MI-1

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS

Dopamine transporter comparative molecularmodeling and binding site prediction using theLeuTAa leucine transporter as a templateMartın Indarte,1* Jeffry D. Madura,2* and Christopher K. Surratt1*

1Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, Pennsylvania 15282

2Department of Chemistry and Biochemistry, Center for Computational Sciences, Duquesne University, Pittsburgh,

Pennsylvania 15282

INTRODUCTION

Addiction to cocaine, methamphetamine, and related psychostimu-

lants destroys millions of individuals, families, and careers, a societal

scourge worldwide. Although, addiction to heroin, oxycodone, fen-

tanyl, and other opiates can be effectively treated with buprenorphine

and to some extent methadone, no such medications are available to

combat psychostimulant addiction despite decades of research. Not

coincidentally, opioid receptor structure and mechanism of action are

much better understood than those of the brain receptors for psy-

chostimulant drugs of abuse, the monoamine neurotransmitter trans-

porter proteins. Pharmacologic and behavioral studies indicate that

the dopamine transporter (DAT) protein is the principal binding site

responsible for cocaine’s reward and reinforcement properties.1,2 The

plasma membrane-bound DAT protein quenches dopamine-mediated

neurotransmission by clearing the neurotransmitter from the synaptic

cleft following Ca21-mediated exocytosis from presynaptic vesicles.

Cocaine, a DAT inhibitor, blocks synaptic uptake of dopamine; the

resultant accumulation of the neurotransmitter in the synapse leads

to an increase in postsynaptic dopamine D2 and D3 receptor activa-

tion in the nucleus accumbens and other brain regions associated

with addiction. Indeed, activation of these accumbal dopamine recep-

tors has been linked with the reinforcing properties of the drug.3,4

Amphetamine also increases synaptic dopamine levels, but by media-

ting dopamine efflux from the presynaptic cell via the DAT.5,6 Logi-

cally, an agent that blocks cocaine and amphetamine binding at the

DAT without substantially interfering with dopamine uptake should

serve as an effective antiaddiction therapeutic. High resolution eluci-

dation of the DAT structure, especially regarding its substrate and in-

hibitor recognition sites, is thus critical.

The Supplementary Material referred to in this article can be found online at http://www.

interscience.wiley.com/jpages/0887-3585/suppmat.

Grant sponsor: NIDA; Grant number: DA016604; Grant sponsor: Samuel and Emma Winters;

Grant sponsor: DOE; Grant numbers: P116Z040100, P116Z050331.

*Correspondence to: Dr. Christopher K. Surratt, Division of Pharmaceutical Sciences, Duquesne

University, Mellon Hall, Room 453, 600 Forbes Avenue, Pittsburgh, PA 15282.

E-mail: [email protected] or Martin Indarte, E-mail: [email protected] or Jeffry D. Madura,

E-mail: [email protected]

Received 11 September 2006; Revised 16 March 2007; Accepted 16 April 2007

Published online 10 September 2007 in Wiley InterScience (www.interscience.wiley.com).

DOI: 10.1002/prot.21598

ABSTRACT

Pharmacological and behavioral studies indicate

that binding of cocaine and the amphetamines by

the dopamine transporter (DAT) protein is prin-

cipally responsible for initiating the euphoria and

addiction associated with these drugs. The lack of

an X-ray crystal structure for the DAT or any

other member of the neurotransmitter:sodium

symporter (NSS) family has hindered understand-

ing of psychostimulant recognition at the atomic

level; structural information has been obtained

largely from mutagenesis and biophysical studies.

The recent publication of a crystal structure for

the bacterial leucine transporter LeuTAa , a dis-

tantly related NSS family homolog, provides for

the first time a template for three-dimensional

comparative modeling of NSS proteins. A novel

computational modeling approach using the

capabilities of the Molecular Operating Environ-

ment program MOE 2005.06 in conjunction with

other comparative modeling servers generated the

LeuTAa-directed DAT model. Probable dopamine

and amphetamine binding sites were identified

within the DAT model using multiple docking

approaches. Binding sites for the substrate

ligands (dopamine and amphetamine) overlapped

substantially with the analogous region of the

LeuTAa crystal structure for the substrate leucine.

The docking predictions implicated DAT side

chains known to be critical for high affinity

ligand binding and suggest novel mutagenesis tar-

gets in elucidating discrete substrate and inhibi-

tor binding sites. The DAT model may guide DAT

ligand QSAR studies, and rational design of novel

DAT-binding therapeutics.

Proteins 2008; 70:1033–1046.VVC 2007 Wiley-Liss, Inc.

Key words: homology; comparative modeling; dock-

ing; drug; pharmacophore; medication; therapeutic;

cocaine; psychostimulant; antagonist; addiction.

VVC 2007 WILEY-LISS, INC. PROTEINS 1033

The DAT and other plasma membrane monoamine

transporters are members of the 12 transmembrane do-

main (TM) neurotransmitter:sodium symporter (NSS)

family,7 in which electrogenic transport of a neurotrans-

mitter substrate across the cell membrane is driven by a

Na1/K1-ATPase-generated Na1 gradient. Cotransport of

Cl2 is also required for the dopamine, norepinephrine,

and serotonin transporter proteins (DAT, NET, and

SERT, respectively); the SERT additionally transports K1,

but in antiport fashion.8 Aligning the amino acid

sequences of the NSS family members guided delineation

of monoamine transporter TM domain borders and

other aspects of transporter secondary structure.9 Such a

sequence alignment can also yield clues as to which NSS

residues probably contribute to the general protein infra-

structure, which residues could play a role in substrate or

ion recognition, and which residues are most likely to be

responsible for a pharmacologic pattern unique to a

given transporter. This sequence information alone

spawned hundreds of NSS site-directed and chimeric

mutants.10 The substituted cysteine accessibility muta-

genesis (SCAM) methodology has especially contributed

to defining monoamine transporter ligand binding cav-

ities, substrate/ion pores, general TM domain infrastruc-

ture, and even detection of substrate- or inhibitor-

induced conformational changes.11–13 Nevertheless, this

approach only circumstantially implicates a given residue

or protein region as a component of the binding pocket.

Unequivocally identifying direct contacts between trans-

porter protein and ligand has proven to be difficult; the

lack of an X-ray crystal structure for any protein homo-

logous to the NSS family has been the major impediment.

Encouragingly, the recently published crystal structure of

a bacterial leucine transporter (LeuTAa), a protein homo-

logous with the NSS family,14 finally provides a suitable

NSS template. Using LeuTAa as a template, the present

study describes a novel modeling approach that employs

comparative modeling to produce a feasible three-dimen-

sional (3D) DAT structure.

Three approaches may be employed in predicting a 3D

macromolecular structure: ab initio prediction, ‘‘fold’’

recognition, and comparative (homology) modeling.15

These differ principally in the sequence and structural

database information used. A true ab initio method bases

structure prediction entirely on the physical and chemical

information contained in the primary amino acid

sequence. However, the term is also used when short ex-

perimental protein sequences and secondary structure

prediction methods are incorporated.16–20 Fold recogni-

tion, or ‘‘threading,’’ relies heavily on the structural simi-

larities between certain distantly related or unrelated pro-

teins. Comparative modeling predicts the 3D structure of

a target protein based primarily on its alignment with

one or more template proteins of known structure.21 For

proteins that share greater than 40% amino acid

sequence identity, comparative modeling is straightfor-

ward and typically accurate.22 For proteins with less

than 30% amino acid sequence identity (e.g., LeuTAa and

the DAT), comparative modeling becomes more challeng-

ing. Still, the rhodopsin crystal structure has successfully

guided the creation of useful comparative models for

many other members of the G protein coupled receptor

superfamily despite the absence of appreciable amino

acid sequence identity.23

Upon obtaining a 3D protein model, the conforma-

tions and orientations (denoted as poses) of ligands that

couple with the macromolecule are computationally

determined (‘‘docking’’).24–27 To find the most energeti-

cally favorable ligand pose within a structurally deter-

mined receptor, the macromolecule is typically held rigid

whereas the ligands are flexible and mobile.28 Here, a

docking procedure similar to the earlier uses of DOCK29

was used to identify potential DAT binding sites. This

approach should reveal DAT amino acid residues likely to

participate in substrate and inhibitor recognition and

thus define targets for mutagenesis and other structure-

function studies. In this way, it is hoped that a blueprint

can be developed for rational design of DAT-binding

therapeutics.

MATERIALS AND METHODS

Comparative modeling

Robetta server sequence alignment and model building

The comparative modeling module of the Robetta

server aligns the target and the template using K*Sync,

a more accurate method than PSI-BLAST or Pcons2.30

K*Sync estimates the most reliable alignment of target

and template based on secondary structure information,

residue information obtained by comparing statistical

representations of protein families (‘‘profile–profile’’

comparisons), and information from multiple structural

alignments of regions with high structural propensity to

fold. The peptide backbone is constructed taking into

account the geometry between template(s) and target via

multiple independent simulations; the lowest energy

models are selected. Side chains of these models are

repacked and conformational space explored using �100

independent Monte Carlo simulations, with a backbone

dependent side chain rotamer library and a full atom

energy function to select the lowest energy conformation

of the comparative model.31–33

The FASTA sequence of the rat DAT protein (SwissProt

locus SC6A3_RAT; accession number P23977; NCBI

accession number AAB21099)34 was utilized as the query

for the hybrid template-based/de novo method of the

Robetta server (http://robetta.bakerlab.org). The bacterial

(Aquifex aeolicus) leucine transporter protein LeuTAa was

employed as the template (PDB, www.rcsb.org, accession

number 2A65; MMDB accession no. 34395). Five models

M. Indarte et al.

1034 PROTEINS DOI 10.1002/prot

were retrieved from the server and separately saved in a

database using the Molecular Operating Environment

(MOE) 2005.06 program (Chemical Computing Group,

Montreal, Canada).35 The all-atom forcefield AMBER99

was used to add hydrogen atoms and assign partial

charges to all models.36 Relaxation of the newly added

hydrogen atoms via several cycles of energy minimization

were performed using a conjugated gradient/truncated

Newton optimization algorithm to convergence criteria

of 0.05 kcal/mol and a dielectric constant (e) of 3. All

nonhydrogen atoms were held fixed during the energy

minimization. Pro_check (MOE version), a scientific vec-

tor language (SVL) code based on Ramachandran plots

and custom-written by the Chemical Computing Group,

was used to detect unfavorable van der Waals contacts

and abnormal covalent bonds in the models. The few

steric clashes found were relaxed by manually selecting

backbone and side chain atoms of the implicated amino

acids and by performing successive steps of energy mini-

mization until the steric clash was removed. All steric

clashes were far from the putative ligand binding sites. A

final refinement of side chains was carried out utilizing

AMBER99 (convergence criteria 5 0.1 kcal/mol, e 5 3).

Backbone atoms were held fixed during the procedure to

find local minima for the side chains of the DAT macro-

molecule.

The final DAT model (herein referred to as Model 1)

was selected using the following criteria: (1) Maximal

spatial overlap of backbones between the DAT models

(targets) and LeuTAa (template). (2) Similarity of Verify

3D scores between target and template models with

respect to TM domains.37,38 (3) Optimal profile of

atom contacts and fewest abnormal covalent bonds as

reported by Pro_check (MOE version). (4) Lowest poten-

tial energy, as calculated using MOE 2005.06.

3D-JIGSAW server sequence alignment and model building

3D-JIGSAW employs PSI-BLAST39 to generate a posi-

tion specific scoring matrix (PSSM) for the template and

target sequence. This PSSM data is used by the PSI-Pred

program40 to predict secondary structures for both

sequences. The PSSM data and secondary structures are

used in a dynamic programming algorithm to perform

an initial alignment. A second dynamic programming

algorithm refines the initial alignment via multiple align-

ment of template structures.41 Target protein side chains

are positioned based on those in the template and are

also added from a side chain rotamer library when

needed. Finally, a mean-field calculation is performed to

select the most probable, best packed side chain

rotamers.41 The rDAT FASTA sequence was used as the

query for the 3D-JIGSAW server (www.bmm.icnet.uk/

�3djigsaw/). Sequence alignment and DAT homology

modeling relative to the LeuTAa template were derived

using both ‘‘interactive’’ and ‘‘automatic’’ modes. The

DAT atomic coordinates for the comparative model were

obtained after model building and selection by the meta-

server of the most energetically favorable structure. This

single model was downloaded, read by MOE 2005.06 and

saved in a molecular database. Using MOE pro_check, the

few steric clashes found were resolved by selecting back-

bone and side chain atoms of the implicated amino acid

residues and performing successive steps of AMBER99

energy minimization (convergence criteria 5 0.1 kcal/

mol, e 5 3). After resolving unfavorable contacts, the

protocol described earlier for the Robetta models was

applied to calculate partial charges and optimize hydro-

gen atoms and side chains, yielding Model 2.

Yamashita et al. alignment and MOE model building

The rDAT FASTA sequence and crystal structure coor-

dinates of LeuTAa were loaded into MOE 2005.06. The

primary amino acid sequences of LeuTAa and DAT were

manually aligned using the MSA proposed by Yamashita

et al.14 Because the initial partial geometry between tem-

plate and target was not specified, only the backbone

coordinates of LeuTAa were used for the model creation.

A series of 10 DATmodels were independently constructed

with MOE using a Boltzmann-weighted randomized pro-

cedure42 combined with specialized logic for the proper

handling of sequence insertions and deletions.43 Each in-

termediate model was evaluated by a residue packing

quality function sensitive to the degrees to which nonpo-

lar side chain groups are buried within the lipid bilayer

and hydrogen bonding opportunities are maximized.

Before the final refinement of side chains, a coarse mini-

mization of backbone atoms using AMBER99 and a con-

jugated gradient method (convergence criterion 5 1.0

kcal/mol, e 5 3) was performed to improve packing and

intramolecular interactions. No steric clashes were

observed. The same protocol described for the Robetta

models was applied to calculate partial charges and opti-

mize hydrogen atoms and side chains. The optimal MOE

model (Model 3) was selected using the criteria listed

above for Model 1, and by weighting the best scores for

side chain packing according to MOE’s packing evalua-

tion function. The sequence alignments underpinning

Models 1–3 are shown in Figures 1–3, respectively.

Modeling of Na1 binding sites

Two sodium atoms were placed in the DAT models

using the corresponding LeuTAa crystal coordinates; their

positions were manually refined in order to preserve

coordination bonds established with adjacent residues.

The side chains of such residues were adjusted to emulate

the LeuTAa environment using the rotamer explorer

module in MOE 2005.06. Side chains were relaxed (with

the two Na1 atoms and backbone positions fixed) using

AMBER99 (convergence criteria 5 1.0 kcal/mol, e 5 3).

Comparative Model of the Dopamine Transporter

DOI 10.1002/prot PROTEINS 1035

DAT model ligand docking

Construction and geometry optimization of DATsubstrates and inhibitors

Three-dimensional models of the DAT substrates dopa-

mine and amphetamine were constructed using the mole-

cule builder feature of MOE 2005.06 (structures pictured

in Fig. 4). Partial charges and hydrogen atoms were

added to protonated and unprotonated molecules using

the Merck Molecular Force Field 94X (MMFF94X), suita-

ble for small drug-like molecules.46–48 All structures

were energy minimized using the conjugated gradient/

truncated Newton optimization algorithm with conver-

gence criterion 5 0.05 kcal/mol, e 5 1.

Binding site selection and exploration

The ‘‘alpha site finder’’ module of MOE 2005.06 was

used to identify possible DAT ligand binding pockets

within the newly-generated DAT models. Hydrophobic or

Figure 1Sequence alignment used to build DAT Model 1, based on LeuTAa crystal structure information retrieved by the Robetta protein prediction server. The 12 LeuTAa TM

domains are highlighted. Gray blocks indicate level of sequence similarity. Tallest blocks: Residue is identical at that position. Intermediate blocks: Residues are

nonidentical but relatively conservative with respect to their properties. Small blocks: Residues share mild conservation with respect to structure or function. The absence

of a block indicates no appreciable structure/function conservation. Gaps in one sequence relative to the other are indicated by dashes. The 12 DAT TM domains are

highlighted and contrasted by varying the color. The UCSF Chimera Visualization System was used to generate this figure.44 [Color figure can be viewed in the online

issue, which is available at www.interscience.wiley.com.]

M. Indarte et al.


hydrophilic alpha spheres served as ‘‘probes’’ denoting

zones of tight atomic packing. All probe clusters of alpha

spheres not situated in cytoplasmic or phospholipid-

facing regions were used to identify potential binding

sites that were used in the docking simulations. These

alpha spheres were used as centroids for the creation of

dummy atoms used to define potential binding sites dur-

ing the docking process.

MOE-Dock 2005.06

A binding region is identified by a cluster of hydro-

phobic and hydrophilic alpha spheres; hydrophobic

spheres mark hydrophobic environments, and hydrophilic

spheres mark hydrophilic environments. Ligand atoms

are matched to corresponding alpha spheres during the

docking process. The alpha spheres are used to calculate

shape complementarity of small molecules fitting into

macromolecules, as well as binding affinities of these

conformers. Docking methods that employ alpha spheres

may generate bound conformations that approach crys-

tallographic resolution.49 The ligand explores the confor-

mational space to locate the most favorable binding ori-

entation and conformation (denoted as a ‘‘pose’’)24–27

by aligning and matching all triangles of the template

points with compatible geometry and chemistry; the pro-

tein atoms remain fixed during the process. For each

ligand, 100 poses were generated and scored in an effort

Figure 2Sequence alignment used to build DAT Model 2, based on LeuTAa crystal structure information retrieved by the 3D-JIGSAW protein prediction server. [Color figure can

be viewed in the online issue, which is available at www.interscience.wiley.com.]



to determine favorable binding modes. An affinity scor-

ing function, G, was employed to rank candidate poses.

This pairwise atomic contact scoring methodology esti-

mates the enthalpic contribution to the free energy of

binding using the following linear function:

G 5 Chbfhb 1 Cionfion 1 Cmligfmlig

1 Chhfhh 1 Chpfhp 1 Caafaa

The fx terms represent the fractional atomic contacts

for a specific interaction, x. The Cx terms are coefficients

that weight the interaction contribution of x to the affin-

Figure 3Sequence alignment as proposed by Yamashita et al.19 used to build DAT Model 3, based on the multiple sequence alignment of NSSE and LeuTAa. [Color figure can be

viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 4MOE 2005.06-generated 2D ligand representation used in DAT model

docking.45 Protonated and unprotonated species were used in the docking

simulations.

M. Indarte et al.


ity score. The individual terms are: hb, hydrogen bond

donor-acceptor pair interactions (an optimistic view is

taken; for example, two hydroxyl groups are assumed to

interact in the most favorable way); ion, ionic interac-

tions (a Coulomb-like term is used to evaluate the inter-

actions between charged groups); mlig, metal ligation

interactions (those involving nitrogen and sulfur atoms

and transition metals are so classified); hh, hydrophobic

interactions; hp, interactions between hydrophobic and

polar atoms; aa, an interaction between any two atoms.

Two different placement methodologies for docking DAT

substrates and inhibitors were used. The alpha triangle

placement method generates poses by superposition of

ligand atom triplets and triplet points within the receptor

site. The triangle matcher method generates poses in a

systematic and more accurate way than the alpha triangle

placement method by aligning ligand triplets of atoms

with triplets of alpha spheres in cavities of tight atomic

packing. The docking process accounted for the two pro-

tonation states of the amine group of ligands. Poses from

molecular databases of each ligand were scored based on

complementarity with binding pocket alpha spheres.

ASEDock

Alpha Sphere Based Protein-Ligand Docking (Ryoka

Institutes), or ASEDock, is a novel fast-docking program

written in the SVL language (MOE platform) and based

on the alpha shape method. Ligand atoms have alpha

spheres within 1 A. On the basis of this, concave shape

models can be created, and ligand atoms from a large

number of conformations generated by superposition with

these points can be evaluated and scored by maximum

overlap with the alpha spheres and minimum overlap

(repulsion) with receptor atoms. The initial ligand confor-

mations were subjected to energy minimization using the

MMFF94S force field46 and when converged, reproduced

experimentally bioactive conformations.49 The scoring

function used by ASEDock is based on protein–ligand

interaction energies. The interaction energy of a given

conformation is calculated using the following formula:

Utotal 5 Uele 1 Uvdw 1 Uligand 1 Usolv

Uele and Uvdw represent electrostatic and van der Waals

interactions, respectively, between the protein macromol-

ecule and the ligand. Uligand represents conformation

energy. Usolv represents the energy because of solvation.

The lowest Utotal of the multiple poses generated were

considered optimal poses. All alpha spheres not situated

in cytoplasmic or phospholipid-facing regions of the

DAT model were used as centroids for the creation of

dummy atoms used to dock DAT ligands. The docking

process took into account the two protonation states of

ligand amine groups. Poses from the molecular databases

for each ligand were ranked based on Utotal.

For each ligand, 500 conformations were generated

using the default systematic search parameters in the

ASEDock module. Five thousand poses per conformation

were randomly placed onto the alpha spheres located

within the TM domains. From the resulting 500,000

poses, 200 poses with the lowest Utotal values were

selected, and these poses were further optimized with the

MMFF94S force field. During this refinement step, the

ligand was free to move within the rigid binding pocket

(the transporter atoms were held fixed).

MOE-DOCK 2004.03 GA

A Monte Carlo simulated annealing process is used,

allowing a sampling of the conformational space for the

ligand and an extensive screening of all possible binding

sites in a particular region of the target macromole-

cule.50–52 Docking interaction energy (Utotal) of a given

conformation is estimated from a set of energy grids cen-

tered in the macromolecule binding site using the for-

mula given above for ASEDock. Macromolecule protein

coordinates remain fixed during the process, while the

flexible, mobile ligand moves along the grid to locate the

most favorable binding orientation and conformation

based on the interaction energy. A docking box of 45 345 3 45 grid points was employed with grid spacing of

0.375 A. The alpha spheres generated in the TM domains

by the site finder module were used as the centroids for

the docking box. Once the docking region was defined,

the alpha spheres were deleted (and not used in any sub-

sequent calculations). Minimized ligands were randomly

placed inside the docking box, and the docking process

initiated with an iteration limit of 10,000, cycle number

of 50, and run number of 100. The two protonation

states of the ligand amine group were taken into account

in the docking process. The final molecular database con-

tained 100 docked poses for each ligand as well as all

energy terms discussed earlier.

Validation of the DAT ligand docking process viaLeuTAa-leucine docking

The three docking methods described above were

used to assess the validity of the DAT-ligand docking pre-

dictions by calculating possible bound conformations of

leucine-LeuT complexes. The crystal structure of LeuT

was retrieved from the PDB and prepared for docking:

partial charges and hydrogen atoms were added, cal-

culated and relaxed within the protein structure as

described earlier. No further minimizations of side chains

were carried out. LeuTAa,-leucine docking poses were

obtained using MOE-Dock 2005.06, ASEDock or MOE-

DOCK 2004.03 GA, and compared to the original crystal

structure. The RMSDs of leucine bound in the crystal

versus the predicted bound leucine conformations for the

different methods were calculated using db_crystal_rmsd,



a SVL code custom-written by the Chemical Computing

Group.

RESULTS

Comparative models

The three 3D DAT models obtained are overall in good

agreement with respect to spatial overlap, especially in

the TM domains [Fig. 5(A)]. The most prominent points

of divergence between the 3 models occur in the extracel-

lular TM loop (EL 5) connecting TMs 9 and 10 [Fig.

5(B), blue arrow], and within TM 1b [Fig. 5(C), green

arrow] of the 3D-JIGSAW model. Indeed, the 10 plausible

conformations for the Yamashita et al. based alignment

(Model 3) diverge at the hinge region connecting TMs 9

and 10 (data not shown). Models 1 (Robetta-based) and 2

(3D-JIGSAW-based) possess a similar sequence alignment

(Figs. 1–3) that creates a similar profile of possible resi-

dues involved in ligand binding. Contrasting with Model

3, Models 1 and 2 overlapped well at the TM 9/10 hinge

(Fig. 5), as did the corresponding loop of LeuTAa (not

shown). This suggests that differences in the sequence

alignments obtained from the Robetta and 3D JIGSAW

servers relative to the Yamashita et al. alignment contrib-

uted to the divergence at DAT loop structures. Loop

positioning may be a critical feature in the extracellular

substrate recognition process. An incorrectly oriented

loop could occlude and remove from consideration a pu-

tative ligand binding pocket in docking experiments. The

three sequence alignments show that the rDAT main

insertions and deletions relative to LeuTAa occur in intra-

cellular loop (IL) 1 (deletion) and the beginning of TM 3

as well as EL 2 (insertion). No insertions or deletions are

observed in regions related to substrate binding sites.

Proline and glycine residues are highly conserved between

the DAT and LeuTAa in the first eight TM domains, sug-

gesting that the nature of a-helix disturbances is similar

between the proteins. Regarding TM 1b, the DAT model

polypeptide backbones do not completely overlap with

the LeuTAa template backbone, leading to subtle differen-

ces in side chain rotamer orientation, and bound sub-

strate conformations.

Docking of DAT substrates and inhibitors

Three docking algorithms were employed: MOE-Dock

2005.06, ASEDock and MOE-DOCK 2004.03 GA. Using

leucine/LeuTAa docking as a test system, ASEDock

yielded bound conformations with the lowest RMSD

scores (0.24 A), followed by MOE-DOCK 2004.03 GA

(0.49 A) and MOE-Dock 2005.06 (0.7 A). Even though

ASEDock appears to be the best in reproducing a physio-

logically relevant leucine-LeuTAa pose, all three methods

were used to elucidate potential DAT binding sites; their

Figure 5Panel A: Backbone superposition of the three comparative DAT models. Extensive

spatial overlap is observed between Models 1 (pink), 2 (blue), and 3 (yellow).

Regions of greatest divergence corresponded to sequences outside of the TM

domains; note the hinge that connects TM 9 and TM 10 (blue arrow). Panel B:

‘‘Zoom’’ view of TM 9 and TM 10 superposition from a different angle. For

clarity, not all TM domains are depicted. Panel C: ‘‘Zoom’’ view of the

superposition of the models with respect to TM 1, indicating Model 2 backbone

spatial differences (green arrow).

Figure 6Spatial similarity of LeuTAa and DAT Model 1 substrate binding sites. Leucine

(yellow, line depiction) is superposed on DAT Model 1 using the 2A65 X-ray

coordinates. Energetically optimal conformations for dopamine (white, ball-and-

stick) and amphetamine (pink, ball-and-stick) predicted by MOE-Dock 2005.06

are pictured. For a given ligand, the result of each docking simulation is

represented by a single chemical structure. The hinged a–helices TM 1 (salmon)

and TM 6 (orange), as well as TM 3 (green), TM 8 (white), TM 10 (cyan),

and TM 11 (gray), are highlighted. [Color figure can be viewed in the online

issue, which is available at www.interscience.wiley.com.]

M. Indarte et al.


RMSD scores are similar yet could potentially yield dif-

ferent poses. An important and attractive feature of the

ASEDock method is indicated by a correlation plot of

RMSD value versus interaction energy (Utotal), revealing

that the lowest RMSD values correlate to the lowest

interaction energies (see online supplementary material).

The three DAT models, refined as described in the Meth-

ods section, were initially employed in MOE-Dock

2005.06 docking simulations with the DAT substrates do-

pamine and d-amphetamine. Given that the two sub-

strates are close structural analogs, it is not surprising

that these ligands were found to dock essentially in the

same primary binding site of the DAT (Fig. 6). Consider-

ing that DAT docking of these substrates employed an

unbiased approach, entirely independent of LeuTAa dock-

ing of its leucine substrate, the substantial overlap

between the dopamine/amphetamine DAT site and the

leucine site of the analogous region of the LeuTAa crystal

structure is remarkable (Fig. 6). The coincidence of sub-

strate binding pockets within the DAT and LeuTAa, pro-

teins largely dissimilar in sequence that recognize struc-

turally dissimilar substrates, in part validates the present

DAT models.

Like the LeuTAa substrate binding pocket, the primary

DAT substrate pocket is at the approximate midpoint of

the lipid bilayer and very close to the two Na1 binding

sites. Regardless of the protonation state, each substrate

optimally fits in to the substrate binding site; however,

protonation introduces a pronounced drop in interaction

energy. A close-up view of the protonated substrates in

the binding site (Fig. 7) shows extensive spatial overlap

of predicted best poses despite the variety of docking

methods and homology models employed. The models

and bound conformations suggest that the charged amine

groups of dopamine and amphetamine can create a net-

work of hydrogen bonds with the amide backbone car-

bonyl groups of A77 and V78 (TM 1) and S320 and

L321 (TM 6), as well as direct interactions with the

carboxylate side chain of D79 (TM 1). The DAT models

do not imply direct interactions between the Na1 atoms

and substrate, consistent with the finding that dopamine

binds to the DAT in the absence of Na1.53–55 The

Figure 7A representative predicted binding scenario for dopamine and amphetamine obtained with different models and docking algorithms using MOE. The poses represent the

top-ranked DAT-ligand associations based on total interaction energy using ASEDock with Model 1 for dopamine (white, ball-and-stick) and amphetamine (pink, ball-

and-stick). Leucine exported from the crystal structure (yellow, line depiction) demonstrates the spatial similarity of binding pockets between rDAT and LeuTAa and the

considerable overlap of leucine and the two docked structures. Models 1 and 3 and the three different docking methods pose charged substrate amino groups (nitrogen

atoms in blue) close to D79, generating the corresponding interaction in the form of hydrogen bonds. The H-bond network (cyan) depicts and describes the strength of the

bond interaction based on ideal angles and distances, represented as the percentage of possible interaction strength between a given residue and ligand. Model 2 displays a

slightly different docking scenario (data not shown), possibly due to the tilted backbone and different side chain locations predicted by ASEDock. The two sodium atoms

(green spheres) do not directly interact with the substrates. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]



m-hydroxyl group of dopamine can form hydrogen

bonds with the amide backbone carbonyl group of S421

and A422 (TM 8). The substrate aromatic moiety can

establish favorable hydrophobic interactions with V152

(TM 3) and V327 (TM 6). More importantly, p–p stack-

ing of this substrate group with the phenyl rings of Y156

(TM 3), F319 (TM 6), and F325 (TM 6) are possible

(Fig. 7). Recently developed MOE 2006.07 software was

used to create ligand interaction plots for charged dopa-

mine and amphetamine (Fig. 8), providing a more visu-

ally-digestible arrangement of putatively key intermolecu-

lar interactions that aids in interpreting the 3D juxtaposi-

tion of ligand and transporter protein.

Docking calculations that take into account alpha

sphere position also yielded a secondary substrate pocket

that affords binding of dopamine and amphetamine with

fairly low interaction energies (Fig. 9). This broad sec-

ondary binding region is located at the extracellular

interface and defined by TMs 1, 6, 10, and 11 and ELs 3,

4a, and 4b.

The ligand-docked DAT models identify discrete DAT

amino acid residues as putative contributors to the sub-

strate pocket. A constellation of DAT residues can be

derived in this way for each ligand, providing targets for

site-directed mutagenesis and subsequent pharmacology

toward high-resolution determination of drug binding

sites.

DISCUSSION

Comparative models of membrane-spanning proteins

with amino acid sequence identity to the template of less

than 25% can have TM Ca-RMSD values above 3.0 A

relative to the native protein.56 Models displaying such

variation between native and predicted conformations

Figure 8Ligand interaction plot of the MOE-Dock 2005.06-generated DAT amphetamine (left panel) and dopamine (right panel) binding pockets. This plot depicts the 2D

(‘‘flattened’’) spatial arrangement of ligand and DAT protein with respect to key interactions. The proximity contour (dashed lines) and solvent exposed areas (solid

purple spheres) of the ligand atoms are indicated, as are the polar (pink), hydrophobic (green), and solvent-exposed (light blue shadow) binding pocket amino acids.

Acidic and basic residues are highlighted with red and blue halos, respectively. [Color figure can be viewed in the online issue, which is available at

www.interscience.wiley.com.]

Figure 9Primary and secondary substrate binding sites predicted from DAT ligand

docking to DAT Model 1. The primary substrate pocket is occupied by the top-

ranked ASEDock poses for dopamine (white, ball-and-stick) and amphetamine

(pink, ball-and-stick). Leucine, exported from its crystal structure (yellow, line

depiction) is included to aid in identification of the primary site. Docked poses

with optimal interaction energies for dopamine (white, stick) and amphetamine

(pink, stick) delineate a docking-derived broad secondary substrate site. The

hinged a–helices TM 1 (salmon) and TM 6 (orange), as well as TM 3 (green),

TM 8 (white), TM 10 (cyan), TM 11 (gray), EL3 (red), EL4a (yellow), and

EL4b (yellow), are highlighted because of their contribution to the docking

calculations. [Color figure can be viewed in the online issue, which is available

at www.interscience.wiley.com.]

M. Indarte et al.


can nevertheless be useful in implicating residues key for

ligand binding. The low sequence similarity (20%)

between the DAT and LeuTAa proteins represents a chal-

lenge for comparative modeling. Accurate sequence align-

ments are difficult to obtain with conventional algo-

rithms. Poor alignments will yield 3D models with

abnormalities such as extratransmembranous loops in

TM regions, or an unacceptable number of hydrophilic

or charged amino acids in otherwise hydrophobic TM

domains. To surmount these hindrances and create feasi-

ble molecular models with predictive power, two different

meta-servers (3D-JIGSAW and Robetta) were used to cre-

ate reliable comparative models for the DAT. These meta-

servers improve upon conventional alignment algorithms

by incorporating secondary structure prediction data,

producing alignments with an accuracy that challenges

the predictive skills of experts.57 Modeling TM domain

proteins with 3D-JIGSAW alignments resulted in models

with statistically significant lower RMSD values than

other alignments.58 The Robetta server has also displayed

excellent structure prediction capabilities, enabling crea-

tion of a 3D model for an entire protein sequence in the

absence of significant sequence identity to a template

protein of known 3D structure.31,32,59 Accurate align-

ments of membrane protein sequences are achieved by

using template and target profiles (statistical representa-

tions of protein families)57,58 that include secondary

structure information in the scoring of alignments,

similar to those used by the 3D-JIGSAW and Robetta

servers.

Classically, docking calculations are used to predict

optimal ligand–protein conformations and to perform

virtual screening of compound databases in discovering

therapeutic lead compounds.24,28,29,60,61 Generally, the

docking calculation is performed when the location of a

binding pocket is known or suspected. Several methods

have been developed to find probable ligand binding

regions.62–72 None, however, takes full advantage of

pharmacologically-characterized ligands in selecting the

most feasible binding site. In the present study, a multi-

ple docking approach was employed in which probable

DAT binding sites are examined thoroughly by perform-

ing docking calculations using pharmacologically well-

characterized DAT ligands as molecular probes.

The recently published crystal structure of the bacterial

leucine transporter LeuTAa,14 a protein distantly but

clearly homologous to the DAT, provided surprising reve-

lations concerning the putative 3D structure of the 12

TM NSS family of transporters. In the LeuTAa—leucine

cocrystal, the center of the TM 1 and TM 6 a–helices is

unwound, exposing main chain carbonyl oxygen and

nitrogen atoms that H-bond to the leucine substrate and

one of the two Na1 ions required for transport. These

unwound regions also apparently serve as hinges involved

in interconversion between outward- and inward-facing

transporter conformations. The DAT comparative models

obtained in the present work also largely adopt the Leu-

TAa 3D conformation, including centrally located disrup-

tions in the TM 1 and 6 helices. Like LeuTAa, the DAT

model suggests that TMs 3 and 8 combine with TMs 1

and 6 to form the substrate binding pocket. Using the

modeling tool SCWRL3,73 the same four TM domains

were found to form the substrate binding site of a 3D

SERT model.74 Several NSS structure-function studies

support direct contributions of TMs 1 and 3 to substrate

recognition.10–12,75–83 TMs 6 and 8 had not been

shown to directly contact the substrate prior to the Leu-

TAa crystallization, although TMs 3, 7, and 8 of the DAT

were able to coordinately bind a Zn21 ion, indicating

close proximity.13

Despite the different protein sequences and cognate

substrate molecules, all ligand docking algorithms located

an optimal binding pocket for dopamine (and its analog

amphetamine) within the DAT model that was almost

superimposable with that for leucine in the LeuTAa crys-

tal (Figs. 6 and 7). DAT residues in TM 1 (e.g., F76 and

D79) and TM 3 (e.g., V152) are within reach of the

docked substrates (Figs. 7 and 8), and the pharmacology

of engineered NSS mutants at these positions is consist-

ent with the DAT model’s substrate binding site. The

rDAT F76A mutation dramatically affected dopamine

apparent affinity.84 Mutations of the identical position in

the hSERT (Y95) and GAT-4 GABA (E61) transporters

suggest a direct interaction with substrates.75,80 The

DAT model indicates that the D79 residue, within the

unwound region of TM 1, may directly interact with sub-

strates, its carboxylate coordinating with the substrate

amino groups of the best ranked poses regardless of the

different docking methods. Cases have been made

for85,86 and against81,82 a salt bridge forming between

the DAT D79 or the analogous D98 SERT carboxylate

and the substrate amine. In the present DAT model, the

D79 carboxylate colocalizes with the carboxylic group of

the leucine substrate in the LeuTAa crystal, arguing

against an ionic bond with the substrate amino group.

D79 is simultaneously able to establish a direct interac-

tion with one of two Na1 sites in the model, the role

played by the backbone carbonyl group of the analogous

LeuTAa residue, G24.14 Accordingly, a D98 SERT mutant

was compromised in its ability to utilize Na1 during sub-

strate transport.85 In some fashion, D79 appears to be

contributing electrostatic interactions that enhance sub-

strate recognition. Finally, the DAT model suggests favor-

able interactions between the TM 3 V152 side chain and

either the aromatic ring or the lipophilic hydrocarbon

portion of both dopamine and amphetamine. This resi-

due is critical to DAT substrate transport.79 The analo-

gous SERT residue is found to be on the ligand-accessible

face of TM 3, in or near the serotonin binding site.77,78

Two of the ligand docking approaches used with the

DAT model, ASEDock and MOE-Dock 2005.06, yielded a

consensus secondary substrate binding pocket distinct



from the primary substrate pocket (Fig. 9). It is conceiva-

ble that this secondary site is a temporary ‘‘waiting

room’’ for the substrate, and the substrate is ushered to

its primary binding pocket in the presence of Na1, Cl2,

or the appropriate outward-facing DAT conformation.

The secondary site may be important for recognition of

cocaine and other dopamine uptake inhibitors (unpub-

lished data). Amphetamine and perhaps other uptake

inhibitors may directly compete with dopamine for occu-

pancy of this secondary pocket if the current conforma-

tion resembles a bioactive conformation able to recognize

and bind inhibitors.

The DAT models remained fixed during the docking

process and therefore conformational flexibility of the

macromolecule upon binding was not addressed. This

should be noted as a limitation of the approach given

that the three DAT models are based on the LeuTAa struc-

ture in only one configuration—the transporter with a

tightly bound substrate. Moreover, the modeling effort

may well miss potential ligand binding sites or overstate

minor sites that would be recognized as such if more

flexibility were introduced in the model. Despite the con-

siderable progress achieved in the past few years, accurate

docking methods that predict macromolecular conforma-

tional changes upon ligand binding still remain computa-

tionally challenging.24

This novel DAT model will continue to provide new

DAT mutagenesis targets. The pharmacology from these

mutants will in turn refine the DAT model, affording

high resolution mapping of DAT substrate and inhibitor

binding sites. At that point, the DAT model may be used

for QSAR analysis of putative DAT ligands, involving in

silico screening of structural libraries containing millions

of compounds. The more promising compounds would

be screened at the bench, and then in preclinical and

clinical settings. In this way, rational design of novel DAT

pharmacotherapeutic ligands should be possible. Such

ligands may interfere with actions of abused psychosti-

mulants including cocaine and the amphetamines while

largely sparing normal DAT function. Novel medications

for treating depression, anxiety disorders, attention defi-

cit hyperactivity disorder, narcolepsy, Parkinson’s disease,

and other DAT-related disorders may also result from

rational drug design afforded by this DAT model.

CONCLUSION

Using the LeuTAa crystal structure as a template, three

comparative modeling approaches were used to create

three DAT models. Although quite similar, the nonidenti-

cal sequence alignments led to subtle but significant dif-

ferences between the models. Three docking methods

were applied to the three DAT models to identify poten-

tial binding sites for the substrates dopamine and the

psychostimulant d-amphetamine. The docking calcula-

tions identified two discrete DAT binding regions: a pri-

mary substrate binding site correlating with the binding

site observed in the LeuTAa crystal structure, and a broad

secondary substrate site closer to the extracellular inter-

face. The secondary site may act as a potential staging

area for substrate translocation through the cell mem-

brane. The proposed binding pockets and their function

are consistent with published and unpublished mutagen-

esis data. The DAT models coupled with ligand docking

simulations are refining mutagenesis and other structure-

function investigations, and should aid in the develop-

ment of QSAR as well as pharmacophore models toward

development of novel medications.

ACKNOWLEDGMENTS

Chemical Computing Group is acknowledged for pro-

viding MOE software, especially for access to a beta

release version of MOE. M.I. thanks the technical sup-

port scientists at CCG, especially Dr. Suzanne Schreyer,

Dr. Alain Deschenes and Dr. Andrew Henry for their as-

sistance. Dr. Barry Honig is thanked for helpful com-

ments and discussions. Dr. Junichi Goto is thanked for

granting the Ryoka Institute docking program ASEDock.

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