Accurate prediction of glucuronidation of structurally diverse phenolics by human UGT1A9 using combined experimental and in silico approaches

Article abstract of DOI:10.1007/s11095-012-0666-z

Purpose: Catalytic selectivity of human UGT1A9, an important membrane-bound enzyme catalyzing glucuronidation of xenobiotics, was determined experimentally using 145 phenolics and analyzed by 3D-QSAR methods. Methods: Catalytic efficiency of UGT1A9 was determined by kinetic profiling. Quantitative structure activity relationships were analyzed using CoMFA and CoMSIA techniques. Molecular alignment of substrate structures was made by superimposing the glucuronidation site and its adjacent aromatic ring to achieve maximal steric overlap. For a substrate with multiple active glucuronidation sites, each site was considered a separate substrate. Results: 3D-QSAR analyses produced statistically reliable models with good predictive power (CoMFA: q ²=0.548, r²=0.949, r _pred ² =0.775; CoMSIA: q²=0.579, r²=0.876, r_pred² =0.700). Contour coefficient maps were applied to elucidate structural features among substrates that are responsible for selectivity differences. Contour coefficient maps were overlaid in the catalytic pocket of a homology model of UGT1A9, enabling identification of the UGT1A9 catalytic pocket with a high degree of confidence. Conclusion: CoMFA/CoMSIA models can predict substrate selectivity and in vitro clearance of UGT1A9. Our findings also provide a possible molecular basis for understanding UGT1A9 functions and substrate selectivity.

Full text of DOI:10.1007/s11095-012-0666-z

Pharm Res (2012) 29:1544–1561
DOI 10.1007/s11095-012-0666-z
RESEARCH PAPER
Accurate Prediction of Glucuronidation of Structurally Diverse
Phenolics by Human UGT1A9 Using Combined Experimental
and In Silico Approaches
Baojian Wu & Xiaoqiang Wang & Shuxing Zhang & Ming Hu
Received: 17 October 2011 /Accepted: 3 January 2012 /Published online: 3 February 2012
#
Springer Science+Business Media, LLC 2012
.
.
.
ABSTRACT
KEY WORDS CoMFA CoMSIA glucuronidation
.
Purpose Catalytic selectivity of human UGT1A9, an impor-
tant membrane-bound enzyme catalyzing glucuronidation of
xenobiotics, was determined experimentally using 145
phenolics and analyzed by 3D-QSAR methods.
homology modeling UGT1A9
ABBREVIATIONS
2D/3D
ADME
CLint
2-dimensional/3-dimensional
absorption distribution, metabolism, and elimination
intrinsic clearance
Methods Catalytic efficiency of UGT1A9 was determined by
kinetic profiling. Quantitative structure activity relationships
were analyzed using CoMFA and CoMSIA techniques. Molecular
alignment of substrate structures was made by superimposing the
glucuronidation site and its adjacent aromatic ring to achieve
maximal steric overlap. For a substrate with multiple active
glucuronidation sites, each site was considered a separate substrate.
Results 3D-QSAR analyses produced statistically reliable models
with good predictive power (CoMFA: q200.548, r200.949,
rpred200.775; CoMSIA: q200.579, r200.876, rpred200.700).
Contour coefficient maps were applied to elucidate structural
features among substrates that are responsible for selectivity differ-
ences. Contour coefficient maps were overlaid in the catalytic
pocket of a homology model of UGT1A9, enabling identification
of the UGT1A9 catalytic pocket with a high degree of confidence.
Conclusion CoMFA/CoMSIA models can predict substrate
selectivity and in vitro clearance of UGT1A9. Our findings
also provide a possible molecular basis for understanding
UGT1A9 functions and substrate selectivity.
CoMFA Comparative Molecular Field Analysis
CoMSIA Comparative Molecular Similarity Indices Analysis
CYPs
Km
MS
PLS
QSAR
cytochrome P450
Michaelis-Menten constant
mass spectroscopy
partial least squares
quantitative structure-activity relationship
UDPGA uridine diphosphoglucuronic acid
UGTs
UPLC
Vmax
UDP-glucuronosyltransferases
ultra performance liquid chromatography
maximal velocity
INTRODUCTION
The high rate of attrition in drug development has become
a conundrum in pharmaceutical industry. One root cause
Electronic supplementary material The online version of this article
(doi:10.1007/s11095-012-0666-z) contains supplementary material, which
is available to authorized users.
:
B. Wu M. Hu (*)
S. Zhang
Department of Pharmacological and Pharmaceutical
Sciences, College of Pharmacy, University of Houston
1441 Moursund St., Houston, Texas 77030, USA
e-mail: mhu@uh.edu
Department of Experimental Therapeutics
The University of Texas M. D. Anderson Cancer Center
1515 Holcombe Blvd., Houston, Texas 77030, USA
X. Wang
Plant Biology Division, Samuel Roberts Noble Foundation
2510 Sam Noble Parkway
Ardmore, Oklahoma 73401, USA

In Silico Predicting UGT1A9-Mediated Glucuronidation
1545
for attrition is the unfavorable absorption, distribution,
metabolism, and elimination (ADME) characteristics (1).
Accordingly, there are considerable interests in developing
either computational (in silico) or in vitro ADME methods to
aid the lead compound selection (2,3). A main advantage of a
computational model is that it allows the ADME properties
predicted for a new structure without experimental determi-
nation. This merit is rather tempting when thousands of
(even more) drug candidates need to be screened. In fact,
the use of ADME modeling/prediction has become an
effective approach for industry to reduce late-stage attrition in
drug discovery (2).
provides substantial insights into the UDPGA binding and
possible catalytic mechanism (13,14). Due to the lack of
structural information of the N-terminus (for substrate
binding), relatively little is known about the specific
molecular interactions that govern UGT selectivity
for its substrates. Nonetheless, Miners and colleagues
demonstrate that substrate hydrophobicity and the
spatial arrangement of two hydrophobic regions (close
to the glucuronidation site) are important for substrate
recognition by several human UGT isoforms based on
(2D/3D) regression models and a pharmacophore model
(8,15,16).
Predicting the metabolic fate of a drug candidate is an
indispensible component of ADME evaluation. Extensive
metabolism may result in poor bioavailability and/or drug
inefficacy, whereas poor metabolism can be associated
with drug toxicity. Significant advances have been made
to predict cytochrome p450 (CYPs)-mediated metabolism
using molecular modeling techniques such as two dimensional/
three dimensional (2D/3D) quantitative structure-activity
relationships (QSAR), pharmacophore, and homology
modeling (4,5). And a number of software packages to
predict CYP metabolism have been commercialized (e.g.,
MetaSite, Simcyp) (6,7). However, relatively fewer efforts
are directed to develop such models and to characterize
structural features of substrates for other important drug
metabolizing enzymes such as UDP-glucuronosyltransferases
(UGTs) (8).
UGTs catalyze the glucuronidation reaction which has
been recognized as a prevailing metabolic and detoxifica-
tion pathway for many drugs, sometimes targeting the
products (hydroxylated phenols) of CYP-mediated metabo-
lism (3). Human UGTs constitute a large family of enzymes,
and are systematically classified into four subfamilies, UGT1,
UGT2, UGT3, and UGT8 (9). One unique feature about
human UGTs is that these enzymes show remarkably broad
substrate specificity. A UGT substrate usually contains one
nucleophilic group (i.e., hydroxyl (-OH) group, carboxylic
acid (-COOH), and amines) to which the glucuronic acid
derived from the cofactor UDP-glucuronic acid (UDPGA) is
transferred. Although it is rare, the acidic carbons and thiol
group can also be glucuronidated (10). Another important
feature of human UGTs is that they exhibit vast overlapping
substrate specificity; this has challenged the identification
of specific probe substrates (and possibly inhibitors) for a
particular UGT enzyme (11). Lacking of an in vivo selective
UGT probe is a significant barrier to in vivo glucuronidation
studies with respect to evaluation of the role of a UGT
enzyme (12)
UGT1A9 is a major UGT1A isoform in human liver
(17). Its role in clearance of both chemotherapeutic and
non-chemotherapeutic drugs (e.g., SN-38, tamoxifen and
acetaminophen) and in detoxification of carcenogens (e.g.,
NNAL and benzo[a]pyrene) has been widely recognized
(18–20). Moreover, UGT1A9 polymorphisms (e.g., M33T,
C183G and V167A) are being identified; those genetic
variants have impaired glucuronidation activity that might
have clinical implications (19,21). Given its importance in
clearance of many xenobiotics/drugs, UGT1A9 has received
considerable studies in recent years (22–26). The aim of this
work is to enhance our understanding of molecular inter-
actions of UGT1A9 with its substrates, and to develop a
more generalized model that can be used to predict
UGT1A9-mediated glucuronidation of novel drug candi-
dates. To this end, ligand-based three-dimensional quantita-
tive structure-activity relationship (3D-QSAR) methods (i.e.,
Comparative Molecular Field Analysis (CoMFA) and
Comparative Molecular Similarity Indices Analysis
(CoMSIA)) were applied to yield statistically reliable
models with good predictive power. The correlation
results obtained by CoMFA/CoMSIA were graphically
interpreted in terms of field contribution maps. The
catalytic pocket (or binding pocket) geometry and its
physiochemical properties indicated from the CoMFA/
CoMSIA analyses were compared with that from a homology
model of UGT1A9.
MATERIALS AND METHODS
Materials
Expressed human UGT1A9 isoform (SupersomesTM) was
purchased from BD Biosciences (Woburn, MA). 4-
Methylumbelliferone-glucuronide, baicalin (baicalein-7-O-
glucuronide), uridine diphosphoglucuronic acid (UDPGA),
alamethicin, D-saccharic-1,4-lactone monohydrate, and
magnesium chloride were purchased from Sigma-Aldrich
(St Louis, MO). Ammonium acetate was purchased from
J.T. Baker (Phillipsburg, NT). SN-38-glucuronide, and
A complete three dimensional structure of human UGTs
is not yet available. Only a partial crystal structure of
UGT2B7 (C-terminal domain) was resolved in 2007 (13).
This structure, combined with molecular modeling studies,

1546
Wu, Wang, Zhang and Hu
propofol-glucuronide were obtained from Toronto Research
Chemicals (North York, Ontario, Canada). Wogonoside
(wogonin-7-O-glucuronide) was purchased from Chengdu
Mansite Pharmaceutical Co. Ltd. (Chengdu, China). All
(145) UGT1A9 substrates (Table I) were obtained from
commercial sources. The chemical structures of these
UGT1A9 substrates are shown in Fig. S1 (Supplementary
Material).
Identification of Glucuronide and Glucuronidation site
Glucuronide formation by UGT1A9 was confirmed via the
hydrolysis (by β-D-glucuronidase) experiment and the
molecular weight detection by UPLC/MS/MS, a standard
procedure in our lab (27,28). The site (-OH group) of
glucuronidation is an important information that was
incorporated into CoMFA/CoMSIA analyses (see later
section). However, regular MS/MS is unable to probe
the site of glucuronidation (or deduce the exact structure
of a glucuronide); because the glucuronic acid moiety is
readily detached from a glucuronide once collision energy is
applied. For a substrate with a single -OH group, the site of
glucuronidation has to be this -OH group and no addition
effort is needed. By contrast, for a substrate containing
multiple -OH groups, three methods were used to eluci-
date a glucuronidation site. That is (1) for flavones and
flavonols, the site of glucuronidation was assigned by the
“UV spectrum maxima (λmax) shift method” (30). This
method is based on the characteristic UV shifts caused
by glucuronic acid substitution on a particular -OH group;
(2) the information regarding the site of glucuronidation was
collated from the literature, the references are provide in
Table S1 (Supplementary Material); (3) the preferred site
of glucuronidation was assigned as 2′-OH for 7 phloretin,
and 3-OH for 23 tyrphostin B42; these assignments are
uncertain, even though they demonstrate a good consistency
in later 3D-QSAR analyses.
Enzyme Assays
Enzyme assays using expressed UGT1A9 were conducted
following a standard protocol as described in our earlier
publications (27,28). Briefly, the incubation procedures were
as follows: [1] UGT1A9 (13–53 μg/ml as optimum for the
reaction), magnesium chloride (0.88 mM), saccharolactone
(4.4 mM), alamethicin (0.022 mg/ml), different concentra-
tions of substrates in a 50 mM potassium phosphate buffer
(pH 7.4), and UDPGA (3.5 mM, added last) were mixed;
[2] the mixture (final volume, 200 μl) was incubated at
37°C for a predetermined period of time (15–120 min);
and [3] the reaction was stopped by the addition of 50 μl
of 94% acetonitrile/6% glacial acetic acid. Great effort
was made to ensure that the rates of metabolite forma-
tion were linear with respect to time (15–120 min) and
protein concentration (13–53 μg/ml), so we can obtain
accurate and reliable initial metabolism rates. Apparent
glucuronidation rates were calculated as the amount of
glucuronide(s) formed per protein concentration per reaction
time (or pmol/mg/min). All experiments were performed in
triplicates.
Kinetics Analysis
Kinetic data points were model-fitted using a nonlinear
least-squares regression method performed by GraphPad
Prism V5 for Windows (GraphPad Software, San Diego,
CA). The model used to fit a kinetic profile was carefully
selected based on a diagnostic plot (i.e., Eadie-hofstee plot).
Overall, Michaelis-Menten equation (Eq. 1), the substrate
inhibition equation (Eq. 2), and a biphasic kinetic model
(Eq. 3) were used. The intrinsic clearance CLint representing
the catalytic efficiency was calculated as Vmax/Km for Eqs. 1
and 2 fitting and Vmax1/Km1 for Eq. 3 fitting. Representa-
tive fitting of the equations to kinetic data was demonstrated
in Fig. 1.
UPLC Analysis
The Waters ACQUITY UPLC (Ultra performance liquid
chromatography) system was used to analyze the UGT1A9
substrates and their glucuronides (27,28). Except for the
elution gradient, all other UPLC conditions (such as the
mobile phase and column) were kept the same. The gradient
was adjusted carefully to ensure a good separation of a
substrate from its glucuronide(s) and of a glucuronide isomer
from the other one(s). Quantitation of propofol glucuronide,
SN-38 glucuronide, 4-methylumbelliferone-glucuronide,
baicalein-7-O-glucuronide, and wogonin-7-O-glucuronide
was made with the authentic reference standards. For all
other glucuronides without commercial availability, the
conversion factor of glucuronide vs. aglycone was deter-
mined using our published method (29). Quantitation of
these glucuronides was based on the standard curve of
the parent compound and further calibrated with the
conversion factors.
Vmax½Sꢀ
V ¼
V ¼
ð1Þ
ð2Þ
Km þ ½Sꢀ
Vmax½Sꢀ
Km þ ½Sꢀ þ
2
½Sꢀ
Ksi

In Silico Predicting UGT1A9-Mediated Glucuronidation
1547
Table I Experimentally Determined Kinetic Parameters for UGT1A9-Mediated Glucuronidation of 145 Compounds, Including 2 Catecins (No.1-2),
5 Chalcones (No.3-7), 1 Chromone (No.8), 6 Courmarins (No.9-14), 3 Curcumins (No.15-17), 6 Aromatic Hydrocarbons (No.18-23), 4 Flavanones (No.24-27),
31 Flavones (No.28-58), 36 Flavonols (No.59-94), 3 Hydroxycinnamic Acids (No.95-97), 11 Isoflavones (No.98-108), 27 Phenols (No.109-135),
and 7 Other Compounds (No.136-145). The Chemical Structures (Including the SMILES Codes) of All Compounds are Shown in Fig. S1
(Supplementary Material). The Site of Glucuronidation is Indicated in the Parenthesis. For a Compound with Multiple Glucuronides Generated, a
Lower-Case Letter is Appended for a Distinction
No.
Name
Km
μM
Vmax
pmol/mg/min
CLint (Vmax/Km)
μl/mg/min
Log (CLint)
(Actual)
Log (CLint)
(CoMFA)
Log (CLint)
(CoMSIA)
1.a
1.b
2
(-)-Epigallocatechingallate (3′-OH)
(-)-Epigallocatechingallate (4″-OH)
(-)-Epigallocatechin(3′-OH)
2-Hydroxychalconec
18.7
15.3
135
2955
9984
5130
552
158
646
38
2.20
2.81
1.58
3.38
1.53
3.12
2.29
2.87
3.17
0.91
1.61
2.48
0.59
1.07
1.40
2.28
1.74
1.14
0.90
3.01
0.82
1.96
2.46
1.31
3.01
2.14
3.18
1.31
2.99
2.61
3.18
2.27
3.66
3.22
2.66
1.83
2.93
2.09
3.54
2.89
2.96
1.53
3.22
3.57
3.08
2.48
2.32
2.72
1.40
3.83
1.68
2.80
1.95
2.88
3.21
1.37
1.53
2.43
1.06
1.04
1.69
2.60
1.59
1.37
1.04
3.10
1.97
1.95
1.89
1.15
3.02
2.27
2.88
1.29
2.99
2.45
3.35
2.44
3.43
3.25
2.66
1.83
3.25
2.09
2.93
3.05
3.07
1.30
3.28
3.44
3.23
2.38
2.42
3.36
1.90
3.36
1.32
2.40
1.97
2.64
3.14
1.62
1.46
2.06
0.84
1.69
1.67
2.56
1.60
1.16
0.68
3.05
1.01
2.04
1.55
1.06
3.16
2.63
2.58
1.39
3.31
2.49
2.98
2.26
3.43
3.22
2.63
2.11
2.84
2.77
2.86
3.14
3.18
1.64
3.18
3.39
2.92
2.74
3
0.23
1.62
0.77
1.72
0.63
0.43
82.8
3.37
12
2400
34
4
4-Hydroxychalconec
54.8
1008
339
5
2′-Hydroxychalconec
4′-Hydroxychalconec
Phloretin (2′-OH)c
Phloretin (4′-OH)c
1309
197
741
1479
8
6
7.a
7.b
8
467
636
7-Hydroxychromone
668
9
3,4-Diphenyl-7-hydroxycoumarin
4-Methylumbelliferone
137
41
10
11
12
13
14
15
16
17
18
19
20
21
22
23.a
23.b
24
25
26.a
26.b
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41.a
41.b
3653
225
304
4
4-Hydroxy-6-methylcoumarin
6-Hydroxy-7-methoxyl-4-phenylcoumarin
8-Hydroxywarfarin
57.8
312
3744
9229
1064
1958
258
12
369
25
Scopoletin
5.53
35.4
18.6
257
192
55
Curcumin
Demethoxycurcumin
Bisdemethoxycurcumin a
14
2070
2928
2086
5292
72.1
39.3
3677
2008
5001
1687
4192
2444
5001
74.4
7494
2079
926
8
Emodin
Endoxifen a
2.87
316
1020
6.6
Enterolactone (3-OH)
Naphtholc
57.4
0.25
1.93
3.60
14.5
3.34
83.4
4.29
6.00
3.34
0.40
1.64
1.25
2.02
1.46
0.45
0.96
1.87
3.01
4.49
2.27
1.15
0.23
2.45
2.44
92
288
20
Raloxifene (6-OH)
Tyrphostin B42 (3-OH)
Tyrphostin B42 (4-OH) a
7-Hydroxyflavanone a
1020
138
1497
20
4′-Hydroxy-3-methoxyflavanone
Hesperetin (3′-OH)
977
407
1497
186
4570
1663
458
67.6
849
123
3450
775
910
34
Hesperetin (7-OH)
Narigenin (7-OH)
2′-Hydroxyflavone
3,4′-Dimethoxy-5,7,3′-trihydroxyflavone (7-OH)
3′-Benzyloxy-5,7-dihydroxy-3,4′-dimethoxyflavone (7-OH)
3′-Hydroxyflavone
4′-Hydroxyflavone
98.7
382
5,7-Dihydroxy-3′,4′,5′-trimethoxyflavone (7-OH)
5-Hydroxyflavone
6,3′,4′-Trihydroxyflavone (3′-OH)b
6,7,3′-Trihydroxflavone (7-OH)
6,7-Dihydroxyflavone (7-OH)
6-Hydroxyflavone
118
6470
2333
4090
76.1
1902
862
6-Methoxyluteolin (7-OH)
5,7,2′-Trihydroxyflavone (7-OH) a
7,3′,4′-Trihydroxyflavone (3′-OH)
7,3′,4′-Trihydroxyflavone (4′-OH)
1654
3748
1192
305
2921
744

1548
Wu, Wang, Zhang and Hu
Table I (continued)
No.
Name
Km
μM
Vmax
pmol/mg/min
CLint (Vmax/Km)
μl/mg/min
Log (CLint)
(Actual)
Log (CLint)
(CoMFA)
Log (CLint)
(CoMSIA)
41.c
42
7,3′,4′-Trihydroxyflavone (7-OH)
7,2′-Dihydroxyflavone (7-OH) a
7,3′-Dihydroxyflavone (7-OH)
7-Hydroxy-2′-methoxyflavone
7-Hydroxy-3-methylflavone
7-Hydroxy-3′-methoxyflavone a
7-Hydroxy-4′-methoxyflavone
7-Hydoxy-5-methylflavone
7-Hydroxyflavone
6.38
0.65
29.6
2.21
1.46
25.3
3.37
0.94
3.59
1.93
0.70
0.25
0.91
0.56
37.5
0.17
0.33
2.38
1.27
0.67
0.11
0.25
0.31
0.10
0.13
0.62
0.062
0.63
1.61
0.22
1.50
1.03
1.16
3.37
0.52
0.21
0.063
0.059
0.13
0.082
0.043
0.074
0.19
0.27
0.30
0.066
2.46
0.63
0.74
0.52
820
128
2.11
2.77
2.74
3.00
3.30
1.02
2.36
3.76
3.13
3.23
3.88
3.96
3.48
3.41
0.77
3.58
3.32
2.94
3.60
1.89
4.44
3.87
4.34
4.34
4.14
4.32
5.04
3.66
1.00
4.32
3.13
3.16
1.00
2.86
3.92
4.25
4.66
4.51
4.40
4.36
4.71
4.86
4.66
4.57
3.84
4.96
2.00
3.38
3.55
3.49
2.70
2.74
2.85
3.04
3.19
2.81
3.00
3.34
2.88
2.96
3.37
3.56
3.16
2.63
0.57
3.56
3.38
3.46
3.98
2.68
4.31
4.05
4.31
4.21
3.89
4.42
4.34
3.67
1.15
3.69
3.09
3.48
1.22
4.00
3.60
4.10
4.56
4.24
4.35
4.56
4.97
4.97
4.47
4.36
4.01
4.82
2.42
3.90
3.37
3.63
2.75
2.94
2.86
2.82
2.83
2.80
3.24
3.03
2.90
3.24
3.64
3.34
3.09
2.99
1.02
3.08
3.20
3.32
3.63
3.34
4.30
3.88
3.97
3.92
3.77
4.34
4.29
3.70
2.17
4.18
3.05
3.79
2.37
4.33
3.91
3.47
4.58
4.47
4.59
4.46
5.05
4.79
4.60
4.28
4.15
4.63
3.10
3.95
3.33
3.54
381
586
43
16166
2228
2929
132
546
44
1008
2006
5
45
46
47
764
227
48
5369
4895
3289
5310
4537
2765
5361
219
5712
1364
1704
7586
18148
3038
9573
5.8
49
50
Apigenin (7-OH)
Baicalein (OH)b
51
52
Chrysin (7-OH)
53
Chrysoeriol (7-OH)
54
Diosmetin (7-OH)
55
Flavopiridol (7-OH)
56.a
56.b
57
Luteolin (3′-OH)
660
3870
2100
866
Luteolin (7-OH)
700
OroxylinA (7-OH)
2060
5070
52
58
Wogonin (7-OH)
3992
78
59
3,2′-Dihydroxyflavone (3-OH)
3,3′-Dihydroxyflavone (3-OH)a
3,5-Dihydroxyflavone (3-OH)a
3,6,4′-Trihydroxyflavone (3-OH)a
3,3′,4′-Trihydroxyflavone (3-OH)
3,4′-Dihydroxyfavone (3-OH)
3,6-Dihydroxyfavone (3-OH)
3-Hydroxy-6-methylflavone a
3,7-Dihydroxy-3′,4′,5′-trimethoxyflavone (3-OH)
3,7-Dihydroxy-3′,4′,5′-trimethoxyflavone (7-OH) a
3,7-Dihydroxyfavone (3-OH)b
3,7-Dihydroxyfavone (7-OH)
3,7-Dihydroxy-3′,4′-dimethoxyflavone (3-OH)
3,7-Dihydroxy-3′,4′-dimethoxyflavone (7-OH)
3,7,3′-Trihydroxyflavone (3-OH) a
3-Hydroxy-2′,3′-dimethoxyflavone
3-Hydroxy-2′-methoxyflavone
3-Hydroxy-3′-methoxyflavone
3-Hydroxy-4′-methoxyflavone
3-Hydroxy-5,7-dimethoxyflavone
3-Hydroxy-5-methoxyflavone
3-Hydroxy-6,4′-dimethoxyflavone
3-Hydroxy-6-methoxyflavone
3-Hydroxy-7,4′-dimethoxyflavone
3-Hydroxy-7-methoxyflavone
3-Hydroxyflavone
60
3100
1900
6800
2200
1800
13000
6750
2872
289
29000
7400
22000
22000
14000
21000
110000
4551
180
61
62
63
64
65
66
67.a
67.b
68.a
68.b
69.a
69.b
70
4600
2040
753
21000
1360
731
11.6
764
10
227
71
4400
3800
2900
1900
3300
1900
2200
5400
8700
10000
2100
6000
246
16000
18000
46000
32000
26000
24000
51000
73000
46000
37000
7000
91000
100
72
73
74
75
76
77
78
79
80
81
82
3-Hydroxy-6-methyl-3′,4′-methylenedioxyflavone
a
83
Datiscetin (3-OH)
84.a
84.b
84.c
Fisetin (3-OH)
Fisetin (3′-OH)
Fisetin (4′-OH)
1500
2620
1620
2300
3520
3090

In Silico Predicting UGT1A9-Mediated Glucuronidation
Table I (continued)
1549
No.
Name
Km
μM
Vmax
pmol/mg/min
CLint (Vmax/Km)
μl/mg/min
Log (CLint)
(Actual)
Log (CLint)
(CoMFA)
Log (CLint)
(CoMSIA)
85.a
85.b
86
Galangin (3-OH)b
Galangin (7-OH)
0.68
0.54
1.47
0.32
0.32
3.87
0.68
0.61
0.67
0.64
0.36
0.90
0.85
0.36
2.67
0.23
0.98
1.61
884
7400
2560
5885
12000
1900
870
11000
4740
4003
38000
6000
220
60
4.04
3.68
3.60
4.59
3.78
2.34
1.77
2.90
3.36
3.69
3.97
3.32
3.65
3.86
2.63
4.65
3.87
2.26
0.30
1.82
1.04
0.30
2.57
3.57
2.53
1.89
0.91
0.57
1.48
2.79
2.44
1.87
2.25
2.00
1.18
1.98
1.78
0.92
1.87
1.18
1.87
1.78
1.18
1.39
1.56
1.15
1.83
1.34
1.15
1.00
3.71
3.77
3.89
4.13
3.60
3.36
2.33
3.33
3.25
3.69
3.91
3.49
3.52
3.59
2.67
4.34
4.11
2.05
1.50
1.62
1.50
0.51
2.44
3.76
2.17
2.17
0.89
0.44
1.48
2.96
2.56
1.57
2.20
1.53
1.45
1.44
1.40
1.25
1.45
1.56
1.40
1.62
1.01
1.30
1.50
1.40
1.90
1.50
1.29
1.06
3.91
3.50
3.76
3.49
3.54
3.41
2.73
3.39
3.73
3.30
3.68
3.44
3.24
3.80
2.94
3.84
3.46
2.88
0.09
1.16
0.86
0.50
2.21
3.34
1.93
1.78
0.74
0.68
1.48
2.23
2.33
1.29
2.42
1.49
1.53
1.60
1.11
1.57
1.39
1.79
1.35
1.17
1.41
1.41
1.92
1.61
1.38
1.33
1.48
1.55
Geraldol (3-OH)
87
Isorhamnetin (3-OH)
Kaempferol (3-OH)
Kaempferol (7-OH) a
Morin (3-OH)
88.a
88.b
89
40
90.a
90.b
90.c
91.a
91.b
91.c
92.a
92.b
93
Myricetin (3-OH)
490
800
2290
4930
9200
2100
4490
7300
430
45000
7333
178
0.4
Myricetin (3′-OH)b
Myricetin (4′-OH)
Quercetin (3-OH) a
Quercetin (3′-OH)
Quercetin (7-OH)
Resokaempferol (3-OH)
Resokaempferol (7-OH) a
Rhamnetin (3-OH)
Syringetin (3OH)
1530
3170
3300
1880
3820
2600
1150
10000
7810
289
94.a
94.b
95
Syringetin (7OH)
Ferulic Acid a
372
96
Isoferulic Acid
237
15640
6204
2024
1143
5299
379
66
97.a
97.b
98
Caffeic Acid (3-OH)
Caffeic Acid (4-OH)
7-Hydroxy-6-methoxyisoflavone
8-Hydroxy-7-methoxyisoflavone
Biochanin A (7-OH) a
Daidzein (7-OH)
564
11
1012
3.08
1.42
1.13
14.3
77.7
213
2
371
3732
335
77
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
1107
628
Dihydrodaidzein (7-OH)
Equol (7-OH)
8.1
794
3.7
Formononetin (7-OH)
Genistein (7-OH)
Glycitein (7-OH)
4.59
2.09
1.47
3.12
1.29
30.9
43.1
34.0
18.2
454
139
30
1290
403
617
274
75
Maackiain
234
Prunetin (5-OH)
230
178
99
ab
4-Bromophenol
3057
647
4-n-Butylphenol
4-Chlorophenol
4-Cyclopentylphenolb
4-Ethoxyphenol
15
3230
1083
3761
1179
1680
3034
2184
2310
1433
2902
4438
2441
1236
1155
1560
95
60
8.3
ab
4-Ethylphenol
15.8
112
75
a
4-Fluorophenol
15
4-Iodophenol
20.5
36.4
154
148
60
4-Isopropylphenol
4-Methoxyphenol
4-Methylphenol
15
57.3
80.6
317
25
4-Nitrophenolb
36
4-Hydroxyacetophenone
4-Hydroxybenzophenone
4-Phenylphenol
14
35.9
56.2
82.5
156
68
22
4-Phenylazophenol
4-Propoxyphenol
14
10

1550
Wu, Wang, Zhang and Hu
Table I (continued)
No.
Name
Km
μM
Vmax
pmol/mg/min
CLint (Vmax/Km)
μl/mg/min
Log (CLint)
(Actual)
Log (CLint)
(CoMFA)
Log (CLint)
(CoMSIA)
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
4-n-Propylphenol
4-sec-Butylphenol
4-tert-Butylphenol a
Butyl-4-hydroxybenzoate
Ethyl-4-hydroxybenzoate
Eugenol
47.6
87.5
78.4
84.1
74.5
25
1999
3237
5253
2691
3055
1004
5454
148
42
1.62
1.57
1.83
1.51
1.61
1.60
1.73
0.76
1.48
1.68
3.33
0.05
1.8
1.49
1.66
1.90
1.51
1.43
1.80
1.61
0.86
1.54
1.85
3.34
-0.09
2.23
0.89
4.10
-0.68
1.99
0.90
0.68
2.79
1.39
1.19
1.47
1.91
1.97
1.95
1.98
1.32
2.08
1.02
2.88
0.52
2.14
0.62
4.43
-0.50
1.92
0.66
0.41
2.08
a
37
67
32
41
40
Methyl-4-hydroxybenzoate
Propofol
101
26
54
6
Propyl-4-hydroxybenzoate
Bisphenol A
Combretastatin A4b
152
91.8
4.18
35.9
3.09
0.78
8.64
268
71.5
748
26.4
43.4
4560
4420
9009
40.4
30
48
2155
1.1
63
Pterostilbene
Resveratrol (3-OH)
A-769662 (6-OH)
Entacapone (3-OH)
Ezetimibe
196
10.8
14
1.14
4.01
-0.56
1.96
0.90
0.44
2.62
88400
73.6
10233
0.30
91
Mycophenolic Acid
Psilocin
6538
5984
73.1
8
SN-38
Tolcapone (3-OH) a
3
18100
417
a These 25 compounds were used as a test set and not included in the derivation of model equations
b Described using the substrate inhibition equation (Eq. 2). Ksi value is not shown because it is unimportant in calculation of CLint (please see “Materials and
Methods”)
c Described using a biphasic kinetics model (Eq. 3). Km1 and Vmax1 values are shown in the columns Km and Vmax, respectively. CLint2 value is not shown
because it is unimportant in calculation of CLint (please see “Materials and Methods”)
2
found to be the glucuronidation site and its adjacent aromatic
Vmax 1½Sꢀ þ CLint2½Sꢀ
V ¼
ð3Þ
ring (Fig. 2). All other substrates were then aligned to super-
impose these two features. Further, a maximal steric overlap
was used to determine the best alignment when more than
one alignment was possible. In the case of a substrate with
multiple active glucuronidation sites, more than one structural
pose was aligned corresponding to the glucuronidation at
each site. This treatment rendered a final total 166 aligned
structure conformations (Table I). The structural diversity
of the aligned ligands is shown in Fig. 3a. (The atomic
coordinates of all molecules of the dataset are available
from the authors upon request, bwu3@uh.edu).
Km1 þ ½Sꢀ
Molecular Alignment
All substrate structures were prepared using SYBYL 8.0
(Tripos, US). Carboxylate groups were considered to be
deprotonated. Energy minimizations were performed using
the Tripos force field with partial atomic charges (assigned
by Gasteiger-Hückel method). One of the most important
factors affecting the quality of a model is the alignment of
the individual molecules. The frequently used alignment
methods (i.e., substructure overlap, pharmacophore overlap,
and docking) are not suitable for this study, because a
common core of atoms or a common features pharmacophore
cannot be defined for the 145 structurally diverse substrates
(Table I) and the protein structure is not available. To achieve
our goals, we performed a flexible alignment of three
most active substrates (66: 3-hydroxy-6-methylflavone,
140: entacapone; 52: chrysin) with a constraint that the
glucuronidation site must be overlaid. The important
common features of the most active substrates were
CoMFA and CoMSIA Analyses
The whole data set was divided into two parts, the training
set (n0141) and the test set (n025) (Table I). The training set
and test set molecules was classified to ensure that both sets
could cover the whole range of glucuronidation activity and
structural diversity studied. Other than these two criteria,
the compounds were randomly assigned. The training set
was used for model building and the test set for an external
validation of the model. All comparative molecular field

In Silico Predicting UGT1A9-Mediated Glucuronidation
1551
Fig. 1 Representative fitting of
a
Eq.1fit
Km = 3.34µM, Vmax = 5001 pmol/mg/min
the model equations (eqs. 1–3)
to kinetic data of UGT1A9 with its
substrates, both rate plot (left)
and Eadie-Hofstee plot (right)
are given. (a) Eq. 1
(Michaelis-Menten model) is
used to describe glucuronidation
of 27 naringenin by UGT1A9. (b)
Eq. 2 (a substrate inhibition
model) is used to describe
glucuronidation of 136
combrestatin A4 by UGT1A9. (c)
Eq. 3 (a biphasic model) is used to
describe glucuronidation of 21
1-naphthol by UGT1A9. Points
are experimentally determined
values, while the solid lines show
the computer-derived curves of
best fit.
Eq.2 fit
Km = 4.18µM, Vmax = 9009 pmol/mg/min,Ksi = 115 µM
b
K
m1 = 0.31 µM, Vmax1 = 89.3 pmol/mg/min,
c
Eq.3 fit
CLint2 = 1.2µl/mg/min
evaluations were performed using SYBL 8.0 (Tripos, US).
The CoMFA steric energy (Lennard-Jones) and electrostatic
(Coulomb) energy were calculated with SYBYL standard
parameters (TRIPOS standard field, 2 Å grid spacing, di-
electric distance 1/r2, cutoff 30 kcal/mol) using a sp3 carbon
probe atom with a charge of +1. We also performed CoM-
SIA analysis (an extension of the CoMFA methodology)
because CoMSIA may produce better or complementary
results (31). In CoMSIA, three different similarity fields
(steric, electrostatic, and hydrophobic) were evaluated with
SYBYL standard parameters (2 Å grid spacing, attenuation
factor α00.3) using a probe atom with 1 Å radius, charge +1,
and hydrophobicity +1. Hydrogen bond donor and acceptor
fields were not considered because CoMSIA analyses with
these two extra features did not result in significant improve-
ment of model quality. Partial least squares (PLS) analyses
were performed following the CoMFA standard implementa-
tion in SYBYL. To check statistical significance of the models,
cross-validations were performed by means of the “leave-one-
out” procedure using enhanced version of PLS, the SAMPLS
method. The optimal number of components was determined
by selecting the smallest Spress (corresponds to the highest q2
value).The same number of components was subsequently
used to derive the final QSAR models with no validation
(column filtering was set to 2.0 kcal/mol); these models were
used for prediction of activity. The statistical results are
summarized in Table II. The q2 (cross-validated r2),
Spress (cross-validated standard error of prediction), r2 (non-
cross-validated r2), and standard error of estimate (SEE)
values were computed as defined in SYBYL. Predictive
power of the obtained CoMFA and CoMSIA models was
further validated with the test set which was not included in
the model derivation. The predictive correlation coefficient,
rpred2, of the CoMFA and CoMSIA models were calculated
according to the definition of Cramer et al and are also shown
in Table II (32).

1552
Wu, Wang, Zhang and Hu
Fig. 2 A flexible alignment of
three most active substrates
(66: 3-hydroxy-6-methylflavone,
140: entacapone; 52: chrysin) to
identify their common structural
features. The flexible alignment
was performed with a constraint
that the glucuronidation site
must be overlaid. The important
commonalities of the most active
substrates are found to be the
glucuronidation site and its
adjacent aromatic ring.
3
Arrows indicate the site of
glucuronidation.
3
7
Homology Modeling and Molecular Docking
residue). GOLDscore was used to identify the lowest energy
docking results. The UGT1A9 model (with kaempferol
docked) coordinates are available in Supplementary Material.
A homology model for human UGT1A9 was constructed
using Modeler 9v6 with a standard protocol (33). The best
model with the lowest objective function values (DOPE) was
selected for loop refinement and followed by energy mini-
mization in GROMACS 3.3 program package (34). Energy
minimization was done by steepest gradient descent, with an
initial step size of 0.01 nm and a maximum of 1000 step.
The VvGT1 (PDB code: 2c1z) from red grape was used as
the template for two reasons (35): First, both UGT1A9 and
VvGT1 belong to the glycosyltransferase 1 family (GT1)
according to the CAZY database (36). Although sharing a
low sequence identity, GT1 members adopt a similar α/β/α
folding (so called “GT-B” fold) and their 3D structures are
predicted to be highly conserved (17). Second, UGT1A9
and VvGT1 surprisingly share an overlapping substrate
specificity (including regioselectivity), for example, they both
preferentially metabolize flavonols at the 3-OH position
(28,34). Due to a low sequence identity between the target
and template proteins (~15%), sequence alignment (shown
in Supplementary Material Fig. S2) was aided with secondary
structure predictions, a strategy used earlier (37). The co-
crystalized cofactor (UDP-2-fluoro glucose) was copied to
the homology model as a block residue. Molecular docking
of kaempferol (88.a) for 3-O-glucuronidation to the UGT1A9
model with the program GOLD (CCDC, Cambridge, UK)
was performed using a distant constraint, a procedure similar
to our earlier publications (38,39). A distance constraint
(2.5~4 Å) was set between 3-OH and His37 (the catalytic
RESULTS
Experimental Dataset
A large database of kinetic parameters was experimentally
determined by kinetic profiling for UGT1A9-mediated glu-
curonidation of 145 phenolics (structures in Supplementary
Material Fig. S1), which are from 12 different classes (see
Table I for compound names, their classification and kinetic
parameters). The log(CLint) values for glucuronidation of
selected substrates range from −0.56 (141) to 5.04 (66),
demonstrating a wide diversity in the catalytic activity
(Fig. 3b). To our knowledge, this is the largest dataset of kinetic
data obtained using expressed UGT1A9 in current literature.
Among all UGT substrates here, 17 compounds form more
than one glucuronide; in particular, there are 4 compounds
(i.e., 41, 84, 90, 91) from which three glucuronides at different
positions (-OH groups) are generated. Differentiated kinetic
properties for glucuronidation at each position suggests that
distinct (productive) binding modes within the catalytic domain
are possible (27,28). Based on this “expert” knowledge (i.e., the
existence of multiple binding modes for a same substrate),
multiple active poses (for each of the aforementioned
17 substrates) were incorporated to the molecular alignment
for QSAR analyses (please also see “Discussion” section).

In Silico Predicting UGT1A9-Mediated Glucuronidation
1553
significantly over-estimated more than 1 logarithmic unit,
even though CoMSIA analysis reveals significantly better
correlation in terms of a higher q2.
a
CoMFA Model
The usual way of understanding CoMFA is by graphing the
associated fields. In Fig. 5a, the steric maps derived from
CoMFA are displayed. Areas indicated by green contours
(numbered 1 and 2) correspond to regions where steric
occupancy with bulky groups will increase catalytic activity.
The yellow contours (numbered 3 and 4) mean bulky groups
should be avoided; otherwise reduced activity can be
expected. 74 is more active than 81, which is possibly
explained by the fact that the former orients its 4′-methoxy
group into the favored region (contour 1) (Fig. 5). Similarly,
compared to the less active 8, the more active 49 fills the
favorable region (contour 2) by the 2-benzene moiety
(Fig. 5). 9 is less active than 10, this is most likely because
it orients the 3,4-diphenyl group into the disfavored region
(contour 3) (Fig. 5). Likewise, due to the occupancy of the
disfavored region (contour 4) by a piperidinyl group, 55 has
a lower activity than 52 (Fig. 5).
b
The maps of electrostatic properties are shown in Fig. 5b.
The areas contoured in blue (numbered 5 and 6) correspond
to regions where electropositive groups will enhance the
Table II Summary of Modeling Parameters from CoMFA and CoMSIA
Analyses
Statistics
q2a
CoMFA
CoMSIA
0.548
0.885
0.949
0.775
0.282
8
0.579
0.799
0.876
0.700
0.435
5
b
<1 {=1,2} {=2,3} {=3,4} ≥ 4
Spress
r2c
d
rpred
SEEe
Fig. 3 A wide diversity in both chemical structure (a) and activity (b) for
UGT1A9 substrates in this work. (a) Overview of all aligned structures in
training set and test set (n0166). (b) UGT1A9 activity distribution
(expressed as log(CLint)) of the training and test set.
Componentsf
Fg
Pr200h
271.8
0.000
190.0
0.000
Fraction
Steric
Predictive Power of the Analyses
0.465
0.535
/
0.185
0.435
0.380
Electrostatic
Hydrophobic
A training dataset of UGT1A9 substrates allows the deriva-
tion of two separate QSAR models with statistical signifi-
cance (Table II). The predictive power of the two models
was validated by predicting the catalytic efficiency of 25
additional substrates not included in the training set
(Fig. 4). For almost all substrates, the predicted values fall
close to the observed log(CLint) values, deviating by no more
than 1 logarithmic unit (Table I & Fig. 4). However, in
CoMSIA prediction, the activities of 46 (7-hydroxy-3′-
methoxyflavone) and 70 (3,7,3′-trihydroxyflavone) are
a Cross-validated correlation coefficient after the leave-one-out procedure.
b Cross-validated standard error of prediction.
c Non-cross-validated correlation coefficient.
d Correlation coefficient for test set predictions.
e Standard error of estimate
f Optimum number of components.
g F-test value
h Probability of obtaining the observed F ratio value by chance alone

1554
Wu, Wang, Zhang and Hu
CoMFA
CoMFA
a
b
CoMSIA
CoMSIA
c
d
70
46
Fig. 4 Correlations between the experimental glucuronidation parameters and the predicted ones from the 3D-QSAR models. (a) Fitted predictions
versus actual catalytic efficiencies for the training set. The predicted values were obtained using the CoMFA method. (b) Predicted versus actual catalytic
efficiencies for the test set not included in model derivation. The predicted values were obtained using the CoMFA method. (c) Fitted predictions versus
actual catalytic efficiencies for the training set. The predicted values were obtained using the CoMSIA method. (d) Predicted versus actual catalytic efficiencies
for the test set not included in model derivation. The predicted values were obtained using the CoMSIA method. Dashed lines represent the observed
prediction bias of 3.0-fold deviation from unity. Solid lines represent the observed prediction bias of 10.0-fold deviation from unity.
catalytic activity, as will electronegative groups placed into
areas indicated in red (number 7 and 8). Contour 5 is close
to the glucuronidation site and in parallel with the bicyclic
ring of 52 chrysin (shown in Fig. 5b). The contribution of
contour 5 to the activity-structure correlation is uncertain
since we could not find a pair to demonstrate its significance.
Although being isomers, 95 and 96 possess distinct catalytic
activity. Due to a substitution difference of the acrylic acid
group on the benzene skeleton, the less active 95 orients its
carboxylate group into an area (contour 6) indicated to be
unfavorable for negatively charged groups (Fig. 5). 55 occu-
pies the contour 7 (unfavorable for electropositive groups)
by the 3-hydroxyl group of the piperidinyl moiety; this is
consistent with the fact that 55 is less active compared to 52
(Fig. 5). 32 orients a carbonyl group with an electronegative
oxygen into a site highlighted to be favorable for negatively
charged residues, thus it possesses a higher activity than 123
(Fig. 5).
back onto the molecular structures. These contours are
given in Fig. 6 together with some exemplary substrates.
For consistency, coloring scheme of the contoured areas
(to indicate a property contribution) for steric and electrostatic
fields is the same as used in CoMFA maps. In Fig. 6a, the
steric property is displayed. Interestingly, the contours
(numbered 1, 2, and 3) are largely consistent with those
derived from CoMFA. The more active 49 orients its 2-
benzene ring into the favorable region (contour 1),
whereas the less active 101 orients its 3-benzene ring
into the unfavorable region (contour 3) (Fig. 6). For a
similar reason, 133 partially occupies the unfavorable
region (contour 2) by a isopropyl group and shows a
lower activity compared to 115. As an additional example
pair (molecules 14 and 38) is given, 14 avoids the unfavorable
steric groups and are more active than 38, whose 2-benzene
ring is positioned into contour 3 highlighted to be unfavorable
for bulky groups.
The maps of electrostatic properties (numbered 4 and 5)
show fewer features in space (Fig. 6b), compared to those
from CoMFA. 105 is more active than 101, largely because
it has a hydroxyl group (with an electropositive hydrogen)
overlaid with the favorable region (contour 4). By contrast,
CoMSIA Model
The CoMSIA method also provides field contribution
contours that allow the correlation results to be mapped

In Silico Predicting UGT1A9-Mediated Glucuronidation
1555
Fig. 5 Field contribution
maps from the CoMFA
analysis. (a) Steric maps.
CoMFA
1
2
3-Hydroxyflavone (81) is shown
inside the field for reference.
Green: Areas in which bulky
groups are sterically favorable
for glucuronidation; Yellow:
Areas in which bulky groups are
unfavorable for glucuronidation.
(b) Electrostatic maps. Chrysin
(52) is shown inside the field for
reference. Blue: Areas in which
electropositive atoms/groups are
favorable for glucuronidation; Red:
Areas in which electronegative
atoms/groups are favorable for
glucuronidation. Examples are
given to on the right side matching
the CoMFA results to experimental
data. Favored and disfavored
contour levels for CoMFA were
fixed at 85% and 15%,
1
3
3
4
a
3
2
4
respectively.
6
7
8
5
6
b
7
7
8
58 is more active than 49 for the reason that an ether group
(with an electronegative oxygen) occupied in contour 5
indicated to be favorable for electronegative groups.
group in the unfavorable region (contour 8) would result
in a diminished activity compared to its absence (Fig. 6).
Contour 9 highlights an area where occupancy of a
hydrophilic (polar) group would enhance the activity,
which exemplified by a comparison of 32 and 90.c. 90.c
orients a 3′-hydroxyl group into the favorable region contour
9, thus is more active than 32.
The maps for hydrophobic properties are shown in
Fig. 6c. Substrates orienting groups with increasing hydro-
phobicity into areas contoured in orange (numbered 6, 7,
and 8) will enhance activity, as will groups with increasingly
hydrophilicity placed in areas indicated in purple (numbered
9). 114 is more active than 119; this is probably because both
ethyl and methyl groups substituted on the phenol backbone
are adjacent to the hydrophobic favorable region (contour 6),
but the former is more hydrophobic than the latter (Fig. 6). An
increased hydrophobicity (from a hydroxyl group to a
methoxy group) in contour 7 (hydrophobicity favorable) leads
to an enhanced activity, as is seen from a comparison between
54 and 56.b (Fig. 6). 55 possesses an activity significantly
lower than 32, which is in complete agreement with the
indication that the presence of a hydrophilic piperidinyl
Exploring UGT1A9 Catalytic Pocket Using a Homology
Model and CoMFA/CoMSIA Maps
To explore the molecular mechanisms of UGT1A9-
substrate interaction, a homology model of UGT1A9 was
constructed. This structural model incorporates a simulated
binding of 88.a kaempferol (in an active 3-O-glucuronidation
mode) where 3-OH group of kaempferol is reasonably
hydrogen-bonded with the catalytic residue His37. The
protein residues forming the pocket wall were identified

1556
Wu, Wang, Zhang and Hu
O
Fig. 6 Field contribution maps
from the CoMSIA analysis.
(a) Steric maps. 3-Hydroxy-6-
methxylflavone (66) is shown
inside the field for reference.
Green: Areas in which bulky
groups are sterically favorable
for glucuronidation;
Yellow: Areas in which bulky
groups are unfavorable for
glucuronidation. (b) Electrostatic
maps. Chrysin (52) is shown inside
the field for reference. Blue: Areas
in which electropositive atoms/
groups are favorable for
O
O
CoMSIA
1
2
3
7
HO
O
7
HO
101: log(CLint) = 1.89
49: log(CLint) = 3.13
3
F
HO
HO
115: log(CLint) = 1.18
133: log(CLint) = 0.76
3
a
O
O
2
7
1
6
HO
O
O
HO
O
glucuronidation; Red: Areas in
which electronegative atoms/
groups are favorable
for glucuronidation.
(c) Hydrophobic maps.
Orange: Areas where hydrophobic
groups enhance glucuronidation;
Magenta: Areas where hydrophilic
groups decrease glucuronidation.
Examples are given on the right side
matching the CoMFA results to
experimental data. Favored and
disfavored contour levels for
CoMSIA were fixed at 85% and
15%, respectively.
14: log(CLint) = 2.28
38: log(CLint) = 1.53
OH
OH
OH
O
O
O
4
4
7
7
HO
HO
O
105: log(CLint) = 2.79
101: log(CLint) =1.89
b
O
OH
O
O
7
7
HO
HO
O
5
6
5
O
49: log(CLint) = 3.13
58: log(CLint) = 3.60
HO
HO
119: log(CLint) = 1.39
114: log(CLint) = 1.87
OH
O
OH
O
O
6
9
7
HO
7
O
HO
7
8
O
OH
OH
OH
c
56.b: log(CLint) = 3.32
54: log(CLint) = 3.41
OH
O
OH
O
Cl
8
7
7
7
HO
O
HO
HO
O
N
52: log(CLint) = 3.96
55: log(CLint) = 0.77
O
OH
O
HO
HO
7
O
OH
O
9
4'
4'
HO
OH
32: log(CLint) = 1.83
90.c: log(CLint) = 3.69
and presented in Fig. 7 (more detailed description of the
binding pocket is provided in Supplementary Material
Fig. S3). The binding pocket is divided into four sub-
pockets designated as S1, S2, S3 and S4 (Fig. 7b), among
which pocket S3 is relatively small in size due to the steric
hindrance by residues from helix Nα3 and its preceding loop.
Interestingly, pocket S4 appears to be open to solvent, and
potentially contributes to accommodation of a long-chain
substrate such as 15 curcumin. The three most active
substrates (66: 3-hydroxy-6-methylflavone, 140: entacapone;
52: chrysin) were mapped into the pocket by an alignment
with kaempferol. The B-ring of 66 is fitted to S1, and the
common aromatic ring to S2. S4 accommodates the B-ring of
52 or the N,N-dimethylamide group of 140.
The CoMFA/CoMSIA maps were superimposed on the
binding site of this structural model based on the binding
mode of kaempferol. The contour maps are largely consistent
with the 3D shape of the UGT1A9 catalytic site (Fig. 8). The

In Silico Predicting UGT1A9-Mediated Glucuronidation
1557
Fig. 7 Three-dimensional
model of the UGT1A9-
kaempferol (88.a) complex.
(a) Side view of the
three-dimensional model of
UGT1A9-kaempferol complex.
Kaempferol is indicated in a
ball-and-stick model. The
cofactor is indicated in a
ball-and-stick model with a
a
molecular surface. An expanded
view of residues potentially
involved in the interactions with
kaempferol (3-O-glucuronidation)
in the model is presented.
Dashed black line indicates the
potential hydrogen bond.
(b) Two-dimensional
schematic representation of
UGT1A9 catalytic pocket.
The three most active substrates
(66: 3-hydroxy-6-methylflavone,
140: entacapone; 52: chrysin)
are mapped into the pocket.
The binding pocket is divided into
four sub-pockets designated as S1,
S2, S3 and S4.
S1
S2
S3
b
S4
His37
green regions where bulky groups favor activity correspond to
the regions of the active site where unfilled spaces exist. This
indicates that a bulky group in these regions increases the van
de Waals interaction between a substrate and UGT1A9, thus
increasing the activity (Fig. 8a, b). Also, the yellow regions
(e.g., a and b in Fig. 8b) where bulky groups disfavor
activity correspond to the regions of the active site where
steric hindrance is noted.
an electronegative group (of a substrate) positioned in this
region (Fig. 8). The residues that are around region d
(Fig. 8c) favoring an electropositive group on a substrate are
Glu178, Glu179 and Asp393 (all are electronegative). In
addition, the hydrophobic regions (e and f) are lined with
the hydrophobic residues Val31, Met33, Leu108, Phe224,
Leu228, Met307, and Phe391 (Fig. 8d).
Unexpectedly, electrostatic maps between CoMFA and
CoMSIA are not consistent with each other; this might
indicate an uncertainty in correlating the electrostatic
property of substrates with their activity (Fig. 8a, c). In
contrast to the fact that CoMFA electrostatic maps appear in
the regions where no polar residues can be identified,
CoMSIA electrostatic maps show a good compatibility
with the surrounding residues. An electronegative group
in its favorable region (contour c or previously named
contour 5 in Fig. 6) presumably interacts with UGT1A9
by forming a hydrogen bond or through electrostatic
interactions. As only polar residues Asp34 (with an electro-
negative side chain) and His37 (with an electropositive side
chain) appear in the neighborhood of contour c, His37 is
proposed to contribute to the interaction of UGT1A9 with
DISCUSSION
In this study, we report two 3D-QSAR models for
UGT1A9-mediated glucuronidation. These models are
more generalized thus more useful in comparison to our
earlier ones (27), which are somewhat limited to predicting
UGT1A9-mediated 3-O-glucuronidation of flavonols, a
subset of phenolics. Our model is challenged by the fact
that many substrates form more than one glucuronide at
different sites (so called “glucuronide isomers”). Recent
studies indicate that the glucuronide isomers are resulted
from distinct binding modes of the same substrate in the
catalytic domain of UGT protein (27,28). Each binding
mode orients a glucuronidation site (-OH) towards the

1558
Wu, Wang, Zhang and Hu
a CoMFASteric + Electrostatic b CoMSIA Steric
S1
S2
D393
D393
M212
M212
H175
H175
E179
E179
D34
H37
M307
V31
D34
H37
a
M307
E178
V102
E178
H213
H213
V102
V31
M33
b
L228
L228
F224
M33
S4
F224
I105
L108
I105
L108
S3
c CoMSIA Electrostatic
d CoMSIA Hydrophobic
D393
D393
M212
M212
H175
H175
E179
E179
D34
H37
D34
H37
c
E178
E178
H213
H213
e
M307
M307
V102
L228
F224
V102
L228
V31
V31
M33
f
M33
F224
L108
L108
I105
I105
Fig. 8 Superposition of the CoMFA/CoMSIA contour maps over the binding site of a homology-modeled UGT1A9 structure based on a simulated binding
model of kaempferol (3-OH). The UGT1A9 protein is shown in a stick model. Kaempferol is indicated in a ball-and-stick model and the cofactor is
shown in a ball-and-stick model with a molecular surface. (a) Overlay of the CoMFA steric and electrostatic maps with the UGT1A9 binding site. (b-d)
Overlay of the CoMSIA steric (b), electrostatic (c), and hydrophobic (d) fields with the UGT1A9 binding site.
catalytic residue (usually a histidine) for reaction, and to
generate a corresponding glucuronide isomer. Accordingly,
we treat such a UGT substrate as multiple “substrates” that
adopt distinct spatial conformations; each of which corresponds
to a binding mode to form a regiospecific glucuronide isomer.
Our derived models with statistical significance and high
predictive capability suggest that this treatment is reasonable
and useful in terms of in silico modeling of UGT substrates.
It is also highlighted that our model can be used to predict
regiospecific glucuronidation mediated by UGT1A9, which
was not accomplished before (27).
difficulty is raised with respect to the molecular alignment of
those structures. We proposed a unique alignment method
that is to superimpose the glucuronidation site and its
adjacent aromatic ring, the two important features identified
by a flexible alignment of three most active substrates (Fig. 2).
Glucuronidation sites should be aligned to improve model
quality as suggested from previous modeling experiences
(8,15). The importance of positioning of a glucuronidation
site into an area close to both cofactor and the catalytic
histidine residue for glucuronidation is also demonstrated
in a recent study (40). Early studies identify a common
features pharmacophore for several UGT isoforms, which
unanimously includes two separate hydrophobic regions
Another challenge to our model is that it incorporates a
large diversity of (n0145) substrate structures. As a result,

In Silico Predicting UGT1A9-Mediated Glucuronidation
1559
(adjacent to the glucuronidation site) (8,15,41). However,
we believe two hydrophobic regions spatially isolated might
not be universal features for all UGT1A9 substrates. For
example, such features cannot be found in 109 (a simple
phenol) structure. Token together, the alignment rule here
conforms to the catalytic mechanism and is applicable to a
simple phenol. More importantly the rule is demonstrated to
be satisfactory when measured by the quality of resulting
models.
the amino acids that are responsible for the large activity
differences between UGT1A9 and 1A10 (25).
A good consistency between the CoMFA/CoMSIA maps
and a homology model of UGT1A9 is highlighted in
terms of the steric and hydrophobic interactions (Fig. 8).
The results provide a highly possible 3D structure of
UGT1A9 binding pocket as well as substantial insights into
the molecular mechanisms regarding the recognition of a
substrate by UGT1A9. The models can be used to guide de
novo design of compounds with desired UGT1A9 activity. For
example, a more active compound should have its -OH group
towards the catalytic residue, and the rest of its structure
occupies those green regions (cavities in the binding site),
and avoids those yellow regions where steric hindrance exists.
We anticipate that this approach of CoMFA/CoMSIA
coupled with a protein homology model may be applicable
to other UGT isoforms. A more exhaustive elucidation of
molecular interactions of other UGT isoforms and a more
complete comparison of substrate selectivity across UGT
isoforms might be necessary in order to ultimately predict
overall glucuronidation and uncover the fine substrate
selectivity difference. This is important as the knowledge
can be used to accelerate drug development and to
promote human health.
Although model construction is based on the active poses
of UGT1A9 substrates, it is of interest to see if the model
can be used to distinguish a non-substrate from a substrate.
We experimentally identified three UGT1A9 non-substrates,
namely, estradiol (a selective probe for UGT1A1), salicylic
acid, and aminosalicylic acid (no metabolite can be detected
when incubating these compounds with UGT1A9). The
predicted log (CLint) values from the CoMFA model are
1.41, 1.11, and 1.42, respectively, indicating these compounds
are very poor substrates of UGT1A9. Therefore, the model is
fairly accurate; though its capability to predict an absolute
non-substrate is somewhat limited.
In conclusion, we have performed 3D-QSAR analyses
using the powerful techniques Comparative Molecular Field
Analysis (CoMFA) and Comparative Molecular Similarity
Indices Analysis (CoMSIA) based on a large training dataset
with a 106-fold range of relative catalytic activity. The
derived models show statistical significance and substantive
predictability (CoMFA: q200.548, r200.949, rpred200.775;
CoMSIA: q200.579, r200.876, rpred200.700). The real-
world use of these models is fully expected to predict the
catalytic activity of structural diverse chemicals (including
drug candidates) towards UGT1A9. Moreover, the field
contribution maps from CoMFA/CoMSIA were applied
to elucidate the catalytic pocket of UGT1A9 with the aid
of a homology model of UGT1A9. The results consistently
depict a plausible catalytic pocket with a set of geometry
configuration and a hydrophobic interacting environment,
even though the electrostatic interactions are less defined.
The use of log (CLint), a measure of catalytic efficiency, as
the parameter for correlation analyses is more reliable and
meaningful, compared to other kinetic parameters such as
Km and Vmax. As stated by Sharma and Duffel (42,43), in
the case of enzymes that may exhibit nonproductive binding
interactions with some substrates, a highly relevant kinetic
parameter for a CoMFA correlation of the structure to its
ability to serve as a substrate for such enzymes is the CLint
value, or log (CLint). This is because CLint is independent of
nonproductive binding contributions. Although there is no
direct evidence (e.g., crystal structure) of nonproductive
binding of a substrate to a UGT protein, kinetic charac-
terization has indicated that nonproductive binding of
substrates to a UGT isoform can be a major reason why the
enzymes frequently exhibit substrate inhibition kinetics (44).
Therefore, the use of CLint value for our CoMFA/CoMSIA
analyses of UGT1A9 is justified. Moreover, in vitro intrinsic
clearance (CLint) is frequently used to predict in vivo clearance
such as hepatic clearance with a reasonable success rate
(45,46). Hence, this parameter is a more appropriate indicator
as the susceptibility of a substrate to glucuronidation in vivo.
The recognition of glucuronidation as an important
metabolic pathway has lent increasing efforts towards
better understanding of the molecular mechanisms of
UGT functions and of the substrate structural features
associated with UGT selectivity. Inevitably, a molecular-
level structural elucidation of the protein is necessary for
such pursuits. Here, a homology model of UGT1A9 was
constructed aiming to enhance our understanding of
interactions between UGT1A9 and substrates, in addition
to the CoMFA/CoMSIA results. The structural information
of our UGT1A9 model was imported from the template
protein VvGT1 (a plant UGT). At present, the use of a plant
UGT for homology modeling of human UGTs is preferred
and justifiable, because (1) plant and human UGTs are
classified into the same superfamily GT1; GT1 members
adopt a GT-B fold and their tertiary structures are
predicted to be highly conserved; (2) plant and human
UGTs share a similar catalytic mechanism (i.e., serine
hydrolase-like mechanism); (3) a determined partial crystal
structure (for C-terminal or UDPGA binding domain portion
only) of human UGT2B7 agrees well with its counterparts in
plant UGT crystal structures (13,14). Such modeling effort
appears to be useful here and has also been utilized to elucidate

1560
Wu, Wang, Zhang and Hu
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This work was supported by grants from the National Institutes of Health
(GM070737) to MH.