet, soy, flaxseed, berries, grapes etc., are

23

Chapter 3. CYP450 aromatase

good sources of phytoestrogens.[79, 80, 81, 82, 83] Their dietary intake has

been associated with reduced breast cancer risk.(reference [84] and refer-

ences therein). The two major classes of phytoestrogens are flavonoids (in-

cluding subclasses flavones, flavanones and isoflavonoids) and lignans (lig-

nanolactones and lignanodiols) (Figure 3.7). Phytoestrogens mimic endoge-

nous estrogens in shape, size and physico-chemical properties making it

possible for them to bind to the same receptors and enzymes as do the estro-

gens. The similarity between estradiol and 6,4’-dihydroxyflavone is shown

in Figure 3.8. An extensive of aromatase inhibition by phytoestrogenic com-

pounds and their use in drug discovery has been published by our research

group.[85]

OH

OH

lignanolactones lignanodiols

77

6655

88

4433

22O

O

4'4'3'3'

A

B

C

O

O

flavones isoflavones

8'8'

88

7'7'

77

1'1'

11

2'2'3'3'

4'4'

4433

22

99

O

9'9'

O

A

B

Figure 3.7 – The general structures of lignanolactones, lignanodi-ols, flavones and isoflavones with stereochemistry and atom num-bering shown.

24

3.5. Phytoestrogens as aromatase inhibitors

Figure 3.8 – The similarity of shapes and the oxygen atom loca-tions in E2 (left) and in 6,4’-dihydroxyflavone (right). The oxygenatoms are coloured red and the carbon skeleton is coloured grey.Hydrogens are omitted for clarity.

Figure 3.9 – Left: The optimal coordination angle and average dis-tance (see publication II) in a carbonyl coordination to the hemeiron. Right: The optimal tilting angle between the heme plane andthe carbonyl group plane. Carbons are coloured grey, oxygen iscoloured red and the heme iron is coloured magenta. Heme sidechains are omitted for clarity.

25

Chapter 3. CYP450 aromatase

Natural phytoestrogens have been widely studied for their aromatase in-

hibiting properties. As a rule, flavones are better inhibitors than isoflavones

and lignans. It has been suggested, on the basis of spectroscopic stud-

ies, that the flavonoids coordinate to the heme iron via the C4 carbonyl

oxygen.[86, 87, 71, 88] A theoretical optimal binding geometry is achieved

when the Fe–O=C angle is 120◦ and the tilting angle between the heme plane

and the carbonyl plane is 90◦. Additionally, a database search in the Pro-

tein Data Bank (PDB) yielded an experimental average distance of 0.19 nm

between the iron and the oxygen (see publication II). The optimal binding

geometry is shown in Figure 3.9. The dihedral angle N-Fe-O-C also plays a

role in the optimal binding geometry, in that the functional groups attached

to the carbonyl carbon should not lead to sterical clashes with the protopor-

phyrin ring.[89] The best natural flavonoid inhibitor is α-naphthoflavone

13 (ANF) (Figure 3.10), which has an IC50 value of 0.07 µM in human pla-

cental microsome-based assay.[86] Substitutions in the flavonoid skeleton

affect the binding strength in various ways. 7-Hydroxyflavone is a more

potent aromatase inhibitor than flavone, as can be seen from reported IC50

values 0.5 µM and 10 µM [90], and 0.21 µM and 48 µM [91], respectively.

Hydroxylation at C7 can overcome the unfavourable influence of other sub-

stitutions, as seen from the IC50 values of 4’-hydroxyflavone (180 µM) and

7,4’-dihydroxyflavone (2 µM) [90]. A hydroxy group at C5 or C3 forms a

hydrogen bond to the C4 carbonyl, weakening its ability to coordinate to

the heme iron. This can be seen by comparing the IC50 values of flavone

(10 µM), 3-hydroxyflavone (140 µM) and 5-hydroxyflavone (100 µM).[90]

Isoflavonoids have the phenyl ring attached to C3, instead of to C2 as in

flavones, and as a result the C4 carbonyl coordinating to the heme iron is

sterically hindered (Figure 3.7 on page 24 and publication II). The best isofla-

vonoid inhibitor is biochanin A with an IC50 value of 15.5 µM in a cell-based

assay.[71] For a complete list of the effects of the substitution on biological

activity, see reference [85] and publication II.

Lignans have been shown to be competitive, though weak inhibitors of

26

3.5. Phytoestrogens as aromatase inhibitors

secoisolariciresinol SI 17

OH

OHHO

OHO

O

matairesinol MAT 16

O

OHO

OHO

O

nordihydroguaiaretic acid NDGA 18

HO

OHO

O

enterodiol END 15

OH

OH

enterolactone ENL 14

O

O

HO

HO HO

HO

O

O

α-naphthoflavone ANF 13

O

O

OH

OHO

coumestrol 19

Figure 3.10 – The structures of the enterolignans ENL and ENDand their dietary precursors MAT 14 and SI, and the structures ofNDGA , ANF and coumestrol.

the aromatase enzyme in microsome and cell-based assays.[92, 93] The en-

terolignans enterolactone 15 (ENL) and enterodiol 16 (END) are produced

by the human gut microflora from their dietary precursors matairesinol 14

(MAT) and secoisolariciresinol 17 (SI), respectively (Figure 3.10) [94, 95, 96,

97], and these enterolignans and their precursors are responsible for the

aromatase inhibition in the human body. Although the binding modes of

these compounds have not yet been proposed, their inhibition has been

measured.[92, 93] The lignanolactones bear a sterically unhindered carbonyl

oxygen capable of coordinating to the heme iron. However, their relatively

large size and flexible structure compared to that of the flavonoids proba-

27

Chapter 3. CYP450 aromatase

bly distorts the binding geometry and results in weaker binding (see publi-

cation II). The lignanodiols, which lack the carbonyl group, probably bind

to the iron via a hydroxy group. Note that nordihydroguaiaretic acid 18

(NDGA, Figure 3.10), which lacks hydroxy groups at the same positions

where ENL and END have potentially coordinating oxygen atoms, is a rela-

tively strong aromatase inhibitor among the lignans.[92]

Coumestrol 19 is a tetracyclic polyphenol belonging to the class of coumes-

tan compounds (Figure 3.10). Coumestrol was first identified in alfalfa and

has later been found in plants, such as soya and cow pea, included in the

human diet.[98, 99] Coumestrol has been shown to inhibit aromatase activ-

ity with an IC50 value of 17 µM and a Ki value of 1.3 µM , being thus a weak

inhibitor.[93] Mutagenesis studies on aromatase made with coumestrol have

shown that mutants S478T and H480Q have reduced binding affinity for

coumestrol [75] (see Section 7.1 on page 61).

3.6 Commercial aromatase inhibitors

One approach to treating hormone-dependent breast cancer is to block the

biosynthesis of estrogens through the inhibition of aromatase or 17β-HSD1.

Inhibitors of the 17β-HSD1 enzyme have not yet reached the market, while

even though the 3D structure of aromatase has not yet been experimentally

solved, a few drug discovery projects have succesfully led to commercial

aromatase inhibitors (AIs) (Figure 3.11). Already, three AIs are on the mar-

ket to treat post-menopausal breast cancer by inhibiting the local estrogen

production in the tumour tissue.[100, 101] Exemestane 20 (Aromasin R© from

Pfizer) is a type I inhibitor, a steroid derivative functioning as a suicide in-

hibitor. It is recognised by the aromatase active site and the catalytic reaction

produces an intermediate that covalently binds to the enzyme, permanently

inactivating it. Letrozole 21 (Femara R© from Novartis) and anastrozole 22

(Arimidex R© from AstraZeneca) are type II inhibitors that are non-steroidal

28

3.6. Commercial aromatase inhibitors

and bind reversibly to the aromatase enzyme. The type II inhibitors have a

sterically unhindered triazolyl group, whose nitrogen coordinates through

its free electron pair to the heme iron. Both type I and type II AIs are being

used in post-menopausal breast cancer treatment.

O

O

NN

N

N N

NN

N

N

N

exemestane 20 letrozole 21 anastrozole 22

Figure 3.11 – The structures of the commercial aromatase in-hibitors.

29

Chapter 3. CYP450 aromatase

30

Chapter 4

17β-Hydroxysteroid

dehydrogenase type 1

17β-Hydroxysteroid dehydrogenases are a group of enzymes catalysing the

interconversion between the C17 functionality in 17β-hydroxysteroids and

17-ketosteroids.[102] 17β-Hydroxysteroids are the biologically active forms

of these hormones both in androgens and in estrogens.[103] The reductive

catalytic reaction requires cofactor nicotinamide adenine dinucleotide phos-

phate NADP(H) (Figure 3.2 on page 11) or its dephosphorylated form nicoti-

namide adenine dinucleotide NAD(H). There are 14 subtypes in the 17β-

hydroxysteroid dehydrogenase family, of which 12 are found in humans,

distinguished by substrate and cofactor specificity.[104]

The 17β-hydroxysteroid dehydrogenase type 1 enzyme (17β-HSD1), en-

coded by the gene HSD17B1, plays a crucial role in the female hormonal

regulation by catalysing the NADPH-dependent reduction of the less po-

tent estrone (E1) into the biologically active estradiol (E2) (Figure 2.3 on

page 7). Androgens are also converted, but to a minor extent.[102] 17β-

HSD1 has been crystallised as an apoenzyme and a holoenzyme and with

estrogens, androgens and synthetic inhibitors. [105, 106, 107, 108, 109, 110,

111, 112, 113] Human 17β-HSD1 enzyme is active as a soluble cytosolic

31

Chapter 4. 17β-Hydroxysteroid dehydrogenase type 1

homodimer and is mainly expressed in the ovaries and placenta and in

liver and breast tissue.[102] 17β-HSD1 is also expressed in malignant breast

tissue,[114] where it has been suggested to have an important role in in situ

E2 production.[115, 116, 117, 118]

4.1 Structure and catalytic mechanism

of 17β-HSD1

17β-HSD1 is a member of the short-chain alcohol dehydrogenase super-

family (SDR) and has the conserved and catalytically crucial Tyr-x-x-Ser-

Lys sequence in the active site (catalytic triad Y155, S142 and K159).[103]

Investigations with bacterial hydroxysteroid dehydrogenases suggest that

the near-by asparagine N114 should also be included among the catalytic

residues.[119, 120] One enzyme monomer contains 328 amino acids with a

molecular weight of ∼35 kDa. The tertiary fold of the enzyme contains the

substrate binding domain and a Rossmann fold — a wall of β-sheets sur-

rounded by α-helices, which is associated with cofactor binding.[121] The

catalytic residues, including Y155, S142 and K159 (and N114), are located

near the nicotinamide moiety of the cofactor NADPH.[122] The active site is

a narrow tunnel consisting of lipophilic residues and is complementary in

shape and volume to the steroid skeleton.[123] The substrate E1 binds to the

cavity by forming hydrogen bonds to residues Y155 and S142 from the C17

carbonyl and to H221, at the opposite end of the cavity from the C3 hydroxy

group.[107] An additional hydrogen bond may be formed from E282 to the

C3 hydroxy group. The 17β-HSD1 enzyme contains a loop near the active

site (residues 186–199) for which several conformations have been observed

in crystal structures. This loop has been associated with substrate entry and

cofactor stabilisation.[124, 109] The overall structure of the enzyme and the

active site with selected residues are displayed in Figure 4.1.

A catalytic mechanism has been proposed for the conversion of E1 to E2

32

4.1. Structure and catalytic mechanism of 17β-HSD1

Figure 4.1 – The tertiary structure and active site of 17β-HSD1,PDB entry 1FDT. The cofactor NADP+ is coloured blue and E2 ma-genta. For clarity, only selected active site residues are displayedand the secondary structures are displayed in transparency.

(Figure 4.2).[106] K159 lowers the pKa value of the Y155-OH group, facili-

tating the proton transfer from Y155-OH to the C17 carbonyl oxygen. The

electrophilic attack of the Y155 proton on the C17 carbonyl oxygen and the

hydride transfer from NADPH to C17 results in the formation of E2. S142

has been proposed to stabilise the oxyanion in Y155 [106] or to provide a hy-

drogen bond counterpart for the C17 carbonyl [102]. It should be noted that

K159 is too far from Y155 to interact with it in the crystal structures. How-

ever, a small repositioning would accomplish a suitable orientation.[106] A

network of solvent molecules is proposed to provide the missing proton.

33

Chapter 4. 17β-Hydroxysteroid dehydrogenase type 1

HO

O

NH

H

HO

Y155

N K159H

S142 OH

HO

H

estrone E1 8 NADPH

CONH2

H

H

H2NN114

O

Figure 4.2 – The proposed catalytic mechanism for 17β-HSD1 con-version of E1 to E2. Redrawn and modified from reference [106].

4.2 Molecular modelling of 17β-HSD1

The 17β-HSD1 enzyme is an attractive target for controlling the production

of E2 in the human body and so attacking breast cancer. Yet surprisingly few

drug discovery projects have focused on 17β-HSD1, even though the crys-

tal structure of the enzyme was reported in 1993 [105] and the first structure

was deposited in the PDB in 1995 and released in 1996.[106] The steroidal

skeleton of E2 has been a target for modification with the aim of finding po-

tent and selective inhibitors of 17β-HSD1 . Modification of the substituents

at C16 and C2 led to potent inhibitors [125], which were docked into the

active site of 17β-HSD1. The steroidal moiety of the inhibitor was found to

superimposed with E2 in the crystal structure of the enzyme, and hydrogen

bonds were formed between a substituent at C16 and residues normally

interacting with the cofactor. The study was later extended to include a

substituent at C17, yielding the C5’ amide pyrazole derivative of E1 23 (Fig-

ure 4.3).[126] With use of the inhibitory data and FlexS [127] ligand align-

ment, a QSAR model was built and validated with an external test set. The

QSAR model was later refined with data from both previously published

and newly synthesised compounds.[128, 129] The compounds were again

aligned using FlexS[127], and a comparative molecular similarity indices

analysis (CoMSIA) was performed. The model was validated with an exter-

34

4.2. Molecular modelling of 17β-HSD1

nal test set, which suggested favourable and unfavourable regions in regard

to hydrogen bond acceptors and donors, and regions favouring positive and

negative charge.

OH

O

O

HO

O

OH OH

N

NN

N

NH2

25

HO

24

F

FF

HO

23

NHNHN

O

SN

NO

O

OBr

O

HO

27O

OHN

O BrBr

NNH

O NH2

N

28

HO

26

O

Figure 4.3 – Selected inhibitors from molecular modelling studiesof 17β-HSD1.

In another study, a highly electronegative fluorine substituent at C17 of the

E2 skeleton was tested for 17β-HSD1 inhibitory activity.[130] With a fluorine

at C17, E2 is thought to mimic the transition state of the enzymatic catalysis.

Other, mostly hydrophobic substituents at C2, C8, C9, C13, C14, C15, and

C18 were included in the study. The synthesised compounds were tested

for inhibitory activity against human HSD subtypes 1, 2, 4, 5 and 7. Only

limited selectivity towards 17β-HSD1 was observed. The most potent 17β-

HSD1 inhibitor 24 (Figure 4.3) with three fluorine substituents at C7 and C17

was docked into the active site of the 17β-HSD1 enzyme crystal structure

35

Chapter 4. 17β-Hydroxysteroid dehydrogenase type 1

(PDB entry 1FDT). A binding mode was suggested, where the 17β-fluorine

accepts hydrogen bonds from S142 and Y155. As well, a hydrophobic pocket

for 7α-fluorine was identified.

The first effort to design a hybrid inhibitor, that is, a compound interact-

ing with the substrate and the cofactor binding sites, was reported in 2002

[113], and the idea was published in 2001.[131] With the substrate crystal

structure from the crystallised complex [108] and the cofactor modelled into

the apoenzyme structure [106], a part of the cofactor was deleted and con-

nected to the 16β-position of E2 with methylene linkers of different length.

The compounds were gradually minimised in the active site and in the ab-

sence of the enzyme structure. The energies of the minimisations for each

compound and for each degree of freedom were compared, and the optimal

spacer length was found to be 8 or 9 methylenes. Subsequent synthesis and

inhibitory assay showed that a linker length of 8 methylenes provides the

powerful and competitive inhibitor 25 (Figure 4.3). A crystal structure of

the enzyme with the newly created hybrid inhibitor was also solved and a

binding mode, making use of the substrate and the cofactor binding sites,

was proven (PDB entry 1I5R).[113] An extension to the previous study [113]

was conducted and several hybrid inhibitors, having a steroidal and an

adenosine moiety, were synthesised and tested for inhibitory activity.[132]

Later, new inhibitors based on 25 were synthesised and tested for inhibitory

activity.[133] The substrate E2 and a newly developed inhibitor 26 having

a benzylidene group at C16 (Figure 4.3), were docked into the active site

of the 17β-HSD1 crystal structure (PDB entry 1IOL). The result was that

the steroidal moiety of the inhibitor was shifted relative to E2 in the crys-

tal structure owing to the spatial requirements of the benzylidene group.

Because the new inhibitor was less potent than 25, it was speculated that

further modification of the benzylidene group would lead to a more potent

inhibitor.

The active sites of several crystal structures of the 17β-HSD1 enzyme have

been analysed with the SPROUT program.[134] The narrowness and hy-

36

4.2. Molecular modelling of 17β-HSD1

drophobicity of the active site tunnel were confirmed, and several starting

points for de novo inhibitor design were suggested.

A recent study concerning variously substituted pyrimidinones included

an analysis of the crystal structures of 17β-HSD1 and ideas for modification

of the lead compound derived from the analysis.[135] New inhibitors were

synthesised and tested and selective 17β-HSD1 inhibitors were identified.

The most potent of these was the pyrimidinone derivative 27 (Figure 4.3),

for which the binding mode was also suggested.

Fungal 17β-HSD from Cochliobolus lunatus (17β-HSDcl) has been suggested

as a useful model enzyme for human 17β-HSD1.[136] A QSAR study based

on flavonoids and cinnamic acid esters as 17β-HSDcl inhibitors was per-

formed with the use of quantum chemically calculated descriptors. Wide

differences between the experimental and predicted inhibitory activities were

found.

Pharmacophore modelling in connection with virtual screening is a rela-

tively new approach being pursued in drug discovery projects relative to

17β-HSD1. Schuster et al. [137] developed pharmacophore models based on

two crystal structure complexes: one with the steroidal ligand equilin and

one with the hybrid inhibitor 25. The authors used the automated pharma-

cophore generation program LigandScout [138, 139]. As a result of the phar-

macophore generation, database screening, synthesis and biological testing,

four inhibitors with IC50 values below 50 µM were identified, the most po-

tent being the hydrazinecarboxamide derivative 28 shown in Figure 4.3.

37

Chapter 4. 17β-Hydroxysteroid dehydrogenase type 1

38

Chapter 5

Methods used in molecular

modelling

This chapter introduces the molecular modelling methods used in my work.

Only molecular mechanistic methods were applied, since only they are suit-

able for large molecular systems to be treated as a whole. Quantum chemical

methods can only be applied to much smaller systems than whole proteins,

although a combination of quantum chemical and molecular mechanistic

methods has successfully been used in modelling enzymatic reactions.[140,

141, 142, 143]

5.1 Force fields

The atom nucleus and the surrounding electron cloud are treated separately

in quantum chemistry, making it possible to model chemical reactions, but

only with massive computational resources. Quantum chemistry can, there-

fore, only be applied to relatively small molecules, and definitely not to large

biomolecules in solvent environment. The study of large molecular systems

in silico requires that simplifications be made, e.g., treating atoms as rubber

39

Chapter 5. Methods used in molecular modelling

balls joined together with strings. The properties of these balls and their

interactions are dependent on the selected force field. A force field is a set

of parameters and mathematical equations used to describe the properties

of atoms and their bonded interactions (bonds, angles, dihedral angles) and

atoms and their non-bonded interactions (van der Waals and electrostatic

interactions). The bonded parameters include the definitions of the atomic

masses and charges for different atom types, bond lengths, bond angles,

and dihedral angles. Several types of force field have been developed for

different modelling targets, e.g., for small molecules, biopolymers and lipid

membranes. Together the definitions of the parameters and the equations

define the behaviour and potential energy of the system under study.

In molecular mechanics, a bond between two atoms is described as a har-

monic oscillator between two particles. The potential energy reaches a min-

imum at a certain distance depending on the atoms in question, and it in-

creases exponentially when the distance is either decreased or increased

(Figure 5.1). The harminic potential is defined so that the bond is not al-

lowed to break. A special case is the Morse potential [144], where the poten-

tial energy curve is not quadratic, and at large distances between the atoms

the bond is allowed to break (Figure 5.2). This feature can be useful in sim-

ulating metal coordination bonds, as was done in publications II and III.

Angles are also described as harmonic functions. Torsion angles, however,

owing to their periodic nature, are described as cosine functions (Figure 5.3).

Bonded interactions can be described in terms of the stretching of bonds,

bending of angles and the rotation of torsion angles. The equation usually

used in calculating the potential energy arising from bonded interactions is

described [145] as

Ebonded =∑

bonds

Kb(b− b0)2 +

∑

angles

Kθ(θ − θ0)2 +

∑

torsion

Kχ[1 + cos(nχ− σ)]

where b is the distance between atoms, b0 is the optimum bond length and

40

5.1. Force fields

Figure 5.1 – The quadratic form of the potential used in bondstretching and angle vibration.

Kb is the force constant of the bond; θ is the angle of three atoms, θ0 is the

optimum angle and Kθ is the force constant of the angle; and n is the mul-

tiplicity of the torsion angle (indicating the number of energy minima at a

full turn of 360◦), χ is the value of the torsion angle, σ is the phase angle

(an angle where the potential is at its minimum value) and Kχ is the pa-

rameter defining energy barriers during the turn. Usually in biomolecular

force fields a term for improper dihedral angles is added and described as

a quadratic function. The improper dihedral term ensures that planar rings

stay planar and that the stereochemistry of a molecule is not flipped to its

mirror image during the dynamics run.

Non-bonded interactions between two atoms i and j are calculated with the

equation

Enon−bonded =∑

pairsij

(εij[(Rmin,ij

rij

)12 − 2(Rmin,ij

rij

)6] +qiqj

rij

)

The first term inside the square brackets is the van der Waals term (or Lennard-

Jones term) and represents the attraction and repulsion of two atoms mov-

ing closer to each other. Rmin,ij is the distance where the potential energy

between the two atoms is at minimum, and rij is the interatomic distance.

41

Chapter 5. Methods used in molecular modelling

Figure 5.2 – The Morse potential.

Figure 5.3 – The cosine form of the torsion angle potential.

When the two atoms are far away from each other, the negative term dom-

inates and the interaction is attractive. As the atoms get closer, the positive

term begins to dominate and the interaction becomes repulsive. ε is a pa-

rameter that depends on the atoms in question and defines the deepness of

the potential energy minimum.

The second term describes the electrostatic interactions and is called the

Coulombic term. qi and qj describe the partial charges of the atoms i and

j. Thus, the total energy of the system can be calculated from the equation

42

5.2. Homology modelling

Etot = Ebonded + Enonbonded + Eother

where Eother includes terms that are specific for a certain force field.

Restraints can be added in the course of the simulations to secure the geom-

etry of a certain part of the system. These include position, angle, dihedral,

distance and orientation restraints. The use of distance restraints adds a

penalty to the potential energy function if a certain distance between atoms

is exceeded.[146]

5.2 Homology modelling

Homology modelling is a powerful tool for predicting the 3D structure of a

protein. In contrast to threading, (usually) only one template is used. In ho-

mology modelling, a known 3D structure of a macromolecule (the template)

is used to derive the structure of an unknown macromolecule (the target).

The steps in the modelling process include the identification of proteins with

known 3D structure that are related to each other with known 3D structure

and selection of one of these for the template, identification of structurally

conserved regions (SCR) and structurally variable regions (SVR), sequence

alignment, assignment of coordinates to SCRs and to SVRs, optional side-

chain modelling, relaxation and validation of the final structure.

Templates that are evolutionally related to the target are found with the help

of software or by searching the literature, preferably both. The identifica-

tion of SCRs and SVRs should be based on literature studies, with the as-

sistance of software for secondary structure predictions.[51, 147] The meth-

ods predicting secondary structures include statistical methods and neural

networks.[148] The accuracy of these methods has increased significantly

43

Chapter 5. Methods used in molecular modelling

in the recent years thanks to the increase in the number of solved protein

structures and the development of algorithms.

Sequence alignment is a technique, where the primary amino acid sequence

of the template is aligned with a sequence of the target in order to find struc-

turally or functionally conserved elements. The goal is to match the cor-

responding residues between the sequences, and thus obtain information,

among other things, on the constitution of the target active site structure. If

the sequence similarity is high, i.e., only a few residues differ between the

sequences, the alignment is easy to perform. Conserved mutations between

the sequences are usually not a problem because the physico-chemical prop-

erties and the size of the mutated residue resemble the properties and size

of the original (e.g., Ile to Leu). Problems may arise, however, when a small

residue is mutated to a large one (e.g., Gly to Trp) or the properties of the

new residue are opposite to those of the original (e.g., Asp to Arg). In these

situations the alignment should be carefully examined, and experimental

data should be applied to validate the alignment.

Automated sequence alignments are usually made with dynamic program-

ming, where each residue in each sequence are put on x and y axes of a

table.[149, 150] Each residue is then compared with all other residues in the

other sequence and a similarity score is entered in the table. Finally, all

similarity scores are compared and the highest scores are used to find the

optimal alignment.

Sequence alignment is the single most important part of the homology mod-

elling process. With proteins sharing low sequence identity, this is by no

means a trivial task. Gaps and insertions must be introduced to the target

sequence to make the important residues match each other in the alignment.

While automated methods are available for this task, results from the soft-

ware should always be verified manually and they must correspond to the

experimental data from mutagenesis and similar studies. Popular multiple

sequence search and aligment programmes are FUGUE [151], BLAST [152],

44

5.2. Homology modelling

ClustalW [153] and T-Coffee [154].

After the conserved parts of the structures have been identified and aligned,

the atomic coordinates from the template are assigned to the model. First,

the SCRs (with or without the side chains) are generated by assigning co-

ordinates from the template. SVRs (loops and random coil areas) can be

built de novo or searched from the PDB [155], which provides connective

structures found in protein crystal structures. To end up with a reasonable

protein structure, the modeller needs to take special care when assigning co-

ordinates to sequence areas with gaps and insertions and find suitable con-

necting loops and coils. Identical side chains between the template and the

model are assigned the original conformation from the template. In the case

of similar side chains, the original conformation is preserved where appli-

cable, as in the coordinate assignment protocol in the Homology module of

Insight II.[156] Problems arise when, for example, a glycine is to be replaced

with an arginine. Because an arginine side chain is much larger that that of

the glycine proton, a non-clashing conformation has to be found. Several

conformations for arginine are browsed and the one that best fits the sur-

roundings should be selected. When every residue in the sequence has been

assigned coordinates, the side-chain conformations can be optimised with

the SQWRL [157] or a comparable program.

After sequence-structure alignment, the model structure must be relaxed

due to the clashes and distortions originating from the different spatial re-

quirements of the aligned residues and newly generated coil and loop areas.

Energy minimisation of the model structure is performed, where atom posi-

tions, bond lengths, bond angles and torsion angles are optimised. If the raw

structure of the model includes clashes or distorted bond/dihedral angles,

the structure can be minimised step-by-step. First, the heavy atoms are kept

at their initial positions and only the hydrogens are allowed to move. Sec-

ond, backbone atoms are fixed to their positions and the side chains are free

to move and, finally, all atoms in the model are free to move. Another pos-

sibility is to restrict the movement of heavy atoms by tethering constants,

45

Chapter 5. Methods used in molecular modelling

which are gradually decreased during the minimisation process.[158] The

result is a relaxed protein model, which can (in some cases) be used in fur-

ther studies. However, the minimised model should first be validated, with

molecular dynamics simulations, for example.

5.3 Molecular dynamics simulations

A powerful method for the validation of a homology model, and for study-

ing the motion and conformational changes of macromolecules, is molecu-

lar dynamics simulation (MDS). We used the GROMACS software package

[146], which is widely used, versatile, rapid and available free of charge to

academic users. The procedure for simulating a generated homology model

is as follows: the energy minimised protein model is placed in a simulation

box, the box is filled with water (and ions) and the system is energy min-

imised to obtain a reasonable starting structure for the system for MDS. The

system is assigned a certain temperature and the initial velocities are calcu-

lated for each atom and updated after pre-defined intervals for a pre-defined

number of times. First the system is equilibrated with restrained backbone

atoms to allow the side chains and solvent molecules to adapt to their en-

vironment. Alternatively, all heavy atoms of the protein can be restrained.

After the equilibration, the system is allowed to move freely, and the be-

haviour of the system is monitored in terms of potential energy conserva-

tion and, for example, backbone movement. The result is a time-dependent

trajectory, that is, an ensemble of protein conformations including confor-

mations representing local energy minima.

The time scales used in MDS are on the order of femtoseconds (10−15 s) for

each step. By repeating these steps millions of times, the time span cov-

ered in typical MDS for a reasonable-sized protein is on the order of tens

or hundreds of nanoseconds. With parallel supercomputing, microseconds

and even milliseconds can be achieved. In reality, normal protein folding

46

5.3. Molecular dynamics simulations

requires milliseconds, and even rapid folding takes microseconds.[159] It is

understandable, therefore, that the conformational space of a protein cov-

ered in MDS is limited and the resulting structures are local rather than

global minima.

5.3.1 Periodic boundary conditions

When a real protein is solvated, there are millions of protein molecules sur-

rounded by an even larger number of water molecules. In simulation sys-

tems, however, usually one protein molecule surrounded by a thin sphere

of water molecules is simulated to keep computational costs under control.

This simplification leads to unwanted effects on the surface of the simula-

tion box, very near to the protein. These surface effects can be avoided by

introducing periodic boundary conditions, in which the simulation box is

surrounded by images of itself in all dimensions. If the protein moves out

of the simulation box during the dynamics run, it moves back inside the box

from the opposite side. Thus, the system contains no boundaries, and sur-

face effects do not disturb the actual simulation. Different box shapes are

used to simulate reality as well as possible. A sphere would be the shape

of choice, but there is no periodicity in it. Earlier, a cubic box was the most

common box type, but nowadays a rhombic dodecahedron or a truncated

octahedron is preferred. The latter two are smaller than a cubic box with the

same image distance and thus save computational time.

5.3.2 Long-range interactions

Non-bonded atoms of a real system interact with each other over a large

distance, but these pair interactions cannot all be taken into account in sim-

ulations because the computational cost increases as the number of particles

squared. Usually a cutoff distance of 10 A is imposed. Within the cutoff,

47

Chapter 5. Methods used in molecular modelling

neighbour lists including the closest neghbours for all atoms are stored (and

updated after a few steps) and the interactions between the neighbours are

calculated. However, truncation of the interactions beyond the cutoff does

not provide a realistic situation, and smoothing functions, such as Particle-

Mesh Ewald method (PME) [160], are used to create a more realistic simula-

tion. In PME, the calculation of the coulombic interaction is divided into a

short-range term within the cutoff and a long-range term beyond the cutoff.

The calculation of non-bonded interactions is computationally a highly de-

manding task owing to the large number of particles, and even larger num-

ber of interactions in the system. The computational cost can be decreased

by introducing bond constraints like LINCS [161], where certain (or all)

bonds are constrained to their equilibrium value. Constraining the bonds

means that the bond vibrations are neglected, and the time step can be in-

creased so that the computational cost is reduced.

5.3.3 Temperature and pressure monitoring

The above-mentioned cutoff schemes and rounding errors in the calcula-

tions may lead to elevated temperature and pressure during the simulation.

This problem is typically controlled by coupling the system to an external

thermal bath, which scales the velocities and maintains the simulation tem-

perature. The pressure of the system is controlled by scaling the atomic

coordinates and the size of the simulation box.[162, 163]

5.4 MDS trajectory analysis and protein

model validation

MDS produces a time-dependent ensemble of protein conformations, usu-

ally converged to a local energy minimum. The dynamic properties of the

48

5.4. MDS trajectory analysis and protein model validation

trajectory can be analysed to validate the simulation and/or the generated

protein model. The root mean square deviation (rmsd) is a measure of how

close a certain structure in the trajectory is to the initial (e.g. crystal) struc-

ture. In proteins, the backbone or Cα rmsd is usually calculated covering

all frames of the trajectory. The potential energy of the system is also moni-

tored. In a well-behaving simulation, these two curves should stabilise dur-

ing the simulation. Another analysis tool is the root mean square fluctuation

(rmsf). If calculated per residue, it shows how much a certain residue has

moved during the simulation. Residue movements are very useful in iden-

tifying stable (helices and sheets) as well as flexible (loops, coils) parts of the

protein.

In further studies (e.g., docking), a static structure is required. The question

is which one to choose from the trajectory? One way to obtain a representa-

tive structure is by clustering. The stable part of the trajectory is clustered on

the basis of backbone rmsd, for example, and the representative structure of

the biggest cluster is then extracted from the water environment, minimised

and used for further studies. Another approach is to calculate an average

structure on the basis of the stable part of the simulation. However, the av-

erage structure does not represent any ”real” conformation of the trajectory.

Static properties of the representative structure can be analysed by a pro-

gram such as PROCHECK.[164] PROCHECK produces several analyses of

the quality of the structure, including main chain bond angles and lengths,

chirality, hydrogen bonds and disulfide bridges. Perhaps the most useful

feature is an analysis of the backbone dihedral angles φ, ψ and ω (Fig-

ure 5.4), displayed as a Ramachandran plot (Figure 5.5). This plot shows

a graphical representation of the backbone angle combinations frequently

observed in secondary structure elements in experimental structures, and it

reveals backbone conformations that are illegal. Illegal angles are also fre-

quently found in crystal structures, which is one reason why they should be

relaxed before use in further studies. The ω angles should all be in trans-

configuration, although cis-peptides are not unknown among experimental

49

Chapter 5. Methods used in molecular modelling

protein structures.

HN

Cα peptide chain

O

O

R

phi φ psi ψ

omega ωpeptide chain

Figure 5.4 – The peptide bond and the main chain angles phi φ,psi ψ and omega ω.

50

5.4. MDS trajectory analysis and protein model validation

Figure 5.5 – A Ramachandran plot of CYP2C5 (PDB entry 1NR6).The red area in the upper left corner shows the backbone confor-mations that are found in β-sheets; the conformations found in α-helices are shown in the red area in the middle-left and left-handedα-helices in the small red area in the middle-right. Residuesmarked with red squares have a bad conformation, which usuallydisappears during minimisation and/or dynamics simulation.

51

Chapter 5. Methods used in molecular modelling

5.5 Ligand–protein docking

Biological macromolecules interact with other molecules, either other macro-

molecules or small molecules. Small molecules interact with proteins in

several ways. To mention a few, substrates are modified by a protein (an

enzyme), inhibitors block the function of an enzyme (either covalently or

non-covalently), and agonists and antagonists influence the activity of a

biomolecular receptor. Common to all these functions is that the small mol-

ecule (the ligand) must be recognised by the macromolecule and bind to it,

that is, there must exist favourable interactions that overrule the energetic

states of the separate binding counterparts. Ligand–protein docking predic-

tions form one important area of molecular recognition studies. Docking

can be used in several ways: for example, to study the mechanism of an

enzymatic reaction, to identify possible binding modes for a ligand, and to

screen a database.[165]

Over one hundred years ago, Emil Fischer proposed his lock-and-key mech-

anism for ligand binding. In this model, the active site and the ligand are

considered as rigid objects matching each other in shape and size. Later,

in 1958, Daniel E. Koshland, Jr. proposed a process of dynamic recognition

between a ligand and an active site. In this model, the ligand is matched to

the receptor after the binding, that is, the shape and size of the receptor are

modified during the binding, while a complex is formed between the ligand

and the active site. Modern docking software treats the ligand as flexible

(or a library of rigid conformations is used), whereas the protein active site

is treated as a rigid or a semi-rigid cavity. The flexibility of the active site

is difficult to take into account in docking software: a proper sampling of

active site conformations would require the use of molecular dynamics sim-

ulations, simulated annealing or Monte Carlo methods. Because all these

methods are time consuming, their applicability is limited. One attempt

to take the flexibility of the active site into account involves letting the po-

lar hydrogens move during the docking, as in GOLD [166], or rotating the

52

5.5. Ligand–protein docking

side chains of user-specified residues, as in FlexScreen [167] and the Flex-

ible Docking protocol in Discovery Studio 2.1 [168]. However, taking the

backbone movements or even conformational changes of the protein into

account is beyond reach for fast molecular docking or virtual screening. I

tested several docking software in my work, and usually GOLD produced

the best results. However, as described in publication III, Surflex-Dock pro-

duced a better alignment of the 17β-HSD1 inhibitors.

Search methods and scoring functions

Finding the bioactive conformation of a ligand requires that the conforma-

tional space be searched, preferably fast and accurately. Two main search

methods are used in present docking software: systematic and stochastic.

In a systematic search the rotating bonds of the ligand are turned in small

increments and the lowest energy conformations are saved. These confor-

mations are usually used to create a library of rigid ligand conformations,

as in FRED. [169] In a stochastic search, the ligand is treated as a whole or

as a group of small ligand fragments.

In the first case, new conformations of the whole ligand are generated and

compared with the previous one. If a better (lower in energy) solution is

found, this is stored and the search is continued until some termination cri-

terion defined by the modeller is met. These searches include simulated

annealing and Monte Carlo methods. One approach in whole ligand treat-

ment is the genetic algorithm (GA) used in GOLD [166]. First, random con-

formations of the ligand are generated. Favourable features of the random

conformations are then combined and new offspring are produced, and the

best solution is found after the evolution of the ligand conformations. In

the fragment-based methods, the molecule is cut into fragments, the frag-

ment(s) are docked, and the ligand is ”grown” back by selecting suitable

conformations of the remaining molecular fragments. These methods re-

semble the de novo ligand design approach, where novel inhibitors are gen-

53

Chapter 5. Methods used in molecular modelling

erated from scratch. Examples of the fragment-based methods are the Sur-

flex method [170], where the end fragments of a ligand are fitted to a nega-

tive image of the active site, the so-called protomol, and the FlexX method

[171], where the fragments of the ligand are added to a rigid base fragment.

The search methods can also be divided into local and global methods. In lo-

cal methods a local minimum of the starting structure is searched, whereas

in global methods a larger sampling in the conformational space is per-

formed, which usually provides better solutions.

The scoring function plays an important role during a docking run. It is

a mathematical way of directing the docking routine and ranking the ob-

tained solutions. The scoring functions can be divided into empirical, force

field-based and knowledge-based functions.[172] Empirical scoring func-

tions, such as Chemscore [173] and Surflex-Dock [174], take into account

experimental data on binding affinities, force field-based scoring functions

calculate the van der Waals and electrostatic interactions between the lig-

and and active site atoms, and knowledge-based scoring functions, such as,

PMF score [175], apply statistical data from solved complex crystal struc-

tures. Additionally, solvation terms may be taken into account. There are

no simple preferences of one method over another, but the available scor-

ing functions are usually tested and the best one (that is, matching with the

experimental data) is chosen for the given data set. A common procedure

is the rescoring of the solutions. After a docking run, some other scoring

function is applied to rank the found solutions. A consensus in the scoring

functions produces the most probable solution.

5.6 QSAR methods

Quantitative Structure–Activity Relationship (QSAR) is a method where the

differences in structure of compounds are correlated with differences in

their biological activity. The most common use of QSAR is to predict the

54

5.6. QSAR methods

activity of a ligand based on a mathematical model derived from known

ligands. Usually tens of compounds with known activity are needed to cre-

ate a predictive model. A QSAR study begins with identifying compounds

that are to be used in the model building (the training set). These should be

structurally variable, share the same binding mode and cover a wide range

of activities, from inactive to highly active compounds. After the collection

of the training set, the compounds must be properly aligned. The align-

ment can be done with or without the target protein. In the absence of the

target protein, the compounds are usually aligned to the most active com-

pound, which should be in its binding conformation. If the target protein

is available, docking the training set is the method of choice. Irrespective

of the method, the alignment should be carefully studied and the important

structural features of the ligands (hydrogen bond acceptors and donors, hy-

drophobic cores, aromatic rings etc.) should appear as superimposed. The

partial charges for the atoms in the molecules need to be assigned in or-

der to take into account the electrostatic interactions. Each ligand is rep-

resented via descriptors, which is a fingerprint of, for example, the sterical

and electrostatic and other interactions. The interaction energies are cal-

culated with, for example, the GRID program.[176] Each compound in the

training set is put into a grid, and the interaction energies between a probe

and the compound are calculated. Probes are atoms or functional groups

that represent some feature of the active site. The most straightforward

QSAR method, Comparative Molecular Field Analysis (CoMFA) [177], uses

an sp3-hybridized carbon with a charge of +1 to encompass sterical and elec-

trostatic features of the compounds in the training set. Hundreds of other

probes are available for more sophisticated descriptor generation.[178] For

example, in the Comparative Molecular Similarity Indices Analysis (CoM-

SIA) method [179], the interactions that are used, in addition to the steric

and electrostatic fields, are hydrogen bonds and hydrophobic interactions.

After the descriptors have been calculated, a partial least-squares analysis

(PLS) [180] is performed to link the structural differences in the training set

55

Chapter 5. Methods used in molecular modelling

compounds to their biological activities. Usually, internal cross-validation

methods are applied before the final model is constructed. The leave-one-

out method generates a model that leaves one compound at a time out from

the training set, and predicts the activity of the excluded compound on the

basis of the generated model. This procedure is done for all compounds in

the training set, and the correlation between the predicted and actual data is

calculated. The correlation coefficient for the leave-one-out cross-validation

q2LOO gives an estimate of how predictive the model is. A value of over 0.6

should be obtained. Possible outliers (compounds with inaccurate or wrong

biological data) are usually identified at this point, and excluded from the

training set. Other cross-validation methods are leave-five-out and leave

20% out. The cross-validation also produces the optimum number of com-

ponents that should be used. The number of components is the number of

dimensions in an N dimensional space that includes maximum explanatory

data without including any irrelevant data. After a satisfactory q2 value is

achieved, a non-validated PLS run is performed to produce the final QSAR

model. Before predicting the activity of a compound whose biological data

is unknown, an external validation should be performed. This validation

should include a set of compounds that were not used in the model build-

ing but for which the biological data is known. The final model can be

considered useful if the activities of the compounds in the test set are pre-

dicted with good accuracy. In predicting the activity of a compound with no

known biological data, the candidate compound must be properly aligned

and charges assigned.

One of the strengths of a QSAR model is the visualisation of the interaction

fields from the final model. The fields (steric and electrostatic in CoMFA)

are usually contoured to a certain energy level, and displayed together with

a compound or the target protein. Visualisation of the fields offers valuable

information about the favourable and unfavourable areas surrounding the

compounds and may suggest new ways to modify the compounds. If a

protein structure is available, superimposition of the model with the protein

56

5.7. Pharmacophore modelling and virtual screening

active site not only further validates the model but also helps in identifying

those residues that are most critical for the binding. A QSAR model can

provide valuable guidelines as to which theoretical compounds are worth

synthesising or which functional groups in existing compounds should be

modified.

5.7 Pharmacophore modelling and virtual

screening

A pharmacophore is a spatial arrangement of physico-chemical features cor-

responding to a drug’s biological activity. Typical features are hydrophobic,

aromatic, hydrogen bond donor and acceptor, cation or anion and, positive

or negative ionizable. These features are represented as spheres of user-

defined radius in space. Hydrogen bond features also have a direction.

Exclusion spheres can be added to represent areas where, for example, an

active site residue side chain is located. Pharmacophores can be generated

automatically or manually, for the target protein alone (without a bound lig-

and), for one or a group of compounds (without a protein) or for a protein–

ligand complex. When only the target protein structure is known, the al-

gorithm analyses the active site and shows pharmacophoric features origi-

nating from the active site residues, as in Discovery Studio [168]. If the tar-

get protein structure is not known, the pharmacophoric features can be de-

rived from the ligands. In the best case the structure of a complex is known

and the actual interactions between the ligand and the active site can be

analysed, as in the LigandScout program.[138, 139] This program also pro-

vides accurate spatial restrictions of the active site, when exclusion spheres

are included in the pharmacophore. LigandScout was used in publication

IV because it is capable of generating pharmacophores from ligand–protein

complexes. In the absence of a complex, the Catalyst pharmacophore tools

implemented in Discovery Studio were employed.

57

Chapter 5. Methods used in molecular modelling

The main strength of a pharmacophore is that it can be converted to a search

query and used in virtual screening. A database of 3D structures of com-

pounds is required, and the pharmacophoric features are matched with the

features of the compounds. The user can usually define how many of the

features are not taken into account, and a mapping score for each compound

is calculated ranking the resulting compounds.[168] This virtual screening

method is fast and allows the identification of possibly active compounds

with a completely different scaffold than the existing compounds, and it

is therefore a valuable tool in finding new drug candidates. Validation

of the pharmacophore used in a screening experiment usually requires a

test screening with a database of compounds to which some known actives

and/or inactives are added. A successful validation run finds the actives

and rejects the inactives. As in all modelling work, the results should be

inspected and validated visually before one proceeds further in the drug

discovery process.

58

Chapter 6

Aims of the study

Steroid hormone chemistry is one of the main research topics of the Phyto-

Syn group at the University of Helsinki. A few years ago, molecular mod-

elling was incorporated as part of the multidisciplinary projects with the

aim of providing a broader view of the field. A sophisticated three-di-

mensional homology model of the aromatase enzyme was built to assist

understanding of the final steps in estrogen production. Additionally, an

atomic level explanation was needed for the different inhibitory activities

of phytoestrogens against aromatase. Docking experiments and molecular

dynamics simulations were used to study the binding of a series of 17β-

HSD1 inhibitors, developed in our laboratory, to the 17β-HSD1 enzyme .

The results of inhibitory activity measurements and dockings of these in-

hibitors provided the necessary data to build a 3D QSAR model for further

inhibitor development. To deepen our knowledge of the interactions occur-

ring during inhibitor binding and to find novel scaffolds for the design of

new 17β-HSD1inhibitors, pharmacophore modelling and virtual screening

were included in the study. Specifically, the aims of my work were

? to build a homology model of the aromatase enzyme and study the

interactions between it and phytoestrogens (I and II)

Chapter 6. Aims of the study

? to study the binding of synthesised 17β-HSD1 inhibitors by molecular

dynamics simulations and to derive a QSAR model on the basis of

biological data for these inhibitors (III)

? to find novel scaffolds for inhibitor design using pharmacophore mod-

elling and virtual screening (IV)

60

Chapter 7

Results and discussion

7.1 The aromatase model and the binding

of phytoestrogens (I and II)

After a few quiet years, the homology modelling of aromatase was resumed

in 2000 when the crystal structure of the first crystallised mammalian CYP450

enzyme CYP2C5 was published.[13] Two further crystal structures of the

same enzyme but with co-crystallised ligands were published in 2003.[54,

181] These structures provided the starting point for our goal to build a so-

phisticated homology model of the aromatase enzyme.

The process was started with the submission of the aromatase sequence

downloaded from the Swiss-Prot database [182] to the FUGUE server [151].

The initial alignment of aromatase with CYP2C5 (PDB entry 1N6B)[54] ob-

tained from FUGUE was manually refined on the basis of the PsiPred [183]

secondary structure element predictions and several mutagenesis experi-

ments [34, 35, 70, 69, 72, 71, 62, 52, 77]. Various alignments were generated

by focusing on the substrate recognition sites defined for the CYP2 enzyme

family by Gotoh [27]. The final alignment is shown in Figure 7.1.

61

Chapter 7. Results and discussion

Figure 7.1 – Alignment of the aromatase sequence to the CYP2C5sequence. The residues corresponding to α-helices are colouredred and β-strands green. Boxes denote areas where the coordinateswere assigned from the template.

The coordinates for the SCR regions, the heme, and loops of identical length

between the template and the model were assigned from the template, and

the remaining loops were searched from the PDB [155]. Splice points, that is,

connective peptide bonds between the SCRs and the loops, were optimised

to trans-configuration and to optimal bond length before the raw model

minimisation. Side-chain optimisation with an external software was not

performed because the Homology module of Insight II software [156] as-

signs the conformation of the original side chain to the new one where ap-

plicable. In cases where bad contacts were observed, new conformations

were fetched from a rotamer library of the Homology module. The raw

model was minimised, and equilibrated and simulated using MDS with

GROMACS. The stable part of the trajectory was clustered on the basis of the

backbone root mean square deviation (rmsd), and the representative struc-

ture of the biggest cluster was extracted and minimised. Subsequently, MDS

62

7.1. The aromatase model and the binding of phytoestrogens (I and II)

study was made with the natural substrate androstenedione (ASD) to find a

plausible orientation of the substrate.

The MDSs performed on an empty-cavity model and for the complex re-

sulted in stabilisation of the backbone movements and the potential ener-

gies. The representative structure of the complex trajectory was subjected to

PROCHECK [164] to analyse the quality of the model. There were only two

residues in the disallowed region of the Ramachandran plot (Figure 7.2),

and as both are located in the β-sheet bundle they do not contribute to the

active site. The substrate ASD was properly oriented above the heme for the

oxidation of C19 of ASD (Figure 7.3). In summary, the model showed sta-

ble behaviour both with the substrate and with an empty active site cavity.

The majority of the mutagenesis results were explained on the basis of our

model.

Figure 7.2 – The Ramachadran plot of the generated aromatase ho-mology model. For explanations of the markings, see Figure 5.5.

63

Chapter 7. Results and discussion

Figure 7.3 – The overall structure of the generated aromatasemodel. α-Helices are coloured red, β-sheets yellow and the coilsand loops green. The heme and ASD are represented as blue sticksand the heme iron is represented as a magenta sphere.

As described earlier, phytoestrogens are moderate to weak inhibitors of the

aromatase enzyme. We wished to find atomic level explanations for the dif-

ferent binding affinities observed for lignans, flavonoids, isoflavonoids and

coumestrol but with emphasis on the lignans because their binding mode

to aromatase had not been reported earlier. Experimental inhibition data

was available for some lignans, including natural compounds and their the-

oretical and verified metabolites. In the case of flavonoids, isoflavonoids

and coumestrol, experimental data was available but an atomic level ex-

planation for the binding affinity, and especially for the Fe–O coordination

geometry, was desired. Molecular dynamics simulations (MDS) were per-

formed on the complexes formed by aromatase and one of the two natu-

ral inhibitors enterolactone 15 (ENL) and α-naphthoflavone 13 (ANF) (Fig-

ure 3.10 on page 27). ENL and ANF were placed in the active site cavity and

oriented so that the coordination between the oxygen and the iron could

64

7.1. The aromatase model and the binding of phytoestrogens (I and II)

take place. A Morse potential was used to define the interaction between

the iron and the coordinating oxygen. MDSs with GROMACS were per-

formed for both complexes. In the case of ENL the system stabilised after

12 ns (total simulation time was 15 ns), and with ANF the stabilisation took

17 ns (total simulation time 20 ns). The stable parts of the trajectories were

clustered and the representative structures of the largest clusters were ex-

tracted and minimised in vacuo. All-atom models of the protein were gen-

erated for the docking studies, which were done with the GOLD program

[166] because it was able to reproduce the binding conformations of ligands

in four complexes (PDB entries 1SUO, 1Z11, 1W0G and 1J0C). The modified

metal binding parameters [184] of GOLD were not included. As a result, the

coordination orientation and residues providing hydrogen bonding coun-

terparts and lipophilic areas affecting the binding of the compounds under

study were identified. Since the coordination is the single most important

contribution to the binding strength, the geometry of the coordination was

analysed with lignans and flavonoids. The dockings suggested that the tilt-

ing angle between the lignanolactone carbonyl plane and the heme plane

was 40◦, representing a 50◦ deviation from the optimal 90◦. The distance

from the heme iron to the oxygen was 0.24 nm, or 0.05 nm more than the

average 0.19 nm (based on the database search described in publication II).

The Fe–O=C angle was 129◦, showing a 9◦ deviation from the optimal 120◦.

These deviations from the optimal values, especially the tilting angle and

the distance, probably lead to weaker coordination and thereby, to weaker

binding.

When ring A of the lignan skeleton (Figure 2.1 on page 5) contains a C3

hydroxy group, a hydrogen bond is formed to the backbone carbonyl of

T310. When the hydroxy group is at C4, the hydrogen bonding counterpart

is the backbone carbonyl of P481. The hydrogen bonds from ring B have

their counterparts in E302 or I305. A hydrophobic area at SRS-5 (Figure 3.3

on page 12) accommodates the hydrophobic skeleton, especially in the case

of NDGA. In the case of lignanodiols, that is, lignans lacking the lactone

65

Chapter 7. Results and discussion

Figure 7.4 – Above left: The binding mode of ENL (green). Aboveright: The binding mode of ANF (orange). Below left: The coordi-nation geometry between C4 carbonyl and the heme iron for fla-vone (cyan) and isoflavone (blue). Below right: The binding modeof coumestrol (yellow). Hydrogen bonds are displayed as dashedred lines

structure, the coordination takes place from either the phenolic or the ali-

phatic hydroxy groups. Hydrogen binding sites are the backbone carbonyls

of P481 and I305. Interestingly, enterodiol 16(END) (Figure 3.10 on page 27)

does not form any hydrogen bonds to the active site residues but forms an

intramolecular hydrogen bond between the phenolic hydroxy groups. The

binding orientations of ENL and ANF are displayed in Figure 7.4.

The binding mode of the flavone/isoflavone compounds, as proposed ear-

lier [185, 71], suggests that the C4 carbonyl coordinates with the heme iron.

These earlier studies relied on homology models of aromatase with an ac-

66

7.1. The aromatase model and the binding of phytoestrogens (I and II)

tive site cavity not refined for the flavonoid skeleton. In this work, MDSs

were performed for an ANF–aromatase complex to find the optimal active

site conformation for the flavonoid/isoflavonoid binding, and these com-

pounds were then docked to the representative structure from the MDS

study. The dockings showed the distance from the C4 carbonyl oxygen to

the heme iron to be 2.0 A for flavone and 2.2 A for isoflavone. The angle Fe–

O=C was found to be 136◦ for flavone and 160◦ for isoflavone (the optimal

angle is 120◦), while the corresponding tilting angle between the carbonyl

plane and the heme plane were 73◦ for flavone and 70◦ for isoflavone. The

coordination geometries of flavone and isoflavone show that the coordina-

tion between flavone and heme is closer to the optimal values and, therefore,

leads to better binding (Figure 7.4).According to my model, the positive in-

fluence of a C7 hydroxy group in the flavone skeleton (see Section 3.5 on

page 23) originates from the hydrogen bond to S478. A hydroxy group at

C6 or C8 is slightly shifted compared to one at C7, and does not support a

hydrogen bond. The C4’ hydroxy substitution leads to the unfavourable in-

teraction between a polar hydroxy group and the hydrophobic environment

formed by residues I132, F148 and C299, and thereby, to decreased activity.

Wang et al. [93] found coumestrol 19 (Figure 3.10 on page 27) to be a better

inhibitor of aromatase than any of the lignans they measured in the same

study. My model suggests that the good inhibitory activity is the result of a

relatively good coordination geometry between the carbonyl group and the

heme iron, including a tilting angle of 67◦, a distance of 1.8 nm between the

iron and the oxygen, and an Fe–O=C angle of 139◦. Additionally, two hydro-

gen bonds between coumestrol and S478 and C299 (Figure 7.4), were found

to anchor coumestrol in place. Mutants S478T and H480Q decreased the

binding affinity for coumestrol.[75] Mutation of the serine at 478 to a thre-

onine probably alters the position and orientation of the hydrogen bonding

counterpart with coumestrol and leads to the loss of the hydrogen bond.

H480 is located near the aromatic skeleton of coumestrol, and the mutation

to a glutamine probably leads to the loss of π – π interactions and a decrease

67

Chapter 7. Results and discussion

of inhibition potency.

Figure 7.5 – Letrozole 21 docked into the active site of aromatase.Letrozole is coloured lemon, and the heme iron is represented asa magenta sphere. For clarity, only selected residues are displayedand hydrogens are omitted.

In further validation of the homology model, MDS was performed for a

letrozole–aromatase complex (unpublished results). Letrozole 21 (Figure 3.11

on page 29), was docked into the active site and MDS was performed. The

simulation showed stable behaviour in terms of potential energy and the

backbone rmsd. The representative structure of the MD trajectory was ex-

tracted and letrozole was docked into the active site. The resulting orienta-

tion is shown in Figure 7.5. Mutagenesis studies [44, 62] have shown that

mutants E302D, I133Y, S478T, S478A, H480Q and H480K are more resistant

to letrozole than the wild-type enzyme. According to my model, the conser-

vative mutation E302D leads to the loss of the hydrogen bond between E302

and Q298, probably affecting the active site shape or volume. I133Y prob-

ably results in decrease in the active site cavity volume, S478T and S478A

change the position of a possible hydrogen bonding counterpart, and H480K

68

7.2. Molecular modelling of 17β-HSD1 and its inhibitors (III and IV)

and H480Q result in the loss of aromatic interactions between letrozole and

H480. The mutant D309A showed highly increased resistance to the in-

hibitor. This is hard to explain with my model, as are the other results with

D309 mutants (see publication I). The position of D309 is well established: it

is located in the I-helix near the very important T310. In the model, letrozole

is not oriented correctly to form hydrogen bonds with D309, and probably

it cannot change the orientation to establish such interactions. Most likely,

then, the D309A mutation leads to conformational changes in the active site.

I474 lies further away from the active site than do S478 and H480 and mu-

tants I474Y, I474W, I474M and I474N showed increased affinity to letrozole

[44], suggesting that the active site is somehow modified by a mutation at

this position. It is difficult to draw further conclusions, however, without

studying the effect of the mutation by MDS or other comparable method.

7.2 Molecular modelling of 17β-HSD1

and its inhibitors (III and IV)

Another attractive target for blocking estrogen production, in addition to

aromatase, is the 17β-HSD1 enzyme function. A high-throughput screening

(HTS) study against the 17β-HSD1 enzyme led to the discovery of a lead

compound 29 (Figure 7.6) [186], and a set of inhibitors based on the tetra-

cyclic skeleton of this lead compound were synthesised in our laboratory

and tested for inhibitory activity.[187] To simulate the dynamic process of

ligand binding and find the structural factors affecting it, MDSs were per-

formed for two potent inhibitors. The crystal structure of 17β-HSD1 (PDB

entry 1FDT) [107] containing the reduced product E2 and the oxidised cofac-

tor NADP+ was first relaxed using minimisation and MDS. This structure

was because there is evidence that the active site of aldo-keto reductase en-

zymes adapts to the ligand during binding [188, 189] and that the cofactor

NADP+ binds before the substrate.[190]

69

Chapter 7. Results and discussion

the lead compound 29

S

O

OH

N

N

O

S

O

N

N

O

S

O

N

N

O

30

31

Figure 7.6 – Structures of the inhibitors 29 and 30 used in the 17β-HSD1 MDS studies, and the structure of the most potent inhibitor31.

First, the crystal structure was relaxed using MDS. During the simulation

of the crystal structure, the reduced product estradiol E2 was moved away

from the catalytic triad (Ser142, Tyr155 and Lys159) and from the cofactor

NADP+. This is probably due to the rejection of the product by the active

site and the relocation of Phe192, a residue located in a loop with two differ-

ent conformations in the crystal structure. Figure 7.7 shows the positions of

E2 in the crystal structure and after the relaxation.

As a means to finding the optimal interactions between the active site and

the inhibitors for MDS studies, the lead compound 29 and another potent

inhibitor 30 were docked into the active site of the relaxed enzyme using

GOLD [166]. Despite the similarity of the structures of the two inhibitors,

two different binding modes were observed (Figure 7.8). MDS studies of the

two inhibitor–enzyme complexes revealed that the orientation of the lead

compound 29 was more stable and it formed active site interactions that

were not observed in the original or relaxed crystal structure of 17β-HSD1.

70

7.2. Molecular modelling of 17β-HSD1 and its inhibitors (III and IV)

Figure 7.7 – The positions of E2 before (magenta) and after (cyan)the relaxation of the crystal structure 1FDT. NADP+ is colouredblue. For clarity, hydrogens are omitted and the active site residuesare displayed as thin sticks.

Figure 7.8 – The two different binding modes of the inhibitors usedin MDSs. The lead compound 29 is coloured yellow and the otherpotent inhibitor 30 is coloured green. For clarity, hydrogens areomitted and the active site residues are displayed as thin sticks.

71

Chapter 7. Results and discussion

Figure 7.9 – The aligned 17β-HSD1 inhibitors after docking. Theprotein surface is displayed as a transparent surface and a part ofNADP+ is shown in blue. For clarity, hydrogens are omitted.

Figure 7.10 – The correlation between the predicted and LOGITtransformed pIC50 values of the 17β-HSD1 inhibitors. The trainingset is marked with blue squares and the test set with red circles.

72

7.2. Molecular modelling of 17β-HSD1 and its inhibitors (III and IV)

In a next step, a library of tetracyclic pyrimidinones synthesised in our lab-

oratory [187] was docked, using Surflex-Dock [170], into the enzyme struc-

ture obtained from MDS with the lead compound 29. The compounds were

found to be well aligned (Figure 7.9), and the ranking from the docking was

in accordance with the experimental inhibition data. This encouraged the

construction of a 3D-QSAR model to predict the activities of compounds not

yet tested for biological activity. Since no IC50 values had been determined

for the inhibitors, but only inhibition percentages at two inhibitor concentra-

tions (0.1 µM and 1 µM), LOGIT transformation [48] was used to obtain the

IC50 values. These values were then employed as dependant variables dur-

ing the CoMFA model building. The more complex CoMSIA method was

tested, too, but the resulting model had no predictive power. The CoMFA

model, on the other hand, had good predictive power, verified with an ex-

ternal test set (Figure 7.10).

The visualisation of the CoMFA fields reveals areas where a certain type

of interaction is either preferred or not preferred. The superimposition of

the CoMFA fields with the enzyme structure from the MDS with the most

potent compound 31 (Figures 7.6 and 7.11) revealed a hydrophobic pocket

(green area) formed by residues V143, G144, L149, F192, M193 and F259.

This pocket accommodates the bulky aromatic thioether side chain of in-

hibitor 31. Red areas located next to residues N152 and R258 suggest that a

negatively charged group at this position would serve as a hydrogen bond-

ing counterpart for these residues, and the blue area near Y218 suggests a

positively charged group at this position.

In summary, MDS with the lead compound 29, identification of the plau-

sible binding modes of the inhibitors and generation of the CoMFA model

helped in understanding the requirements for an active inhibitor. In partic-

ular, the CoMFA fields superimposed with the enzyme structure revealed

the hydrophobic pocket formed during the MDS, and the predictive power

of the model can help in the design of even more active inhibitors of 17β-

HSD1.

73

Chapter 7. Results and discussion

Figure 7.11 – The CoMFA fields superimposed with the enzymestructure obtained from MDS with the lead compound 29. Themost potent inhibitor 31 is coloured orange and NADP+ blue.

Pharmacophore modelling is a new method in 17β-HSD1 drug discovery

projects. Four pharmacophore models were developed in this work, based

on A) the relaxed crystal structure of 17β-HSD1 from the QSAR study (see

publication III), B) our thienopyrimidinone library [187], C) the 17β-HSD1

crystal structure complex with E2 (PDB entry 1FDT [107]), and D) the docked

complex of our most potent inhibitor 31 and 17β-HSD1(see publication III).

Where a complex was analysed, the LigandScout program [138, 139] was

used, and where ligands alone or the receptor alone were analysed, the Dis-

covery Studio 2.1 software (DS) [168] was employed. With the data noted

above, four pharmacophore hypotheses were generated, differing from each

other in number or the nature of the pharmacophoric features. Figure 7.12

shows the four generated pharmacophores.

The Interaction Generation protocol in DS, performed on the relaxed crys-

tal structure from III, followed by clustering of the pharmacophoric fea-

tures and manual adjustment, produced five pharmacophoric features (A

74

7.2. Molecular modelling of 17β-HSD1 and its inhibitors (III and IV)

Figure 7.12 – The four generated 17β-HSD1 pharmacophoresbased on A) the relaxed crystal structure, B) the thienopyrimidi-none library, C) the crystal structure 1FDT and D) the complex ofour most potent inhibitor 31 and 17β-HSD1. For clarity, hydro-gens are omitted and in pharmacophore D only selected exclusionspheres are displayed.

in Figure 7.12). Two hydrogen bond donors were pointed towards S222 and

E282 and one hydrogen bond acceptor was pointed towards S142. Addi-

tionally, two hydrophobic centres were located in the centre of the active

site. The Common Feature Pharmacophore Generation protocol of DS was

used to find the most important factors contributing to tight binding in our

thienopyrimidinone inhibitor library (B in Figure 7.12). Three hydrogen

bond donor features were identified, two located at the carbonyl groups

and one at the pyrimidinone nitrogen. However, the donor feature at the

nitrogen was manually changed to a hydrophobic feature after careful in-

spection of the active site residues showed that there are no residues in this

area capable of functioning as hydrogen bond donors. The interactions in

75

Chapter 7. Results and discussion

the complex crystal structure 1FDT [107] were identified with the Ligand-

Scout program. An automated pharmacophore generation identified two

hydrogen bond acceptors, one pointing from the C3 hydroxyl towards Y155

and one from the C17 hydroxyl towards H221, and two hydrophobic fea-

tures, one located at C18 and one at the aromatic ring A of E2 (C in Figure

7.12). LigandScout was also used for the pharmacophore generation of the

docked complex of our most potent inhibitor 31 (Figure 7.6) and 17β-HSD1

after MDS. The program generated a hydrogen bond from the thiophenyl

side chain hydroxyl towards the backbone carbonyl of G144, two hydrogen

bonds from the two carbonyls in the compound towards Y218 and R258,

and one hydrophobic feature around the thiophenyl substituent (D in Fig-

ure 7.12).

The Maybridge HitFinder database [191], consisting of 14,400 drug-like com-

pounds, was used in the database searches. To enlarge the conformational

space of the compounds in the database, the Diverse Conformation Gen-

eration protocol in DS was used to generate a maximum of 10 conformers

for each compound. The Ligand Pharmacophore Mapping protocol was

used to rank the conformers from the database on the basis of their fit to the

pharmacophores, and the best ranking conformer of each compound was

accepted or rejected on the basis of their scaled FitValues (from 0 to 1). Com-

pounds having a scaled FitValue of ≥ 0.5 were accepted. These compounds

were then docked to the 17β-HSD1 structure using GOLD Suite [166] and

the default scoring function Goldscore. Additionally, the docked solutions

were rescored with the Chemscore scoring function. The above-mentioned

methods (FitValue, Goldscore and Chemscore) were used to acquire a con-

sensus score for each of the compounds, and the compounds shown in

Figure 7.13 were identified. Additionally, the compounds identified for

pharmacophores originating from our inhibitors (pharmacophores B and D)

were evaluated with the developed QSAR model (see publication III). All

compounds were predicted to have inhibitory activity at sub-micromolar

levels. Note that the compounds in Figure 7.13 should be treated as poten-

76

7.2. Molecular modelling of 17β-HSD1 and its inhibitors (III and IV)

tial lead candidates because they possess functional groups that are usually

unfavoured in drug-like compounds.

In summary, four pharmacophores generated by different methods were

used to identify the contributions to the binding affinity to the 17β-HSD1

enzyme. The pharmacophores were used as 3D database search queries,

and novel scaffolds for potential 17β-HSD1 inhibitors were identified.

NS

N HNHN

O

F N

N

SHN

ONH

SO

O

F F

NS

N

NO NH2

O

ON

S

NN

NCl

Pharmacophore B

S

NS

NCF3

O

SO

O

HN

N N

CF3

Pharmacophore C

O

O

NH

O

NNS

F S

OCF3

CF3

S

NN

NF3C

Pharmacophore D

HON

NO2

O

CF3

ON

H2NN N

NHS

NO

CF3

Pharmacophore A

Figure 7.13 – Compounds identified from the MaybridgeHitFinder database using the generated pharmacophores and con-sensus ranking.

77

Chapter 7. Results and discussion

78

Chapter 8

Conclusions

In this study of the structure and interactions of the estrogen-producing en-

zymes CYP450 aromatase and 17β-HSD1, I have shown that, despite the low

sequence identity, a stable aromatase model that explains most of the results

from mutagenesis studies can be built, using a mammalian CYP450 crystal

structure as template. Further, this model explains the differences in the

binding affinity of phytoestrogenic compounds. State-of-the-art software

was used in the process, and molecular dynamics simulations in particular,

proved to be an excellent tool in model validation and study of the bind-

ing modes of ligands with aromatase . The same was shown for 17β-HSD1.

QSAR in combination with sophisticated docking procedures were used to

generate a predictive model for 17β-HSD1 inhibitors having a tetracyclic

thienopyrimidinone scaffold. Finally, 17β-HSD1 pharmacophore genera-

tion followed by virtual screening produced several candidates for future

drug design projects.

The results of this study can be utilised in designing new inhibitors for both

of the enzymes. The predicted structure of aromatase can be applied in

de novo design of aromatase inhibitors and study of their interactions with

the enzyme, and the QSAR model of 17β-HSD1 can be used to predict the

activities of newly designed inhibitor candidates prior to their synthesis.

79

Chapter 8. Conclusions

The pharmacophore models can be used in searching any 3D database for

lead candidates.

Altogether, this work shows the power of molecular modelling in modern

macromolecular, as well as small molecule, research projects. It comple-

ments traditional wet chemistry and contributes in a valuable way to multi-

disciplinary research projects.

The primary outcomes of this study concerning drug discovery are: A) a

letrozole-validated model of the aromatase enzyme and B) new pharma-

cophores for virtual screening of 17β-HSD1 inhibitors.

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Structures, names and codes of the essential amino acids

HONH2

COOH

Serine, Ser, S

COOHH2N

NH2

Asparagine, Asn, N

COOH

NH

Proline, Pro, P

COOH

NH2

Isoleucine, Ile, I

COOH

NH2

Lysine, Lys, K

ONH2

COOH

Alanine, Ala, A

NH2

COOH

Methionine, Met, M

COOHHO

NH2

O

Glutamic acid, Glu, E

COOHHS

NH2

Cysteine, Cys, C

COOH

NH2

Valine, Val, V

COOH

NH2

Phenylalanine, Phe, F

COOH

HN

NH2

Arginine, Arg, R

H2N

NHCOOHHO

NH2

Aspartic acid, Asp, D

O

COOHH2N

NH2

O

Glutamine, Gln, Q

COOHH

NH2

Glycine, Gly, G

NH2

COOH

Histidine, His, H

HN

N COOH

NH2

Leucine, Leu, L

OH

NH2

COOH

Threonine, Thr, T

NH2

COOH

Tryptophan, Trp, W

HN

COOH

NH2

Tyrosine, Tyr, Y

HO

S

H2N