Structure-based ligand discovery for the Large-neutral
Amino Acid Transporter 1, LAT-1
Ethan G. Geiera,1, Avner Schlessingera,b,1,2, Hao Fana,b,c, Jonathan E. Gablec,d, John J. Irwina,b,c, Andrej Salia,b,c,3,
and Kathleen M. Giacominia,3
Departments ofaBioengineering and Therapeutic Sciences andcPharmaceutical Chemistry,bCalifornia Institute for Quantitative Biosciences,
anddGraduate Group in Biophysics, University of California, San Francisco, CA 94158
Edited by John Kuriyan, University of California, Berkeley, CA, and approved February 19, 2013 (received for review October 17, 2012)
The Large-neutral Amino Acid Transporter 1 (LAT-1)—a sodium-
independent exchanger of amino acids, thyroid hormones, and pre-
scription drugs—is highly expressed in the blood–brain barrier and
various types of cancer. LAT-1 plays an important role in cancer
development as well as in mediating drug and nutrient delivery
across the blood–brain barrier, making it a key drug target. Here,
we identify four LAT-1 ligands, including one chemically novel sub-
strate, by comparative modeling, virtual screening, and experimen-
tal validation. These results may rationalize the enhanced brain
permeability of two drugs, including the anticancer agent acivicin.
Finally, two of our hits inhibited proliferation of a cancer cell line by
distinct mechanisms,providing useful chemicaltoolstocharacterize
the role of LAT-1 in cancer metabolism.
membrane transporter|polypharmacology|glioblastoma multiforme|
solute carrier (SLC) transporter
where it mediates transport of large-neutral amino acids (e.g.,
tyrosine) and thyroid hormones (e.g., triiodothyronine) across the
cell membrane(1).Morespecifically, LAT-1 ishighlyexpressedin
theblood- andbrain-facingmembranesofthe blood–brainbarrier
(BBB) to supply the central nervous system (CNS) with essential
nutrients and to help maintain the neural microenvironment (2).
LAT-1 is also an important drug target because it transports
several prescriptiondrugs,suchas the antiparkinsoniandrug L-dopa
and the anticonvulsant gabapentin, across the BBB, thereby en-
abling their pharmacologic effects (3, 4). This function at the
BBB has made LAT-1 a target for drug delivery by modifying
CNS-impermeable drugssuch that they become LAT-1substrates
and have enhanced BBB penetration (5, 6).
In addition, LAT-1 expression levels are increased in many
types of cancer, including non-small-cell lung cancer and glio-
blastoma multiforme (GBM) (7, 8). LAT-1 expression increases
as cancers progress, leading to higher expression levels in high-
grade tumors and metastases (9). In particular, LAT-1 plays a key
role in cancer-associated reprogrammed metabolic networks by
supplying growing tumor cells with essential amino acids that are
used as nutrients to build biomass and signaling molecules to
enhance proliferation by activating progrowth pathways such as
the mammalian target of rapamycin (mTOR) pathway (10).
Furthermore, inhibiting LAT-1 function reduces tumor cell pro-
liferation, indicating that it may be a viable target for novel anti-
cancer therapies (11–13). A cancer drug targeting LAT-1 can
therefore be a LAT-1 inhibitor that deprives the cancer cells of
nutrients or a cytotoxic LAT-1 substrate with an intracellular
target (e.g., a metabolic enzyme).
LAT-1 is a large protein with 12 putative membrane-spanning
helices (14). To transport solutes across the membrane, LAT-1
binds SLC3A2, a glycoprotein with a single membrane-spanning
helix that serves as a chaperone for LAT-1 (14). The atomic
structure of human LAT-1 is not known, but LAT-1 exhibits sig-
nificant sequence similarity to prokaryotic transporters such as
members of the amino acid/polyamine/organocation transporter
(APC) family, whose representative structures have been recently
arge-neutral Amino Acid Transporter 1 (LAT-1) is a sodium-
independentexchangerfoundin thebrain, testis,andplacenta,
determined by X-ray crystallography (15–19). Structures of the ar-
ginine:agmatine antiporter AdiC from Escherichia coli (15, 17, 18)
and Salmonella enterica (20) in different conformations reveal an
internal twofold pseudosymmetry, similar to the structures of the
These data, combined with structures of additional related trans-
porters (22) and molecular dynamics (MD) simulations (23), sug-
gest a common transport mechanism among the LAT-1 homologs
and LeuT, in which the role of sodium in LeuT is proposed to be
mimicked by a protoninsome APC transporters (23). Thus, LAT-1
probably also transports ligands across the cell membrane via the
alternating access transport mechanism (22, 24, 25).
In this study, we take an integrated computational and experi-
mental approach to characterize previously unknown LAT-1
ligands. We construct structural models of LAT-1 based on
structures of homologous APC family transporters from pro-
karyotic organisms and then perform virtual ligand screening of
metabolite and prescription drug libraries against these models to
predict small-molecule ligands. The top-scoring hits are tested
experimentally for LAT-1 inhibition and transport by using cis-
inhibition experiments and trans-stimulation assays, respectively.
Furthermore, we characterize the effect of select validated ligands
on cell proliferation. Finally, we describe the pharmacological
implications of our results, including how the intended and un-
intended effects of the discovered ligands may be mediated by
LAT-1 transport across the BBB as well as their potential use as
chemical tools to characterize the role of LAT-1 in cancer.
LAT-1 Predicted Structure and Ligand Binding. LAT-1 was modeled
based on the X-ray structure of the arginine/agmatine transporter
AdiC from E. coli in the outward-occluded arginine-bound con-
formation (17) and the structure of the APC transporter ApcT
from Methanococcus jannaschii in an inward-apo conformation
(16) (Fig. S1 and SI Materials and Methods). The final LAT-1
model contains the whole transmembrane domain of the protein
(i.e., the 12 transmembrane helices), including the residues con-
stituting the predicted ligand-binding site. Comparative models
were firstscoredby using Z-DOPE, a normalizedatomic distance-
dependent statistical potential based on known protein structures
(26). TheZ-DOPE scores of the top models were −0.3,suggesting
that 60%ofitsCα atomsare within 3.5Å oftheir correctpositions
Author contributions: E.G.G., A. Schlessinger, J.J.I., A. Sali, and K.M.G. designed research;
E.G.G., A. Schlessinger, and J.E.G. performed research; E.G.G., A. Schlessinger, A. Sali, and
K.M.G. contributed new reagents/analytic tools; E.G.G., A. Schlessinger, H.F., J.E.G., A. Sali,
and K.M.G. analyzed data; and E.G.G., A. Schlessinger, A. Sali, and K.M.G. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1E.G.G. and A. Schlessinger contributed equally to this work.
2Present address: Department of Pharmacology and Systems Therapeutics, Tisch Cancer
Center, Mount Sinai School of Medicine, New York, NY 10029.
3To whom correspondence may be addressed. E-mail: firstname.lastname@example.org or kathy.giacomini@
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| April 2, 2013
| vol. 110
| no. 14www.pnas.org/cgi/doi/10.1073/pnas.1218165110
to discriminate between known ligands and likely nonbinders
(decoys), by using enrichment curves derived from ligand-docking
calculations (28). The logAUC score for the final refined LAT-1
model was 31.9 (Table S1), suggesting that it is suitable for pre-
dicting ligands for experimental testing (28–30).
The model of LAT-1 interacting with phenylalanine indicates
that the majority of the key polar interactions between LAT-1 and
the carboxyl and amino group of the amino acid ligands are
conserved between LAT-1 and the AdiC template structure (Fig.
1A and Fig. S1). For example, the backbone polar groups of
LAT-1 residues T62, I63, I64, S66, G67, F252, A253, and G255
are predicted to form polar interactions with phenylalanine (Fig.
1). These residues correspond to A22, I23, M24, S26, G27, W202,
S203, and I205 of AdiC, which make similar interactions with the
carboxyl and amino groups of its ligand arginine (17). Because the
carboxyl and amino groups are conserved among all other known
LAT-1 ligands, such as thyroxine and gabapentin (Fig. 1B), we
hypothesize that they make similar interactions with LAT-1.
Conversely, differences in the ligand preferences of LAT-1 and
AdiC may be explained by two major differences in the binding
sites of the LAT-1 model and the AdiC structure (Fig. S2). First,
several residues with hydrophobic side chains (i.e., I139, V148,
F252, F402, and W405) are located in the LAT-1 binding site,
likely contributing to increased ligand-binding affinity of hydro-
phobic amino acids to LAT-1 via van der Waals interactions and
the hydrophobic effect (e.g., the tryptophan indole ring). Some of
these hydrophobic residues are replaced by nonhydrophobic res-
idues in LAT-1 homologs, including the template structure AdiC
and other SLC7 members. For instance, the aromatic residue
W405 in LAT-1 corresponds to the polar T361 in AdiC. Second,
several binding site residues in AdiC are replaced by residues with
smaller side chains in LAT-1, creating a larger volume in LAT-1’s
binding site that can accommodate larger amino acids. For in-
stance, M104, I205, and W293 in AdiC correspond to the smaller
V148, G255, and S342 in LAT-1 (Fig. 1A and Fig. S2).
Virtual Screening of Drugs and Metabolites. We computationally
screened filtered libraries of 6,436 and 12,730 small molecules
from the Kyoto Encyclopedia of Genes and Genomes (KEGG)
DRUG and KEGG LIGAND COMPOUND databases (28),
respectively, against two LAT-1 models (Fig. 2 and Table S1).
Some of the top-scoring hits were shown previously to be LAT-1
ligands, increasing our confidence in the binding site model. For
example, the known substrate L-Trp was ranked 50th in the
docking screen of KEGG LIGAND COMPOUND. The 200
(3.1%) KEGG DRUG and 500 (3.9%) KEGG COMPOUND
top-scoring hits against our top two models were analyzed
manually. A compound was selected for experimental testing
based on three criteria: (i) similarity between its docking pose
and those of known ligands in complex with LAT-1 (28); (ii) the
chemical novelty of its scaffold, especially if it occurred fre-
quently among the top-scoring compounds; and (iii) its phar-
macological effect (28).
Experimental Validation of Predicted Ligands. A LAT-1–overex-
pressing cell line was generated by stably transfecting HEK cells
with human LAT-1 cDNA. HEK-LAT1 cells expressed 20-fold
higher levels of LAT-1 mRNA relative to HEK-EV cells and
demonstrated LAT-1–specific uptake of the established system L
substrates, gabapentin and L-leucine (Fig. S3 A–D). Twelve of the
top-scoring molecules were selected for experimental testing by
cis-inhibition assay (Table 1, Table S2, and Fig. 2). Each molecule
was tested as a LAT-1 ligand by determining its ability to inhibit
transport of a known LAT-1 substrate in HEK-LAT1 cells at
LAT-1 inhibitor 2-aminobicyclo-(2,2,1)-heptane-2-carboxylic acid
(BCH) was also included as a positive control. At 100 μM, in-
hibition of intracellular gabapentin accumulation ranged from
88% (3,5-diiodo-L-tyrosine) to <10% (cystine, mebendazole, and
nocadezole), with the metabolites 3,5-diiodo-L-tyrosine and
3-iodo-L-tyrosine, as well as the tryptophan hydroxylase inhibitor
fenclonine and the anticancer agent acivicin, demonstrating sig-
nificant inhibition of gabapentin and L-leucine transport (Fig. 3A
and Fig. S3E). Acivicin also obtained a dissimilarity score of 0.74
using the JCDissimilarityCFTanimoto score, which calculates
dissimilarities among molecules based on chemical fingerprints,
structure of the LAT-1–phenylalanine complex. LAT-1 (gray) and phenylalanine
(cyan) are shown as the stick models; oxygen, nitrogen, and hydrogen atoms
Phe-252, Ala-253, and Gly-255) are shown as dotted gray lines. (B) Structures
of representative LAT-1 substrates. Known LAT-1 substrates, including me-
tabolites (tryptophan, methionine, and thyroxine) and prescription drugs
(melphalan, L-dopa, and gabapentin) are shown using MarvinView 184.108.40.206
Predicted LAT-1 structure and ligand-binding mode. (A) Predicted
of the known substrate tryptophan (green lines) and four ligands discovered
in the docking screen. Residues making polar interactions with the ligand
are illustrated with sticks; carbon atoms are colored in white, nitrogen atoms
in blue, and oxygen atoms in red; hydrogen bonds are represented by
dotted gray lines. The predicted pose of a known LAT-1 ligand, tryptophan,
is shown with green lines. The compounds depicted are 3-Iodo-L-tyrosine
(A), 3,5-diiodo-L-tyrosine (B), fenclonine (C), and acivicin (D). Halogen
atoms in the discovered ligands are colored in purple (iodine) and green
Predicted binding modes for LAT-1 ligands. Predicted binding modes
Geier et al.PNAS
| April 2, 2013
| vol. 110
| no. 14
indicating that it is a chemically novel LAT-1 ligand (Table 1 and
Materials and Methods).
The potencies of selected active ligands were further estab-
lished by determining the IC50values for inhibiting gabapentin
accumulation in the HEK-LAT1 cells. IC50values ranged from
7.9 μM (3,5-diiodo-l-tyrosine; Fig. 3B) to 340 μM (acivicin; Fig.
3C). At 10 μM, inhibition of gabapentin accumulation ranged
from 61% (3,5 diiodo-l-tyrosine) to <10%, with 3,5-diiodo-l-tyrosine
and 3-iodo-l-tyrosine significantly inhibiting gabapentin transport
(Fig. 3A). Interestingly, 3,5-diiodo-l-tyrosine is a stronger inhibitor
than the positive control BCH. In summary, one-third (4 of 12)
of the top-scoring molecules selected for experimental testing
are LAT-1 ligands capable of inhibiting gabapentin and L-leucine
transport in HEK-LAT1 cells.
Identification of LAT-1 Substrates. The four molecules found to
significantly inhibit gabapentin accumulation in the HEK-LAT1
assay. This assay takes advantage of LAT-1’s obligatory exchange
mechanism of transport by exchanging intracellular L-leucine
from preloaded HEK-LAT1 cells with an extracellular mole-
served as positive controls and were able to induce L-leucine
efflux from the HEK-LAT1 cells, including L-leucine (43%),
gabapentin (36%), and BCH (30%) (Fig. 4A). In contrast, glycine
was used as a negative control because it is known not to be
a LAT-1 substrate and did not induce any L-leucine efflux. Two of
the four inhibitors confirmed in our cis-inhibition assay also in-
duced L-leucine efflux. Acivicin and fenclonine induced L-leucine
efflux by 27% and 29%, respectively, indicating that they are
transported by LAT-1. These results indicate that the drug-like
molecules acivicin and fenclonine, which both have pharmaco-
dynamic effects in the CNS, are likely LAT-1 substrates. Surpris-
ingly, both of the more potent LAT-1 inhibitors, the metabolites
3,5-diiodo-L-tyrosine and 3-iodo-L-tyrosine, were only able to
induce 7.9% and 5.4% L-leucine efflux, respectively, suggesting
that they are inhibitors that only bind to, but are not transported
by, LAT-1. Finally, guanfacine and rufinamide were also studied,
and neither induced significant L-leucine efflux.
Inhibition of LAT-1–Dependent Cell Proliferation. LAT-1 is highly
expressed in various cancer cells, providing them with nutrients
and signaling molecules for growth. Thus, a drug targeting LAT-1
in cancer can be an inhibitor that deprives the cancer cells from
nutrients or a cytotoxic substrate with an intracellular target. We
therefore investigated the antiproliferative effects of select vali-
dated LAT-1 ligands, including the LAT-1 substrate acivicin and
the inhibitor 3-iodo-L-tyrosine, by cell proliferation assay in the
high LAT-1–expressing GBM cell line, T98G (8). The LAT-1–
specific effects of each ligand on cell growth were determined in
control cells (T98G-EV) and cells with LAT-1 expression (Fig.
S4A) and function (Fig. S4B) knocked down (T98G-KD). The
anticancer drug acivicin was a more potent growth inhibitor of
T98G-EV (75% growth reduction) than T98G-KD (51% growth
reduction) (Fig. 4B). Similarly, 3-iodo-L-tyrosine had a more po-
tent effect on T98G-EV cells, reducing their growth by 27%,
Table 1.Small-molecule ligands confirmed experimentally
no i t cnuF*emaN
y t i r a l im i ss iD
3,5-l-diiodotyrosine Tyrosine metabolism; thyroid hormone deficiency
treatment; radioactive agent
3-Iodo-l-tyrosineTyrosine metabolism; radioactive agent
r o t i b i hn in i n o t o r eSen i no l cneF
81 . 0
47 . 0 c i t sa l poen i t nAn i c i v i cA
*Generic or chemical name of the molecule.
†Pharmacological function of the drug or the physiological function of the metabolites, when applicable.
‡Dissimilarity measure calculated with the Chemaxon fingerprints. Dissimilarity values of >0.7 suggest that the molecule is
chemically different from all known LAT-1 ligands.
§A 2D sketch of the molecule is shown.
ligands were validated by cis-inhibition of gabapentin uptake. (A) Cells were
coincubated with 12 predicted ligands and a positive control (BCH) at either
100 μM (filled bars) or 10 μM (open bars) concentrations and gabapentin
(1 μM unlabeled and 10 nM radiolabeled). Each bar depicts the mean of two
to four separate experiments; error bars represent SEM. (B and C) Dose-
dependent inhibition of gabapentin (1 μM unlabeled with 10 nM radio-
labeled) accumulation by 3,5-diiodo-L-tyrosine (IC50= 7.9 μM) and acivicin
(IC50= 340 μM), respectively. Each point is the mean of two or three separate
experiments; error bars represent SEM. Statistical analysis in A was by one-way
ANOVA and Dunnett’s multiple comparison test. *P < 0.05.
Experimental validation of predicted LAT-1 ligands. Predicted LAT-1
| www.pnas.org/cgi/doi/10.1073/pnas.1218165110Geier et al.
whereas having no effect on T98G-KD (Fig. 4B Right). These
of inhibiting cancer cell proliferation in a LAT-1–dependent
manner by means of two distinct mechanisms, including nutrient
deprivation and cytotoxicity, respectively.
Three key findings emerge from our study. First, two drug-like
molecules that interact with different proteins in the CNS are also
substrates of LAT-1. This finding may explain the mechanism by
which these drugs penetrate the BBB to reach their targets in the
CNS. It also provides a starting point for optimizing the two
drugs for better BBB permeability. Second, two of the discov-
ered LAT-1 ligands, including one inhibitor and one substrate,
inhibit proliferation of cancer cells. This result indicates that
LAT-1 can be targeted for cancer therapy by means of different
mechanisms and reveals chemical tools for further characteriz-
ing the role of LAT-1 in cancer. Third, the identified LAT-1
ligands achieve their pharmacological effect (positive or nega-
tive) on the CNS or cancer by interacting with multiple targets.
This finding suggests that effective therapy can be obtained by
applying modeling and docking approaches to whole systems,
including pathways and networks. We take each of the three key
findings in turn.
LAT-1–Mediated BBB Drug Permeability. Passive diffusion has long
been thought of as the primary mechanism by which most drugs
cross the BBB to permeate the CNS (31). The contribution
of carrier-mediated transport to this process is assumed to be
minimal, even though different classes of membrane transporters
have been shown to restrict and/or facilitate access of drugs,
nutrients, and toxins to the CNS (32–34). LAT-1 is one such
influx transporter known to transport nutrients and xenobiotics
across the BBB. In this study, we identified two previously un-
known LAT-1 substrates, including acivicin and fenclonine,
which may also cross the BBB via LAT-1–mediated transport.
Both were found to be likely LAT-1 substrates in trans-stimula-
tion studies (Fig. 4), and both are known to have pharmacody-
namic effects in the CNS. Even though previous studies have
used trans-stimulation to establish whether or not a specific
transporter can transport different compounds (35–37), this as-
say provides indirect evidence that a compound may be a sub-
strate for a specific transporter. Nevertheless, acivicin was
assessed in a clinical trial for treating various solid tumors that
did not involve the CNS but failed these trials due to CNS-re-
lated toxic side effects (e.g., lethargy and confusion) (38).
Furthermore, these side effects were reversed when acivicin was
concomitantly administered with a mixture of amino acids, in-
cluding the prototypical LAT-1 substrate,
observations highly implicate LAT-1 in mediating acivicin’s CNS
permeability in humans. The second molecule, fenclonine, is an
irreversible tryptophan hydroxylase inhibitor used to deplete
CNS serotonin levels in animal models of human disease (39).
Together with our results, LAT-1 likely mediates the effects in the
CNS by transporting fenclonine across the BBB. Therefore, influx
transporters such as LAT-1 may be important mediators of drug
efficacy and toxicity in the CNS and have a greater contribution to
drug penetration across the BBB than previously thought.
Targeting LAT-1 for Cancer Therapy. Changes in cell metabolism are
strongly associated with cancer. Membrane transporters have
been shown to play a key role in such reprogrammed metabolic
networks by providing nutrients to transforming cells. For ex-
ample, the glucose transporter (GLUT1; SLC2A1) is up-regulated
in various cancers to provide glucose as a carbon source to ac-
commodate an increased rate of anabolic cellular reactions and
to maintain a microecosystem favorable for cancer cells (40).
Moreover, LAT-1 imports essential amino acids that serve as
nutrients and pro-proliferative signaling molecules by exporting
glutamine brought into cancer cells via the glutamine transporter
ASCT2 (10). Thus, therapeutics targeting LAT-1 can be (i)
inhibitors that selectively block transport by LAT-1 and/or
ASCT2, depriving the cancer cell of nutrients required for pro-
liferation, or (ii) cytotoxic substrates that are delivered into the
cell via LAT-1 and/or ASCT2 to act on an intracellular target.
LAT-1 ligands that act through each of these mechanisms were
discovered in our screen (Figs. 2–4 and Table 1).
First, 3-iodo-L-tyrosine is a thyroid hormone derivative typi-
cally used to treat hormone deficiencies and as a radioactive
agent. Here, cis-inhibition and cell-proliferation experiments
identified 3-iodo-L-tyrosine as a potent LAT-1 inhibitor (Fig. 3A)
that reduces proliferation of T98G glioblastoma cells (Fig. 4B),
possibly by starving these cells of nutrients supplied by LAT-1.
Our results suggest that, in addition to its putative anticancer
applications, 3-iodo-L-tyrosine may be useful as a diagnostic imag-
ing agent to identify tumors and other disease states associated
with LAT-1 up-regulation (41).
Second, acivicin is a cytotoxic agent with antitumor activity
that targets glutamine-dependent amidotransferases in the bio-
synthesis of purines and pyrimidines (42). Trans-stimulation and
dicted ligands. Predicted LAT-1 ligands validated in cis-inhibition assays were
subjected to substrate determination by trans-stimulation of L-leucine efflux
(1 μM unlabeled and 10 nM radiolabeled). (A) Cells were preloaded with
L-leucine, and efflux was induced by subsequent addition of each test
compound at a concentration of 1 mM. Gabapentin, L-leucine, and BCH were
included as positive controls, and glycine and guanfacine were included as
negative controls. (B) The cytotoxic effects of acivicin (100 μM) and 3-iodo-L-
tyrosine (1 mM) against T98G glioblastoma cells stably expressing an shRNA
against LAT-1 (T98G-KD; filled bars) or EV (T98G-EV; open bars) are depicted.
Cell proliferation for both cell lines and treatment conditions were nor-
malized to cell density at treatment day 0 and then to the vehicle control
treatment at 48 h. Each bar represents the mean of three or four separate
experiments, and error bars represent the SEM. Statistical analysis in A was
by one-way ANOVA and Dunnett’s multiple comparison test and in B was by
two-way ANOVA and Bonferroni correction for multiple testing. *P < 0.05.
Substrate determination and cytotoxicity characterization of pre-
Geier et al.PNAS
| April 2, 2013
| vol. 110
| no. 14
cell-proliferation experiments indicated that acivicin is likely a
LAT-1 substrate (Fig. 4), suggesting that LAT-1 can be targeted
for acivicin delivery into tumor cells. Interestingly, acivicin failed
in various clinical trials (e.g., for advanced solid malignancies)
because of CNS-related toxic side effects (38) or insufficient
efficacy (43). Thus, targeting LAT-1 in a tumor with a drug that
is a LAT-1 substrate may not be a rational therapeutic strategy
because LAT-1 would also facilitate entry of the drug into the
CNS. However, design of other cytotoxic substrates of LAT-1,
which are not associated with deleterious CNS effects, may rep-
resent a viable drug development strategy for cancer. Although
cell proliferation experiments indicate that multiple transporters
may mediate acivicin accumulation in cells, the significant dif-
ference in sensitivity to acivicin between T98G-KD and -EV cells
clearly indicates a LAT-1–specific effect on cell proliferation, most
likely by mediating acivicin uptake.
Approach. Although polypharmacology can be exploited to im-
prove the treatment of various nervous system disorders and
cancers, it may also lead to toxicity. Virtual screening against the
LAT-1 model identifies ligands that likely achieve their phar-
macological effect by interacting with multiple proteins. Current
efforts to design reagents, including drugs or chemical tools, for
treating complex diseases include optimizing binding affinities of
one or more molecules against more than one target. Recent
advances in comparative modeling and molecular docking for
ligand discovery, coupled with the determination of a number of
membrane protein structures including transporters, enables us
to target multiple components of a single pathway (e.g., mTOR)
or organ (e.g., the BBB) by using structure-based ligand dis-
covery. Importantly, some of these newly determined structures
represent different protein conformations, allowing in silico
screens of small molecules against comparative models of dif-
ferent conformations to suggest chemically distinct ligands. For
example, a structure-based approach predicted that molecules
binding to a model for the outward-facing conformation of the
GABA transporter 2 were chemically distinct from those pre-
dicted to bind an occluded model (44). Thus, as more structures
of LAT-1 homologs are discovered, our results can be refined to
identify novel LAT-1 ligands for effective therapy and the study
of CNS diseases and cancer.
In summary, we constructed structural models for LAT-1 based
on atomic structures of distantly related prokaryotic homologs.
Two small organic molecule libraries containing endogenous
metabolites and prescription drugs were then virtually screened
against these models. Select top-ranked docking hits were tested
experimentally, and four previously unknown LAT-1 ligands were
identified: 3,5-diiodo-L-tyrosine, 3-iodo-L-tyrosine, fenclonine,
and acivicin. Furthermore, acivicin and 3-iodo-L-tyrosine were
cancer cellline.These findings provide chemical tools toelucidate
the role of membrane transporters as potential drug targets and in
mediating tissue permeability to small organic molecules. Future
studies are needed to further elucidate the mechanism by which
these ligands interact with LAT-1.
Materials and Methods
Comparative Model Construction. LAT-1 was modeled by using MODELLER-
9v11 based on the X-ray structure of the arginine/agmatine transporter AdiC
from E. coli in the outward-occluded arginine-bound conformation [Protein
Data Bank (PDB) ID code 3L1L] (17). We also modeled LAT-1 based on the
structure of ApcT from M. jannaschii in an inward-apo conformation (PDB ID
code 3GI9) (ref. 16; SI Materials and Methods). We relied on a published
alignment (15), as well as alignments obtained from the Promals3D server
(45), where gaps were present primarily, but not only, in the predicted ex-
tracellular loops and were manually refined (SI Materials and Methods and
Fig. S1). For each template structure and alignment, 100 models were gen-
erated by using the standard “automodel” routine of MODELLER-9v11 (46).
The initial models were assessed by using Z-DOPE, a normalized atomic
distance-dependent statistical potential based on known protein structures
(26).For selected LAT-1models, thebindingsite was refinedby repacking the
side chains on a fixed backbone using Scwrl4 (47). The final models for virtual
screening were selected based on their ability to discriminate known ligands
from decoy compounds using enrichment curves derived from ligand-dock-
ing calculations (SI Materials and Methods) (28, 29, 48). These final models
were also evaluated based on residue hydrophobicity (49) and evolutionary
conservation profiles (SI Materials and Methods and Fig. S5) (50).
Virtual Screening and Ligand Docking. Virtual screening against the LAT-1
models was performed by using a semiautomatic docking procedure (29)
relying on DOCK 3.5.54 (51). The docking poses of the database molecules
were ranked by DOCK score, which is a sum of van der Waals, Poisson–
Boltzmann finite-difference electrostatics, and ligand desolvation penalty
terms. Poses of the 500 highest-ranked compounds from each of the docking
screens were inspected by eye to prioritize compounds for experimental
testing (SI Materials and Methods) (28, 48).
Chemical Similarity Calculations. The chemical novelty of the top hits was first
evaluated by using Instant JChem (Version 5.7.0; ChemAxon). Specifically,
we calculated the chemical dissimilarity measure JCDissimilarityCFTanimoto
among the top small-molecule hits and the 44 known LAT-1 ligands from the
databases ChEMBL (52) and UniProt (53), as well as from the literature (1);
predicted ligands with values of >0.7 were classified as chemically novel.
Cell Lines. Stably transfected HEK 293 cells were created by transfecting
pcDNA5/FRT (Invitrogen) vector containing the full-length human LAT-1 cDNA
(HEK-LAT1) and the empty vector (HEK-EV) by using Lipofectamine 2000
(Invitrogen) per the manufacturer’s instructions. Transfected cells were main-
tained in DMEM-H21 containing 10% (vol/vol) FBS, 100 units/mL penicillin, 100
μg/mL streptomycin, and 200 μg/mL hygromycin B at 37 °C and 5% CO2. Stable
LAT-1 knockdown cells were created by infecting 2 × 105T98G GBM cells with
lentivirus produced by the University of California, San Francisco (UCSF) Len-
tiviral RNAi core (54) carrying a pSicoR vector expressing green fluorescent
protein (GFP) and either an anti–LAT-1 shRNA (T98G-KD; Table S3) or empty
vector (T98G-EV) at a multiplicity of infection equal to 10. One week after
infection, GFP+ cells were isolated by using fluorescence-activated cell sorting
(FACS) analysis by the Laboratory for Cell Analysis at the UCSF Comprehensive
Cancer Center.GFP+ T98G-KDand T98G-EV cellswere validated for LAT-1RNA
and functional knockdown as described in SI Materials and Methods. T98G,
T98G-KD, and T98G-EV cells were maintained in DMEM-H21 containing 10%
FBS, 100 units/mL penicillin, and 100 μg/mL streptomycin at 37 °C and 5% CO2.
Inhibition of [3H]-Gabapentin Uptake. Uptake studies were performed as de-
well in poly-D-lysine–coated 24-well (BD Falcon) plates and grown to 80–90%
confluence. Cells were rinsed with prewarmed, sodium-free choline buffer at
and then incubated in 0.3 mL of prewarmed choline buffer containing 1 μM
unlabeled gabapentin and 10 nM [3H]-gabapentin (American Radiolabeled
Chemicals) for 3 min at 37 °C in the presence of 10 and 100 μM test compound
(Sigma-Aldrich). The reaction was terminated by washing cells twice with
1.0 mL of ice-cold choline buffer, followed by addition of 700 μL of lysis buffer
(0.1% SDS vol/vol, 0.1 N NaOH). Intracellular radioactivity was determined by
scintillationcountingandnormalizedperwellofprotein content as measured
by bicinchoninic acid protein assay (Pierce). Concentration-dependent in-
hibition was measured under the same conditions as for the single-point
measurements. Cells were incubated with 0.5, 1, 10.0, 50.0, 100.0, and 200.0
μM 3,5 diiodo-L-tyrosine or 10.0, 50.0, 100.0, 400.0, 800.0, and 1,600.0 μM
acivicin. The concentration at which 50% of [3H]-gabapentin accumulation
was inhibited (IC50) was computed by fitting the data using GraphPad Prism
Trans-stimulation of [3H]-L-Leucine Efflux. Trans-stimulation studies were
performed by monitoring intracellular L-leucine efflux from HEK-LAT1 cells
stimulated by extracellular addition of known or putative LAT-1 substrates.
HEK-LAT1 cells were seeded under the same conditions described for in-
hibition experiments. Cells were rinsed with prewarmed choline buffer and
then preloaded with [3H]-L-Leucine (Perkin-Elmer) by incubating cells in
0.3 mL of prewarmed choline buffer containing 1 μM unlabeled and 10 nM
radiolabeled substrate for 5 min at 37 °C. Uptake was terminated by
washing cells twice with 1.0 mL of ice-cold choline buffer, and [3H]-L-Leucine
efflux was then induced by addition of 1 mM test compound (Sigma-Aldrich)
in prewarmed choline buffer for 1 min at 37 °C. Trans-stimulation was ter-
minated by washing cells twice with 1.0 mL of ice-cold choline buffer,
| www.pnas.org/cgi/doi/10.1073/pnas.1218165110Geier et al.
followed by addition of 700 μL of lysis buffer (0.1% SDS vol/vol, 0.1 N NaOH). Download full-text
Intracellular radioactivity was determined as described above.
Cell Proliferation Assay. T98G-KDand-EVcellswereseededat2.5×103cellsper
well in 96-well plates (Corning Life Sciences), and on the following day cells
were exposed to growth medium containing either drug or vehicle (0.85%
saline solution) for 48 h. Cell density was measured on the treatment day and
48 h after treatment by using the CellTiter-Glo cell viability kit (Promega)
according to the manufacturer’s instructions. Cell lysates were transferred to
white opaque 96-well plates (Corning Life Sciences), and bioluminescence was
measured on a Glomax luminometer (Promega). Proliferation of each cell line
followed by normalization of drug to vehicle treatment.
Statistical Analysis. Data were analyzed by one-way ANOVA followed by
Dunnett’s multiplecomparison test, two-way ANOVA followed by Bonferroni
correction for multiple testing, or two-tailed unpaired t test. Probability
values of <0.05 were considered statistically significant.
technical assistance and maintenance of the computational resources re-
quired for this study; Jonathan Sockolosky for technical assistance in cloning
LAT-1; and University of California, San Francisco ViraCore for the production
of custom lentivirus. The project was supported by National Institutes of
Health Grants R01 GM54762 (to A. Sali), U54 GM074929 and U01 GM61390
(to A. Sali and K.M.G.), P01 GM71790 (to A. Sali), F32 GM088991 (to
A. Schlessinger), and T32 GM007175 (to E.G.G.). We also received funding
for computing hardware from Hewlett Packard, IBM, NetApps, Intel, Ron
Conway, and Mike Homer.
1. Kanai Y, et al. (1998) Expression cloning and characterization of a transporter for
large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98). J Biol
2. Roberts LM, et al. (2008) Subcellular localization of transporters along the rat blood-
brain barrier and blood-cerebral-spinal fluid barrier by in vivo biotinylation. Neuro-
3. Alexander GM, Schwartzman RJ, Grothusen JR, Gordon SW(1994) Effect of plasma levels
of large neutral amino acids anddegreeofparkinsonism onthe blood-to-braintransport
of levodopa in naive and MPTP parkinsonian monkeys. Neurology 44(8):1491–1499.
4. Wang Y, Welty DF (1996) The simultaneous estimation of the influx and efflux blood-
brain barrier permeabilities of gabapentin using a microdialysis-pharmacokinetic
approach. Pharm Res 13(3):398–403.
5. Killian DM, Hermeling S, Chikhale PJ (2007) Targeting the cerebrovascular large
neutral amino acid transporter (LAT1) isoform using a novel disulfide-based brain
drug delivery system. Drug Deliv 14(1):25–31.
6. Gynther M, et al. (2010) Brain uptake of ketoprofen-lysine prodrug in rats. Int J Pharm
7. Kaira K, et al. (2008) Prognostic significance of L-type amino acid transporter 1 ex-
pression in resectable stage I-III nonsmall cell lung cancer. Br J Cancer 98(4):742–748.
8. Kobayashi K, et al. (2008) Enhanced tumor growth elicited by L-type amino acid
transporter 1 in human malignant glioma cells. Neurosurgery 62(2):493–503, discus-
9. Kaira K, et al. (2008) l-type amino acid transporter 1 and CD98 expression in primary
and metastatic sites of human neoplasms. Cancer Sci 99(12):2380–2386.
10. Nicklin P, et al. (2009) Bidirectional transport of amino acids regulates mTOR and
autophagy. Cell 136(3):521–534.
11. Oda K, et al. (2010) L-type amino acid transporter 1 inhibitors inhibit tumor cell
growth. Cancer Sci 101(1):173–179.
12. Ohkawa M, et al. (2011) Oncogenicity of L-type amino-acid transporter 1 (LAT1) re-
vealedby targeted genedisruption inchickenDT40 cells: LAT1isa promising molecular
target for human cancer therapy. Biochem Biophys Res Commun 406(4):649–655.
13. Shennan DB, Thomson J (2008) Inhibition of system L (LAT1/CD98hc) reduces the
growth of cultured human breast cancer cells. Oncol Rep 20(4):885–889.
14. Verrey F, et al. (2004) CATs and HATs: the SLC7 family of amino acid transporters.
Pflugers Arch 447(5):532–542.
15. Gao X, et al. (2009) Structure and mechanism of an amino acid antiporter. Science
16. Shaffer PL, Goehring A, Shankaranarayanan A, Gouaux E (2009) Structure and mech-
anism of a Na+-independent amino acid transporter. Science 325(5943):1010–1014.
17. Gao X, et al. (2010) Mechanism of substrate recognition and transport by an amino
acid antiporter. Nature 463(7282):828–832.
18. Kowalczyk L, et al. (2011) Molecular basis of substrate-induced permeation by an
amino acid antiporter. Proc Natl Acad Sci USA 108(10):3935–3940.
19. Schlessinger A, et al. (2010) Comparison of human solute carriers. Protein Sci 19(3):
20. Fang Y, et al. (2009) Structure of a prokaryotic virtual proton pump at 3.2 A resolu-
tion. Nature 460(7258):1040–1043.
21. Yamashita A, Singh SK, Kawate T, Jin Y, Gouaux E (2005) Crystal structure of a bac-
terial homologue of Na+/Cl—dependent neurotransmitter transporters. Nature
22. Forrest LR, Krämer R, Ziegler C (2011) The structural basis of secondary active trans-
port mechanisms. Biochim Biophys Acta 1807(2):167–188.
23. Shi L, Weinstein H (2010) Conformational rearrangements to the intracellular open
states of the LeuT and ApcT transporters are modulated by common mechanisms.
Biophys J 99(12):L103–L105.
24. Jardetzky O (1966) Simple allosteric model for membrane pumps. Nature 211(5052):
25. Guan L, Kaback HR (2006) Lessons from lactose permease. Annu Rev Biophys Biomol
26. Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein
structures. Protein Sci 15(11):2507–2524.
27. Eramian D, Eswar N, Shen MY, Sali A (2008) How well can the accuracy of comparative
protein structure models be predicted? Protein Sci 17(11):1881–1893.
28. Schlessinger A, et al. (2011) Structure-based discovery of prescription drugs that in-
teract with the norepinephrine transporter, NET. Proc Natl Acad Sci USA 108(38):
29. Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking.
J Med Chem 49(23):6789–6801.
30. Fan H, et al. (2009) Molecular docking screens using comparative models of proteins.
J Chem Inf Model 49(11):2512–2527.
31. Mensch J, Oyarzabal J, Mackie C, Augustijns P (2009) In vivo, in vitro and in silico
methods for small molecule transfer across the BBB. J Pharm Sci 98(12):4429–4468.
32. Smith BJ, et al. (2001) P-glycoprotein efflux at the blood-brain barrier mediates dif-
ferences in brain disposition and pharmacodynamics between two structurally related
neurokinin-1 receptor antagonists. J Pharmacol Exp Ther 298(3):1252–1259.
33. Park S, Sinko PJ (2005) The blood-brain barrier sodium-dependent multivitamin
transporter: a molecular functional in vitro-in situ correlation. Drug Metab Dispos
34. Abbott NJ, Patabendige AA, Dolman DE, Yusof SR, Begley DJ (2010) Structure and
function of the blood-brain barrier. Neurobiol Dis 37(1):13–25.
35. Yamamoto A, Akanuma S, Tachikawa M, Hosoya K (2010) Involvement of LAT1 and
LAT2 in the high- and low-affinity transport of L-leucine in human retinal pigment
epithelial cells (ARPE-19 cells). J Pharm Sci 99(5):2475–2482.
36. Zhang L, Schaner ME, Giacomini KM (1998) Functional characterization of an organic
cation transporter (hOCT1) in a transiently transfected human cell line (HeLa).
J Pharmacol Exp Ther 286(1):354–361.
37. Meier C, Ristic Z, Klauser S, Verrey F (2002) Activation of system L heterodimeric
amino acid exchangers by intracellular substrates. EMBO J 21(4):580–589.
38. Hidalgo M, et al. (1998) A Phase I and pharmacological study of the glutamine an-
tagonist acivicin with the amino acid solution aminosyn in patients with advanced
solid malignancies. Clin Cancer Res 4(11):2763–2770.
39. Delaville C, Navailles S, Benazzouz A (2012) Effects of noradrenaline and serotonin
depletions on the neuronal activity of globus pallidus and substantia nigra pars re-
ticulata in experimental parkinsonism. Neuroscience 202:424–433.
40. Kroemer G, Pouyssegur J (2008) Tumor cell metabolism: cancer’s Achilles’ heel. Cancer
41. Lahoutte T, et al. (2003) Comparative biodistribution of iodinated amino acids in rats:
selection of the optimal analog for oncologic imaging outside the brain. J Nucl Med
42. O’Dwyer PJ, Alonso MT, Leyland-Jones B (1984) Acivicin: a new glutamine antagonist
in clinical trials. J Clin Oncol 2(9):1064–1071.
43. Bonomi P, Finkelstein D, Chang A (1994) Phase II trial of acivicin versus etoposide-
cisplatin in non-small cell lung cancer. An Eastern Cooperative Oncology Group study.
Am J Clin Oncol 17(3):215–217.
44. Schlessinger A, et al. (2012) High selectivity of the γ-aminobutyric acid transporter
2 (GAT-2, SLC6A13) revealed by structure-based approach. J Biol Chem 287(45):
45. Pei J, Kim BH, Grishin NV (2008) PROMALS3D: a tool for multiple protein sequence
and structure alignments. Nucleic Acids Res 36(7):2295–2300.
46. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial
restraints. J Mol Biol 234(3):779–815.
47. Krivov GG, Shapovalov MV, Dunbrack RL, Jr. (2009) Improved prediction of protein
side-chain conformations with SCWRL4. Proteins 77(4):778–795.
48. Carlsson J, et al. (2011) Ligand discovery from a dopamine D3 receptor homology
model and crystal structure. Nat Chem Biol 7(11):769–778.
49. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character
of a protein. J Mol Biol 157(1):105–132.
50. Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N (2010) ConSurf 2010: calculating
evolutionary conservation in sequence and structure of proteins and nucleic acids.
Nucleic Acids Res 38(Web Server issue):W529–W533.
51. Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in
molecular docking. J Chem Inf Model 50(9):1561–1573.
52. Overington J (2009) ChEMBL. An interview with John Overington, team leader,
chemogenomics at the European Bioinformatics Institute Outstation of the European
Molecular Biology Laboratory (EMBL-EBI). Interview by Wendy A. Warr. J Comput
Aided Mol Des 23(4):195–198.
53. Apweiler R, et al. (2010) The Universal Protein Resource (UniProt) in 2010. Nucleic
Acids Res 38(Database issue):D142–D148.
54. Wang X, McManus M (2009) Lentivirus production. J Vis Exp (32):e1499.
55. Chen Y, Zhang S, Sorani M, Giacomini KM (2007) Transport of paraquat by human
organic cation transporters and multidrug and toxic compound extrusion family.
J Pharmacol Exp Ther 322(2):695–700.
Geier et al.PNAS
| April 2, 2013
| vol. 110
| no. 14