New computational strategy to analyze the interactions of ERα and ERβ with different ERE sequences

New Computational Strategy to Analyze the Interactions

of ERa and ERb with Different ERE Sequences

ANNA MARABOTTI,1,2 GIOVANNI COLONNA,2 ANGELO FACCHIANO1,2

1Laboratory of Bioinformatics and Computational Biology, Institute of Food Science,National Research Council, Avellino, Italy

2Interdepartmental Research Center for Computational and Biotechnological Sciences,Second University of Naples, Naples, Italy

Received 30 June 2006; Revised 26 September 2006; Accepted 29 September 2006DOI 10.1002/jcc.20582

Published online 31 January 2007 in Wiley InterScience (www.interscience.wiley.com).

Abstract: The importance of computational methods for the simulation and analysis of biological systems has

increased during the last years. In particular, methods to predict binding energies are developing not only with the

aim of ranking the affinities between two or more complexes, but also to quantify the contribution of different types

of interaction. In this work, we present the application of HINT, a non Newtonian force field, to rank the affinities

of complexes formed by estrogen receptors (ER) � and � and different estrogen responsive elements (ERE) near the

estrogen-regulated genes. We used the crystallographic coordinates of the DNA binding domain of ER� complexed

to a consensus ERE as a starting point to simulate several complexes in which some nucleotides in the ERE

sequence were mutated. Moreover, we used homology modeling methods to create the structure of the complexes

between the DNA binding domain of ER� (for which no experimental structures are currently available) and the

same ERE sequences. Our results show that HINT is able to rank the affinities of ER� and ER� for different ERE

sequences, and to correctly identify the positions on the DNA sequence that are most important for binding affinity.

Moreover, the HINT output gives us the opportunity to identify and quantify the role played by each single atom of

amino acids and nucleotides in the binding event, as well as to predict the effect on the binding affinity for other nu-

cleotide mutations.

q 2007 Wiley Periodicals, Inc. J Comput Chem 28: 1031–1041, 2007

Key words: HINT; computational biology; modelling; binding affinity; protein–DNA interaction; estrogens

Introduction

Computational biology in the last years has become an increas-

ingly important discipline for the study of biomolecular systems

and for the prediction of their behaviors. Among the most chal-

lenging goals to achieve, an important target is the capability of

describing in a quantitative or semi-quantitative way the phe-

nomena leading to macromolecular recognition. In fact, the for-

mation of a thermodynamically stable and specific complex

between interacting macromolecules is a key element to allow

biological systems to perform their functions. Among macromo-

lecular associations, the site-specific interactions between pro-

teins and DNA regulate most important events in cells, such as

transcription, replication, and recombination, and therefore the

investigation of the mechanism driving binding energy and spec-

ificity in protein–DNA complexes is a main field for molecular

simulations. However, the problem of describing and quantifying

the mechanisms for recognition between amino acids and nucle-

otides has not still been definitely solved, despite almost 2 deca-

des of studies.1,2 The main difficulties raise from the fact that

the multiple physico-chemical factors determining the required

level of specificity, and especially their complexity and inter-

play, are not easily managed by computational methods. In fact,

several types of interactions (i.e., electrostatic interactions,

hydrogen bonds, hydrophobic effects, etc.) are involved in for-

mation of the complex and must be taken into account to predict

the binding energy, together with structural information raising

from DNA deformation, distance-dependent multi-body interac-

tions, and solvation contributions. Nevertheless, during the last

years, some programs and methods were developed that attempt

to correlate theoretical and experimental binding affinities for

protein–DNA complexes.3–7 They utilize Newtonian physics and

molecular mechanics force field parametrization, or knowledge-

based statistical potentials. In many cases, they can accurately

predict the preferred binding sites as well as the qualitative pro-

Contract/grant sponsor: Italian Ministry of University (MIUR); contract/

grant numbers: FIRB RBNE0157EH_003

Correspondence to: A. Marabotti; e-mail: [email protected]

q 2007 Wiley Periodicals, Inc.

tein–DNA interaction, but the overall accuracy of the quantita-

tive predictions may be limited, for example by the risk of over-

simplification of the assumptions, or by the database dependence

on statistical potentials.4,7 In fact, in the Newtonian-based ap-

proaches, free energies or entropies are often modeled as sums,

based on group additivities, or free energy component additiv-

ities, or entropy component additivities, but for additivity it is

essential that all the components are independent, which is not

the case in most biological events.8 On the other hand, knowl-

edge-based statistical potentials try to overcome this limit by

extracting information about macromolecular interactions

directly from their experimentally-derived molecular structures.

Their principal weakness is that they strongly depend on chain

length and composition of the reference database, and that may

be unable to quantitatively reflect the true contact energy, even

if they often correctly rank the relative strengths of inter-residue

interactions.7,9

An alternative approach is used in the program HINT (Hy-

dropathic INTeractions). The physico-chemical meaning of

the hydropathic analysis with HINT has been previously re-

viewed.10,11 Briefly, HINT is an empirical force field based on

parameters derived from experimentally determined measure-

ments of the partition coefficient between water and 1-octanol

(LogPo/w). The strength of this program arises from the fact

that, since LogPo/w is a free energy parameter, its measurement

takes into account both enthalpic and entropic contributions

originating from all molecules, including water, that participate

in a complex. Different hydropathic properties of the interacting

atoms are then implicitly expressed in the key parameter, the

hydrophobic atomic constant (ai), which is calculated by a pro-

cedure adapted from the CLOGP method conceived by Hansch

and Leo.12 Because the HINT analysis is carried out on biomo-

lecular systems with three-dimensional (3D) structure, geometric

information is embedded in the procedure. In this way, the inter-

action is considered a concerted event, as it occurs in nature.8

The total interaction score between two molecules i and j

involved in the complex, called HINTSCORE, provides a quan-

titative evaluation of the association process, and is determined

by the following equation:

HINTSCORE ¼X

i

X

j

bij ¼X

i

X

j

ðai � Si � aj � Sj � Tij � Rij þ rijÞ

where bij is the interaction score between the interacting atoms i and

j, ai and aj are the hydrophobic atom constant of atoms i and j, Siand Sj the solvent accessible surface area of atoms i and j, Tij a

logic function assuming þ1 or �1 values, depending on the nature

of interacting atoms, and Rij and rij are functions of the distance

between atoms i and j. A positive bij value identifies the favorable

contacts (hydrogen bonds, acid/base, and hydrophobic interactions),

while negative bij values identify the unfavorable ones (acid/acid,

base/base, hydrophobic/polar). The sum of all bij terms describes the

total interaction between the two species.

Since each bij is related to a partial �g value, the total HINT-

SCORE is directly related to the global DGinteraction. A linear

relationship was described in a comprehensive free energy vali-

dation study performed on a collection of several structurally

well-characterized protein–ligand complexes for which accurate

binding data were available13–15 (also reviewed in ref. 16).

In our current work, we extended the application of HINT to

the study of the interactions between estrogen nuclear receptors

and DNA. The characterization of the interactions and phenom-

ena that lead to the estrogen-dependent transcription can be of

outstanding interest, if we consider their importance for the de-

velopment of drugs for the prevention and the treatment of

breast cancer.17,18 Therefore, it is important to have rapid and

reliable techniques that could suggest to biologist, medicinal

chemists, and clinicians how the interaction between the receptor

and its target DNA sequence can be influenced and, from a mo-

lecular point of view, what kind of elements (atoms and bonds)

are involved in such interaction.

Estrogen receptors (ER) are ligand-activated proteins that,

following estrogen binding, dimerize and bind to specific DNA

sequences, called estrogen response elements (ERE), which were

found in the regulatory regions of several estrogen target genes.

This results in enhanced transcription of the target gene.19 Two

isotypes of ER, � and �, which are composed by six domains

with different functions were identified in mammals.17,20,21 They

exhibit almost the same affinity for the endogenous estrogen,

and display similar ligand binding profiles, but in general ER�binds to ERE with a lower affinity than ER�.22–24

Several experimental studies (reviewed in ref. 25) analyzed

the interaction of ER� and ER� with DNA and concluded that a

minimal consensus ERE sequence is characterized by two palin-

dromic (i.e. that are identical when read either starting from 50

or 30 terminal) half sites each formed by six conserved nucleo-

tides, separated by three bases: 50-AGGTCAnnnTGACCT-30.26

They also showed that, when nucleotide changes are present

with respect to the optimal consensus sequence, the ER affinity

is lower and the transcriptional activity is less enhanced.25 It is

difficult to evaluate the real extent of these differences in affin-

ities, since the determination of Kdiss for ER/ERE complexes is

highly influenced by the technique used, and furthermore very

few determinations of Kdiss for ER� are documented, but we

can generally say that few base changes can lower the binding

affinity of complexes three times or more.25,27

Some structures of the DNA-binding domain (DBD) of ER�(DBD-ER�) complexed to different ERE27–29 are currently

available in the PDB Data Bank.30 Instead, no structures of the

DBD-ER� are currently available. Anyway, it is possible to

model it by means of computational biology procedures. In par-

ticular, comparative modeling (also called homology modeling)

methods are the first choice when a reference template is avail-

able.31 Last CASP6 competition showed that, although some

improvement should be required especially for loop modeling

and side chain conformation accuracy, most of the differences in

the superposition of C� between the modeled structure and the

template obtained by experimental techniques can be included in

a range of only 1–2 A when models share high sequence iden-

tity with their templates.31,32 This is the case of DBD-ER�,which shares about 94% sequence identity with DBD-ER�.21

The residues which differ among the two proteins are localized

mainly in their terminal portions, except Met42 in DBD-ER�(Ile42 in DBD-ER�) which is involved in a dimer contact medi-

ated by ordered water molecules,27 and then could potentially

1032 Marabotti, Colonna, and Facchiano • Vol. 28, No. 6 • Journal of Computational Chemistry

Journal of Computational Chemistry DOI 10.1002/jcc

influence dimer association and DNA binding affinity. Thus, a

model obtained by homology modeling methods, although less

accurate than a model obtained by X-ray crystallography at high

resolution, should be of quality suitable to analyze phenomena

such as this kind of macromolecular interaction.

We analyzed the binding of ER� and ER� to consensus ERE

sequence in a quantitative way by means of HINT, to investigate

which elements of both systems are involved in binding and are

responsible for affinity and selectivity. We also simulated the

binding of ER� and ER� to several mutant ERE sequences, in

which each couple of nucleotides in the two halves of the bind-

ing site was varied with the other possible couples, and we ana-

lyzed how the mutations in different ERE positions can affect

binding affinity in both complexes.

Methodology

The crystallographic structure of the human dimeric DBD-ER�complexed to the consensus ERE sequence: 50-CCAGGTCA-

CAGTGACCTG-30 (PDB file 1HCQ.pdb29) was used as tem-

plate to model the structure of the human dimeric DBD-ER� by

homology modeling methods. The sequences of the two DBD-

ER domains retrieved from UniProt database33 were aligned

with the program BLASTP,34 then this alignment was used as a

starting point for the molecular modeling procedure, using the

program MODELLER version 6.135 implemented in the software

package InsightII (version 2000.1, 2000; Accelrys Software,

USA). First, we used the single chain A of the template to

model five different monomers of DBD-ER�. We set 4.0 A as

the RMSD between the crystal structure of the templates and the

fully optimized models, and left other settings as default. The

best model was selected among all with the aid of the programs

PROCHECK36 and ProsaII.37 Then, the dimeric DBD-ER� was

assembled from two monomers, using as reference the dimeric

structure of the template, to keep the same relative orientation

of the two subunits, with the aid of InsightII tools. Finally, the

coordinates of DNA and of water oxygens were added to the

file, which was saved in PDB format.

The mutations were introduced in the two half sites

(AGGTCA and TGACCT) of the sequence of consensus ERE

present in the crystallographic file 1HCQ.pdb. Each base pair

was mutated one at a time in all the other three possible base

pairs with the aid of InsightII tool ‘‘Replace Nucleotides’’ in the

‘‘Biopolymer’’ menu, which automatically optimizes the confor-

mations of the new nucleotides in the double helix structure.

With the same methodology, we also mutated simultaneously

two symmetric base pairs in the two halves of the DNA consen-

sus sequence: G5 and C15 (for DNA sequence numbering see

Fig. 3). Finally, in the same way, we converted the consensus

ERE in two nonestrogen consensus sequences: the glucocorticoid

Figure 1. Example of the HINT output for calculation. In each line, a single atom–atom interaction

between the two molecules is quantified and classified.

1033Estrogen Receptor-DNA Interaction


responsive element (GRE), with the sequence 50-CCGGTACA-

CAGTGTTCTG-30,25 and the progesterone responsive element

(PRE) with the sequence 50-GAACAAACTGTTCTAGCT-30,22

to use them as negative controls for the binding of ER� and

ER�. When the introduction of a nucleotide (typically, a thy-

mine, with its methyl moiety) caused a steric hindrance that

interferes with protein residues, we removed the bad contact

using the program SCWRL3.038 to change the conformation of

the side chain(s) involved.

The program HINT (version 3.09S�; eduSoft LC) imple-

mented in the software package SYBYL (version 6.91; Tripos)

and the Web server DDNA39 were used and compared for their

ability to rank the affinity of all the complexes obtained.

Before calculations, all structures were carefully checked to

verify that correct atom and bond types were assigned. Then, for

DDNA analysis we uploaded the PDB files of all the complexes,

obtained as described earlier, to the Web server and gave all in-

dication required by the Web interface. The server gives as an

output the predicted binding affinity of the complex, expressed

in kcal/mol.

More steps were required before submitting the complexes to

HINT calculations. Hydrogen atoms were added to the PDB files

of the complexes using SYBYL tools. The orientation of hydro-

gen atoms added to water molecules was set with SYBYL tools

to optimize the presence of hydrogen bonds in the solvent. A

mild energy minimization was applied only to hydrogen atoms

to remove bad inter- or intramolecular steric contacts that are

not minimized by the automatic algorithms upon hydrogen atom

addition. To do this, the Powell algorithm was applied until a

final gradient of 0.5 kcal/mol/A was reached. After these proce-

dures, the files were suitable for HINT analysis.

Settings and options of HINT program were chosen accord-

ing to previous work.13–15 In details, to calculate the Log Po/w

of both protein and DNA, the ‘‘dictionary’’ option was used by

setting the solvent condition as ‘‘neutral’’, and the ‘‘essential’’

option was set to treat explicitly only the hydrogen atoms linked

to noncarbon atoms. Only water molecules directly involved in

protein–DNA interactions (called ‘‘bridging waters’’) were taken

into account for HINT calculations. These water molecules were

firstly selected automatically by HINT, setting a contact distance

of 4 A from the two macromolecules, and then their positions

were optimized allowing a translation for oxygen atoms of less

than 0.1 A.40 After that, they were further manually selected by

choosing only water molecules that were involved in H-bonds

simultaneously with protein and DNA, as indicated by the

SYBYL feature ‘‘Calculate H-Bonds’’.

After LogPo/w evaluation, the HINTSCORE was calculated

for interactions between protein and DNA, protein and bridging

water molecules, and DNA and bridging water molecules.

An example of the output of HINT calculations is shown in

Figure 1. According to the default parameters of HINT output, a

distance cut-off of 6 A was set to evaluate HINT scores between

the atoms, and a cut-off value of 10 was set to filter nonmea-

ningful interactions. The HINTSCORE values for each atom–

atom interaction were summed to obtain the values relative to

each amino acid-nucleotide interaction and the total HINT-

SCORE. From HINTSCORE values, it is possible to rank affin-

Figure 2. Alignment of the sequences of DBD of ER� (top) and ER� (bottom). The grey background

indicates conserved residues.

Figure 3. Structure of DBD of ER bound to the ERE sequence: 50-CCAGGTCACAGTGACCTG-30. A: Structure of the complex be-

tween DBD-ER�, obtained by homology modeling procedures, and

the consensus ERE element. B: Crystallographic structure of the

complex between DBD-ER� and the consensus ERE element. Cyl-

inders indicate �-helices, arrows indicate �-strands, and big spheres

indicate Zn ions. DNA and bridging water molecules are represented

in ball and stick mode. The numbering of the consensus ERE

sequence is shown at the bottom of the figure.



ities between different complexes: the bigger the HINTSCORE,

the higher the affinity between the interacting species.

Results

Because of their high sequence identity, the best alignment

of the amino acid sequences of DBD-ER� and DBD-ER�is very trivial to obtain, with no gaps and no ambiguity to

solve. As a consequence, the result obtained with BLASTP

(Fig. 2) was used for the subsequent procedures without further

processing.

The modeled structure of ERE/DBD-ER� is shown in Figure

3A. As expected by the high percentage of sequence identity, no

major differences are present with respect to the structure of

ERE/DBD-ER� (Fig. 3B). The two backbones are very well

superimposable (RMSD: 0.61 A) and all the secondary structures

of the template are conserved in the model.

Table 1. HINTSCORE Values Calculated on Different ER/ERE Complexes.

ERE

DBD-ER� DBD-ER�

HINTSCORE

protein–DNA

HINTSCORE

protein–DNA þ H2O

DDNA output

(kcal/mol)

HINTSCORE

protein–DNA

HINTSCORE

protein–DNA þ H2O

DDNA output

(kcal/mol)

Consensus 12676 20874 �8.28 8857 17793 �8.05

A3C 12704 21064 �8.29 8828 17852 �8.11

A3G 12838 21308 �8.29 8876 17928 �8.10

A3T 12632 20864 �8.31 8769 16718 �8.08

G4A 12473 21520 �8.20 8691 17286 �7.97

G4C 12546 20865 �8.28 8657 17429 �8.05

G4T 12324 20854 �8.25 8610 17117 �7.99

G5A 10715 18681 �8.21 4778 13453 �7.99

G5C 11876 19970 �8.28 7812 16603 �8.02

G5T 11250 18624 �8.24 8233 16145 �7.97

T6A 11955 20013 �8.31 8195 16932 �8.06

T6C 12549 21232 �8.31 8772 18190 �8.10

T6G 12961 21781 �8.32 9023 18540 �8.11

C7A 12085 19025 �8.25 8269 16831 �8.02

C7G 12531 20858 �8.29 8100 17465 �8.06

C7T 12669 20759 �8.27 8427 16946 �8.02

A8C 12911 21573 �8.27 9099 18158 �8.04

A8G 12873 21460 �8.26 9012 18121 �8.04

A8T 12911 21526 �8.25 9081 18199 �8.02

T12A 12857 21496 �8.25 9086 16724 �8.04

T12C 12832 21368 �8.27 9011 18079 �8.04

T12G 12943 21661 �8.27 9103 18179 �8.04

G13A 12519 20686 �8.28 8406 17144 �8.02

G13C 12350 20528 �8.28 8199 16928 �8.06

G13T 12355 18383 �8.25 7386 13838 �8.04

A14C 12847 21638 �8.34 9009 18611 �8.11

A14G 12628 21101 �8.33 8679 18010 �8.11

A14T 11655 19169 �8.31 6386 14574 �8.06

C15A 12110 20195 �8.22 8024 15736 �7.98

C15G 12024 20369 �8.27 7692 16525 �8.05

C15T 11825 19411 �8.21 6803 14770 �7.98

C16A 12280 21013 �8.23 8630 17149 �7.99

C1G 12285 23915 �8.25 8590 17387 �8.05

C16T 12440 20811 �8.23 8603 17201 �7.98

T17A 12640 20378 �8.29 8698 16484 �8.08

T17C 12824 21324 �8.31 8856 17670 �8.08

T17G 12734 20598 �8.32 8833 17682 �8.09

G5AþC15T 9331 17077 �8.17 3105 11083 �7.93

G5CþC15G 11221 19262 �8.27 6650 14879 �8.02

G5TþC15A 10616 17731 �8.18 7612 15000 �7.90

GRE 7999 13568 �8.24 �602 6286 �7.97

PRE 6588 10756 �8.14 2814 11840 �8.21

HINTSCORES values were calculated with or without including the contribution of ‘‘bridging waters’’ i.e. waters that interact simultane-

ously with the protein and DNA. For DDNA analysis, water molecules were included in the PDB files.



Table 2. Protein–DNA Interactions Identified and Quantified by HINT in Consensus ERE/DBD-

ER� or ERE/DBD-ER� Complexes

Consensus ERE/DBD-ER� Consensus ERE/DBD-ER�

Amino acid/DNA

base interaction HINTSCORE

Amino acid/DNA


ALA29-A14 �46 ALA29-A14 �37

ALA29-A32 �49 ALA29-A32 �45

ALA29-G13 7 ALA29-G13 12

ALA29-G23 �16 ALA29-G31 25

ALA29-G31 15 ALA29-G5 �12

ALA29-G5 �15 ALA29-T24 �10

ALA29-T24 �11 ALA29-T6 �10

ALA29-T6 �13 ARG33-A32 �11

ARG33-G13 285 ARG33-G11 �14

ARG33-G31 287 ARG33-G13 699

ARG33-T12 811 ARG33-G31 763

ARG33-T30 892 ARG33-T12 276

ARG56-A14 94 ARG33-T24 19

ARG56-A32 102 ARG33-T30 186

ARG56-G13 862 ARG33-T6 39

ARG56-G31 1220 ARG56-G13 184

ARG56-T12 �31 ARG56-G31 160

ARG56-T30 �29 ARG56-T12 �24ARG63-G13 1200 ARG56-T30 �25

ARG63-G31 1179 ARG63-G13 1184

ARG63-T12 147 ARG63-G31 1161ARG63-T30 164 ARG63-T12 61

ASP12-A14 �145 ARG63-T30 53

ASP12-A32 �108 CYS24-A14 22

GLN36-G23 �42 CYS24-A32 20GLN60-G11 42 GLN36-G23 203

GLN60-G29 51 GLN36-G5 314

GLN60-T12 710 GLN60-G11 42

GLN60-T30 834 GLN60-G29 39GLU25-A14 �17 GLN60-T12 209

GLU25-A32 �32 GLN60-T30 267

GLU25-C15 507 GLU25-A14 13

GLU25-C16 58 GLU25-A32 �94

GLU25-C33 713 GLU25-C15 801

GLU25-C34 71 GLU25-C16 66

GLU25-G13 12 GLU25-C33 630

GLU25-G22 �120 GLU25-C34 55

GLU25-G23 �64 GLU25-G13 �2

GLU25-G31 11 GLU25-G22 �103

GLU25-G4 �61 GLU25-G23 �87

GLU25-G5 �59 GLU25-G31 10

GLY16-C2 �245 GLU25-G4 �30

GLY16-C20 �262 GLU25-G5 �31

GLY26-A14 �13 GLY16-C2 �151GLY26-A32 �4 GLY16-C20 �268

GLY26-G13 �196 GLY26-A14 �15

GLY26-G31 �163 GLY26-A32 �6HIS18-A21 146 GLY26-G13 �217

HIS18-A3 197 GLY26-G31 �192

HIS18-C2 37 HIS18-A21 450

HIS18-C20 34 HIS18-A3 402ILE35-G22 �50 HIS18-C2 49

ILE35-G4 �60 HIS18-C20 33

LYS28-A21 40 ILE35-G22 �93

(continued)



Both complexes were submitted to calculations to rank their

binding energies (Table1). From both DDNA server and HINT

outputs it appears that the affinity of ER� for the consensus ERE

is higher than that of ER�. This result is in line with experimental

data25 and may be caused by the differences in amino acid

sequence that, although limited, could affect the binding of the re-

ceptor to the DNA. However, we are aware that this may also be

a result influenced by the fact that ERE/DBD-ER� is a crystallo-

graphic structure; whereas ERE/DBD-ER� is obtained by homol-

ogy modeling procedures and therefore its refinement could not

be comparable to that of the experimental structure.

In addition, HINT calculation allowed us to identify several

protein–DNA contacts in both complexes and to discriminate

between specific and aspecific ones (Table 2). Five amino acids

(Gly25, Lys28, Ala29, Lys32, and Arg33) are mainly involved

in specific interactions with the purine or pyrimidine rings,

Table 2. (Continued)

Consensus ERE/DBD-ER� Consensus ERE/DBD-ER�

Amino acid/DNA


Amino acid/DNA


LYS28-C15 �67 ILE35-G4 �224

LYS28-C16 �14 LYS28-A21 29

LYS28-C33 �143 LYS28-A3 12

LYS28-C34 �38 LYS28-C15 �48

LYS28-G22 441 LYS28-C33 �48

LYS28-G23 89 LYS28-C34 �21

LYS28-G4 241 LYS28-G22 211

LYS28-G5 79 LYS28-G23 35

LYS32-A14 �25 LYS28-G4 73

LYS32-A32 �43 LYS28-G5 11

LYS32-G13 21 LYS32-A14 �12

LYS32-G23 165 LYS32-A32 �15

LYS32-G31 20 LYS32-G22 104

LYS32-G5 264 LYS32-G23 317

LYS32-T24 84 LYS32-G4 147

LYS32-T6 100 LYS32-G5 461

LYS53-A14 42 LYS32-T24 27

LYS53-G13 �48 LYS32-T6 39

LYS53-G31 �23 LYS53-G13 �25

LYS57-G13 375 LYS53-G31 �23LYS57-G31 728 LYS57-G13 �20

LYS57-T12 �93 LYS57-G31 �2

LYS57-T30 �79 LYS57-T12 �60

PHE30-G31 �11 LYS57-T30 �88PHE30-T12 66 PHE30-G31 �11

PHE30-T30 112 PHE30-T12 161

SER15-C2 �115 PHE30-T30 143

SER15-C20 �144 SER15-C2 �57TRP22-C20 11 SER15-C20 �108

TYR17-A21 �295 TRP22-C20 45

TYR17-A3 �247 TYR17-A21 �490TYR17-C2 �361 TYR17-A3 �263

TYR17-C20 �224 TYR17-C2 �327

TYR19-A21 7 TYR17-C20 �250

TYR19-A3 35 TYR19-A21 �4TYR19-G22 805 TYR19-A3 61

TYR19-G4 967 TYR19-G22 534

TYR41-G11 13 TYR19-G4 451

TYR41-T12 27 TYR41-T12 27TYR41-T30 79 TYR41-T30 24

The HINTSCORE presented here arise from the sum of atom-atom interaction hydropathic term

bij (see text) for each amino acid/DNA base interaction. Specific interactions, i.e. that involve

only the purinic or pyrimidinic rings, are highlighted in bold, whereas aspecific interactions, i.e.

that involve only the ribose and the phosphate scaffold of the DNA, are highlighted in italic.

Interactions in normal characters are formed both by specific and aspecific components.



whereas other residues, in particular His18, Tyr19, Arg56,

Lys57, Gln60, and Arg63 interact mainly with phosphate and

ribose. In both complexes, the nucleotides involved in specific

interactions are all comprised in the two half sites of the palin-

dromic ERE sequence. Nevertheless, it is interesting to note that

not all nucleotides of the consensus half sites seem to be in con-

tact with the receptors (in particular, neither C7, A8, and T35,

nor, symmetrically, C25, A26, and T17). Instead, G4, G5, G31,

and A32 (and, symmetrically, G22, G23, G13, and A14) interact

simultaneously with several residues. Figure 4 shows the side

chain positions of residues interacting specifically in the two

complexes. This analysis is in agreement with deductions made

on the basis of the crystallographic data.27,29

To gain further insights on the importance of each nucleotide

for protein–DNA interaction, we evaluated the impact of base

pairs mutations on the affinity, in the two half sites of the ERE

sequence. In this set of data, we also included the complexes

between ER� or ER� and two nonestrogen responsive elements,

used as negative controls: the GRE and the PRE. Again, this

evaluation was performed using DDNA and HINT.

Results reported in Table 1 show for DDNA a predicted bind-

ing affinity very similar for all complexes, ranging from �8.34 to

�8.14 kcal/mol. It is very hard to find significant differences

between the various mutations, although the predicted binding af-

finity is slightly worse in ER� and ER� complexes for mutations

affecting positions G4, G5, C15, and C16 (less than 0.1 kcal/mol

higher than the consensus ERE sequence) and for double muta-

tions G5A þ C15T and G5T þ C15A (about 0.1 kcal/mol higher).

DDNA seems also unable to find marked differences between

estrogen and nonestrogen responsive sequences. In fact, the pre-

dicted binding affinity for GRE is comparable to that of all other

complexes, and that for PRE sequence is only 0.15 kcal/mol

higher with respect to consensus ERE sequence.

On the contrary, HINT discriminates with higher sensitivity

the affinity between ER� or ER� and ERE sequences. In partic-

ular, mutations of G5, T6, A14, and C15 clearly lower the

HINTSCORE values of both receptor isotypes for ERE (espe-

cially for ERE/DBD-ER� complex), whereas mutations of A3,

G4, C7, A8, T12, C16, and A17 with any nucleotide have mini-

mal effects on the HINTSCORE.

In both complexes, the single mutation of G5 with A is the

one with the worst effect on the total HINTSCORE. This con-

firms the hypothesis that identifies G5 as the most important nu-

cleotide to drive the binding energy of the complex.27,29,41 Ana-

lyzing the HINTSCORE at atomic level in the ERE/DBD-ER�complex, we found that the negative effects of this mutation are

especially conditioned by the bad interaction between Glu25 and

the methyl group of the thymine (partial HINTSCORE ¼�1394), whereas the aminic group of C33 in the consensus ERE

sequence was able to make a strong H-bond with the carboxy-

late moiety of the acid (partial HINTSCORE ¼ þ713). Another

unfavorable effect derives from the proximity of the aminic

group of A5 to the positively charged moiety of Lys32 (partial

HINTSCORE ¼ þ23), whereas G5 in the consensus ERE

sequence was able to contact more favorably the same amino

acid with its carbonilic oxygens, that are partially negatively

charged (partial HINTSCORE ¼ þ264). Instead, the negative

interaction of Lys28 with the aminic moiety of C33 in the con-

sensus sequence (partial HINTSCORE ¼ �143) is replaced by

the favorable interaction of the positively charged moiety of the

amino acid with the carbonilic oxygen of T33 (partial HINT-

SCORE ¼ þ115). The effects of the G5A mutation are even

Figure 4. Close-up of the interactions between ER� (panel A) and ER� (panel B) with ERE. Only

protein residues interacting specifically with DNA bases are shown and represented in ball and stick

mode, whereas the backbone of the protein is represented as a ribbon. DNA is represented in stick

mode. The color code is: carbon green, oxygen red, nitrogen blue, phosphorus magenta. The Zn ions

are represented as violet big spheres.



more pronounced on ERE/DBD-ER� complex. Again, the inter-

actions that are responsible for HINTSCORE variation are those

between Glu25 and T33 (partial HINTSCORE from þ630 in

consensus ERE/DBD-ER� complex to �623 in mutant ERE/

DBD-ER� complex) and between Lys32 and A5 (partial HINT-

SCORE from þ460 in consensus ERE/DBD-ER� complex to

þ183 in mutant ERE/DBD-ER� complex). Moreover, these neg-

ative effects are not significantly counterbalanced by the reversal

of the negative interaction between Lys28 and C33 in a positive

one, as in ER�-ERE complex (partial HINTSCORE from �48

in consensus ERE/DBD-ER� complex to þ40 in mutant ERE/

DBD-ER� complex).

In the case of symmetrically mutated base pairs G5 and C15

in the two halves of the DNA consensus sequence, results (Table

1) show that this double mutation affects significantly the HINT-

SCORE of the complexes, especially when A and T are intro-

duced, respectively, to replace G5 and C15. The effects of dou-

ble mutations seem to be additive, and thus each mutation con-

curs independently to cause a global negative interaction.

The resulting HINTSCORE of the complexes between ER�or ER� and the negative controls GRE and PRE clearly show

that HINT is able to discriminate between specific and non spe-

cific interactions. In fact, the HINTSCORE values obtained for

these two elements are significantly lower than those for the spe-

cific ERE elements, in both complexes (in the case of ER�-GREcomplex, the HINTSCORE value is even negative when bridg-

ing water is not included in calculations). The variations involve

especially specific interactions, in particular those between DNA

and Glu25, Lys28, Lys32, and, only for PRE, Ala29 and Arg33.

Looking at the scores, it is possible to see that in both com-

plexes the most important variation affects the interaction

between Glu25 and DNA. This is due to the fact that both in

PRE and in GRE sequences there are more thymine nucleotides

than in the ERE sequences (see Material and Methods section),

and as a consequence the methyl moiety of this nucleotide has a

strongly negative interaction with the charged moiety of the

amino acid (the partial HINTSCORE for Glu25-DNA interac-

tions in ER� goes from þ1019 for the ERE complex to �2941

for the PRE complex, to �2534 for the GRE complex; in ER�the HINTSCORE goes from �2125 for the ERE complex to

�5038 for the PRE complex, to �5793 for the GRE complex).

Another interaction which converts its HINTSCORE from posi-

tive in ERE to negative in GRE and PRE is Lys32-DNA (partial

HINTSCORE for Lys32-DNA in ER�/ERE is þ587, in ER�/PRE is �126, in ER�/GRE is �751; in ER�/ERE is þ1012, in

ER�/PRE is þ2, in ER�/GRE is �1562). Again, the main con-

tributors for this negative effect are thymine residues, which

lower the HINTSCORE because the introduction of a hydropho-

bic moiety near a charged residue is accounted as unfavorable

by HINT and thus marked with a negative sign. Some interac-

tions appear to be unchanged for a complex, but not for the

other: for example, the interaction Arg33-DNA in ER�/ERE and

ER�/GRE complexes is very similar (HINTSCORE: þ2276 and

þ2265, respectively), while in ER�/PRE complex the HINT-

SCORE is lower (HINTSCORE: þ1470). Obviously, this

depends from the different sequences of the responsive elements

(the main responsible for this low HINTSCORE in ER�/PREcomplex is T31, which is G31 in GRE sequence).

The number of bridging waters varies from 10 to 12 among

all complexes. When the mutations C7A and C13T were intro-

duced, an extra water molecule was deleted since the methyl

group of thymine was in narrow contact with crystallographic

water. As a consequence, a lower contribution of water mole-

cules to the total HINTSCORE is present in these cases. Look-

ing at the results, it is possible to conclude that the contribution

of bridging water molecules accounts 8000–9000 score points to

the total HINTSCORE among all analyzed complexes (Table 1).

Therefore, for this particular series of complexes, the specificity

of the recognition for consensus or nonconsensus sequence is

apparently not greatly affected by bridging water molecules, in-

stead they strongly concur to increase the strength of the global

interaction. This is confirmed indirectly by the observation that

in another crystallographic structure of DBD-ER� complexed to

a nonconsensus ERE sequence (vitellogenin gene B2 from Xen-

opus) the eight ordered water molecules found in the consensus

complex are essentially unperturbed by the sequence change.27

However, this statement should not be generalized, since studies

with HINT on several other complexes show that the contribu-

tion of ordered water molecules can greatly affect the specificity

of an interaction.15

Discussion

Protein–DNA interactions are very important for the life, and

generally are tightly regulated to ensure a perfect coupling and

recognition between the two macromolecules. Generally, the rec-

ognition of a target sequence on DNA is necessary for the inter-

action. In particular, steroid hormone receptors are known to

interact with DNA sequences present near the gene promoters,

called ‘‘hormone responsive elements’’. They belong to several

classes, and each hormone receptor recognizes a specific

sequence; this justifies at least part of the selectivity of the hor-

mone response.42 These responsive elements are generally made

of two half sites of six base pairs arranged in a palindromic

way, separated by some intervening base pairs. In general, con-

sensus sequences for different receptors differ for no more than

two base pairs in the palindromic half sites, or by half site spac-

ing and orientation.29 Therefore, the discrimination of the con-

sensus sequences by the nuclear receptors must be achieved on

the basis of very few elements. Crystallographic studies on sev-

eral steroid receptors coupled to consensus or nonconsensus

sequences27,29,43,44 showed that only few residues of the protein

structure are involved in specific contacts with DNA, and that

hydrogen bonds are the main contributors for binding energies.

However, it is reported that a single base pair substitution can

result even in 10-fold increase of the dissociation constant of a

complex,27 corresponding roughly to a DG difference of at least

1 kcal/mol in standard conditions. Since accurate prediction of

protein–DNA interactions is essential to understand cellular

processes and can help us to find new targets for the treatment

of many diseases, programs must be able to accurately rank the

energetics of protein–DNA association, to discriminate between

different complexes.

In our work, we tested the ability of HINT to rank the affin-

ity of ER� and ER� for different ERE sequences that differ



from each other only for one or few base pairs. Previous HINT

analyses conducted on complexes formed by DNA and small

molecules intercalating the double helix have shown the ability

of this ‘‘natural force field’’ to analyze the free energy of bind-

ing and sequence selectivity of both known and designed ana-

logues of doxorubicin45,46; our current study further extends the

field for HINT predictions.

Results demonstrate that this program is able to find differen-

ces in the interactions between the different complexes. The dif-

ferences are more evident when the receptors are bound to non-

estrogen responsive elements, i.e. GRE and PRE, but significant

differences in HINTSCORE values were also detected for single

base pair changes. This is especially true for mutation G5A,

affecting the third base pair of the half site, which has been

experimentally recognized as the principal responsible for bind-

ing energy.27,29,41 Other mutations that appear to cause a marked

decrease in binding affinity are G5C, G5T, T6A, C7A, A14T,

C15A, C15G, and C15T. Multiple base changes that occur

simultaneously in both half sites seem to have an additive, but

independent, negative effect on binding energy.

Furthermore, with HINT we are able to identify not only the

residues involved in the interaction, but even the atoms that

influence this interaction, and to quantify their contribution

(see Fig. 1), thus explaining the binding phenomena to an

atomic level.

Past studies found a correlation between the calculated

HINTSCORE values and the experimentally determined binding

free energies for protein–ligand complexes (*513 HINT score

units for 1 kcal/mol).13,16 By applying this correlation to our

results to find a rough estimation of DG, we found that the dif-

ferences in affinity would account for even 3–4 kcal/mol for

some nonconsensus sequences, and the complexes formed with

GRE and PRE would account from 9 to 20 kcal/mol less than

consensus ERE sequence. Although these values appear high,

several experimental results proved that the change of one or

few base pairs in ERE elements can affect the binding affinity

of several times.25,27 A parallel work is currently in progress to

determine the exact correlation coefficient between the calcu-

lated HINTSCORE values and the experimentally determined

binding free energies in protein–DNA complexes47 and the

application of this new relationship would result in a more pre-

cise estimation of the DG by HINT.

We compared the HINT results with those obtained by means

of a tool representative of the state-of-the-art of binding affinity

predictors, and we chose DDNA because it appears reliable and

also easy-to-use, since it is available for scientific community

through a Web server.39 This program is based on a knowledge-

based statistical potential, DFIRE48 which was derived from a

distance-scaled, finite, ideal gas reference state and which seems

to be less dependent from the structural database used as train-

ing set.49 This database is composed by high resolution (<3.0

A) structures of proteins of 70 different types complexed with

small organic noncovalently binding ligands (MW < 1000),

whose protein–ligand binding affinities vary over 10 orders of

magnitude. The results of the DFIRE energy function on pro-

tein–ligand complexes were compared to the published results of

12 other scoring functions generated from either physical-based,

knowledge-based, or empirical methods, resulting in the best

correlation coefficient between theoretically predicted and exper-

imentally measured binding affinities for 100 protein–ligand

complexes.7 The authors tested DDNA on 45 protein–DNA

complexes with known 3D structures and found a correlation

coefficient of 0.83 between experimental and theoretical binding

energies.7 Surprisingly, DDNA failed to detect significant differ-

ences in affinity among our complexes. In fact, the differences

between the predicted binding affinities are no greater than 0.15

kcal/mol, even when ERE is replaced by GRE or PRE. Prob-

ably, since the reference database is made of protein–ligand

complexes, some features for protein–DNA interactions could

not be derived in the energy function, and therefore DDNA is

not able to take into account small differences in the DNA struc-

ture. However, not even the HINT force field was derived spe-

cifically from the analysis of DNA structures, and this fact con-

firms the general validity of the hydropathic approach.

A last annotation concerns the importance of water molecules

in protein–DNA interaction. In the current series of data, the

contribution of ordered water molecules is practically equivalent

in all cases, with few exceptions when the steric hindrance of

the methyl moiety of thymine competes with crystallographic

waters. Therefore, in the currently analyzed series of complexes,

this contribution seems not to be relevant to discriminate the

binding specificity of ER� and ER� to different ERE. We would

like to point out that this observation is related only to these

systems, since studies with HINT on several other complexes

show that the contribution of ordered water molecules can

greatly affect the specificity of an interaction.15 On the contrary,

the high HINTSCORE values associated to the contributions of

bridging waters clearly confirms that the thermodynamics of

DNA–protein recognition is water-dependent, with both enthalpic

and entropic contributions, as it was previously reviewed for

ligand–protein and protein–protein associations.16

In conclusion, computational techniques allowed us to inves-

tigate at a molecular level how a mutation in the ERE sequence

can influence the affinity of both DBD-ER� and DBD-ER�.This study can increase the knowledge about estrogen receptor/

DNA interactions, and could also open the possibility to perform

a high throughput analysis on putative ERE elements found in

estrogen regulated genes. In fact, the ability of HINT to detect

different affinities in complexes between ER and very similar

ERE sequences could be applied to predict if putative ERE

sequences are really able to bind ER. Alternatively, since muta-

tions present in known ERE sequences alter ER binding, the

analysis with HINT could help in providing a structural explana-

tion at molecular level to possible functional alterations of the

complex. It is also possible that mutations in ERE sequences

could act as ‘‘modulators’’ of the affinity between the receptor

and DNA, thus allowing a higher or lower transcription of the

regulated genes. An analysis with HINT could provide sugges-

tions for the introduction of appropriate mutations for a better

control of transcriptional levels for the expression of genes that

are under the control of these regulatory elements, with a signifi-

cant interest not only for the biomedical field, but also for bio-

technological applications. Finally, HINT analysis could be ex-

tended to rank binding affinity for different categories of recep-

tors, for which a consensus sequence is not currently known,

with the aim of predicting new responsive elements.



Acknowledgment

The authors are indebted to Prof. Glen E. Kellogg (Virginia

Commonwealth University, USA, and eduSoft LC, USA) and

Prof. Andrea Mozzarelli (University of Parma, Italy) for provid-

ing the software HINT and for continue support.

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New computational strategy to analyze the interactions of ERα and ERβ with different ERE sequences

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Transcript of New computational strategy to analyze the interactions of ERα and ERβ with different ERE sequences