Virtual screening and inhibition assay of
human intestinal maltase and 3C-like
protease of SARS using molecular docking on
WISDOM production environment
Thi-Thanh-Hanh NGUYEN1, Sun LEE1, Soonwook HWANG2, Seungwoo
RHO2, Vincent BRETON4, Doman KIM1
1Biotechnology
2Korea
and Bioengineering, Chonnam National Uiversity, Gwangju, South Korea
Institute of Science and Technology Information, Daejeon, Korea,
3HealthGrid
LPC-Clermont-Ferrand, France, 4 LPC-Clermont-Ferrand, France
TEL: +82-62-530-1844, FAX: +82-62-530-1949 , E-mail: dmkim@chonnam.ac.kr
WISDOM In silico Drug Discovery
Enabling Grids for E-sciencE
WISDOM: http://wisdom.healthgrid.org/
 Goal: find new drugs for neglected and emerging diseases
• Neglected diseases lack R&D
• Emerging diseases require very rapid response time
 Need for an optimized environment
• To achieve production in a limited time
• To optimize performances
 Method: grid-enabled virtual docking
• Cheaper than in vitro tests
• Faster than in vitro tests
Dr. Vincent Breton
Searching for new drugs
 Drug development is a long (10-12 years) and
expensive (~800 M US$) process
 In silico drug discovery opens new perspectives to speed
it up and reduce its cost
Target discovery
Lead discovery
Target
Identification and validation
Lead
identification
- 2/5 years
- 30% success rate
- 0.5 year
- 2/4 years
- 65% success rate - 55% success rate
Gene expression analysis,
Target function prediction,
Target structure prediction
De novo design,
Virtual screening
Lead
optimization
Virtual screening,
QSAR
From Dr. Vincent Breton
A first step towards in silico drug discovery: virtual screening
Starting compound
database
 In silico virtual
screening
 Very computationally
intensive but
potentially much
cheaper and time
effective than typical
in vitro testing
Filter, preparation
Define binding site
Visual evaluation
Docking, scoring, filter
Visual evaluation
Predicted
binding models
Protein surface
Water
Ligand
Post-analysis
Visual evaluation
Compounds
for assay
From Dr. Vincent Breton
Computational demand
 Starting from millions
of compounds, select a
handful of compounds
for in vitro testing
Starting target
structure model
Discoveries of novel inhibitor for human intestinal maltase
• Human intestinal maltase : N-terminal of
Human maltase glucoamylase responsible
for the hydrolysis of α (1-4)-linkages from
maltooligosaccharide and belongs to
glycosides hydrolase family 31
• Inhibition of the enzyme activity
→ retardation of glucose absorption
→ decrease in postprandial blood glucose
level
• Important target to discovery of new drug
for treatment of type-2 diabetes.
Sim L, Quezada-Calvillo R, Sterchi EE, Nichols BL,
Rose DR. 2008, J Mol Biol. 375(3):782-92
Data challenging on WISDOM production environment
Total numbers of docking
308,307
Total size of output results
16.3 GBytes
Estimated duration by 1 CPU
Duration of experiments
22.4 years
3.2 days
Maximum numbers of concurrent CPUs 4700 CPUs
Crunching Factor
Distribution Efficiency
2556
54.4 %
WISDOM
Processing in virtual screening
454,000 chemical compounds from Chembridge
Scoring based on docking score
( 308,307)
Autodock 3
2974 compounds selected
Interaction with key residues
Chimera
and ligplot
2574 compounds
selected
Key interactions
binding models
clustering
Wet
Laboratory
42 compound
selected
In vitro
test
www.themegallery.com
Cloning and expression of human intestinal
maltase in Pichia pastoris
Control
Enzyme activity
PCR
M
P
0
2.7Kb
48 96h 0
24 40
24 40 48 96h Glc
 Conditions for HMA expression
Set 1
M
1
2
→ Culture 500 ml in 2 L flask at
30℃ and 200 rpm
Set 2
C
1 2
C
→ 0.5% methanol
→ ~4 days
→ enzyme reaction : 90 min at 37 ℃
(50 mM maltose)
Primer set 1 : α-factor - Internal
Primer set 2 : α-factor – 3’AOX1
Primarily in vitro Inhibition assay
Inhibition at 100 μM
Kinetic characterization of hit compounds
18
17
acarbose
0.08
6
0.08
5
0.06
0.04
4
1/v
1/v (mg/Units)
1/v (mg/Units)
0.06
0.04
3
2
0.02
0.02
1
0.00
-20
0
0.00
0
20
40
60
80
Inhibitor (M)
-20
0
20
40
60
Inhibitor (M)
80
-20
0
20
40
60
Acarbose (M)
→ Competitive inhibitor
→ Competitive inhibitor
→ Competitive inhibitor
→ Ki = 19.8 ± 1.2 μM
→ Ki = 19.6 ± 0.9 μM
→ Ki ≒ 19.4 μM
Chemical structure, physiochemical properties and
inhibition activity of the indentified hits with HMA
Compound
No
17
Chemical
structure
Lowest M.W clogP Ki
IC50
Type of
energy (g/mol)
(μM) (µM) inhibition
-16.43
473
3.04 19.8 58±4 competitive
±1.2
18
-16.44
429
Acarbose
-12.62
645.60
5
3.56 19.6± 55±3 competitive
0.9
19.4
52±4 competitive
Hydrogen bond interactions with key residues of two hit
compounds in active site of protein
A)
(B)
(A)
(C)
www.themegallery.com
Docking experiment of two hit compounds
with human pancreatic α-amylase
Human pancreatic α-amylase PDB ID: 1XCX
Number
Name of
compounds
Binding energy
(kcal/mol)
1
IAB
-15.69
2
17
-12.99
3
18
-12.89
A
Active site
187899
258532
Acarbose
160
140
Acarbose
No.17
No.18
C
Relative activity (%)
120
100
80
D
60
40
 Biotechnol. Lett. 2011 Nov;33(11):2185-9
20
0
0 uM
10 uM
25 uM
Inhibitor
50 uM
100 uM
Discovery of Novel inhibitor of 3CL protease of SARS
The possibility of the re-emergence of SARS is a serious threat, since
efficient therapy and a vaccine are not currently available;
The 3C-like protease (3CLpro) of severe acute respiratory syndrome
associated coronavirus (SARS-CoV) is vital for SARS-CoV replication and
is a promising drug target.
WISDOM
Processing in virtual screening
454,000 chemical compounds from Chembridge
Scoring based on docking score
( 308,307)
Autodock 3.0
1468 compounds selected
Interaction with key residues
Chimera
and ligplot
1065 compounds
selected
Key interactions
binding models
clustering
Wet
Laboratory
53 compound
selected
In vitro
test
www.themegallery.com
Cloning and expression of 3CL-protease of SARS
in E. coli BL21 (DE3)
Transformation
into E.coli DH5α
Colony-PCR
of E.coli BL21 (DE3)
pET28a
3CL932bp
RE digestion
940 C 940 C
5 min 1min
530 C
M
B U W1 W2 W3 E1 E E3 E4 E5 E6 E7 E8 E9 M
720 C 720 C
1min 5 min
30 s
25 cycles
45
3CL protease
31
Ni-NTA
purification
Primarily Inhibition study
*
Km = 10.17 ± 1. 4 μM
(3CL protese from E.coli BL21(DE3)
Inhibitor at 100 μM
IC50 of hit compounds against 3CLpro of SARS
Compound Free binding
No
energy
IC50 (μM)
1
(kcal.mol-1)
-14.5
58.35 ± 1.41
2
-15.09
62.79 ± 3.19
3
-15.17
101.38 ± 3.27
4
-15.20
77.09 ± 1.94
5
-15.75
90.72 ± 5.54
6
-15.02
38.57 ± 2.41
7
-15.13
41.39 ± 1.17
Kinetic analysis of 3CLpro of SARS inhibition by compound 7
Fig. Lineweaver-Burk plot (A) and Dixon plot (B) of the inhibition of
3CLpro from E.coli BL21 (DE3) by compound 7.
→ Compound 7 inhibits 3CLpro as a competitive inhibitor
→ Ki value for compound 7 is 9.93 ± 0.44 μM
Hydrogen bond interaction of compound 7 against 3CLpro
Hydrogen bond interaction of compound 6 against 3CLpro
 Bioorg. Med. Chem. Lett. 2011 May 15;21(10):3088-91
 Inhibitors of SARS-coronavirus 3CL Protease for Severe Acute Respiratory Syndrome and Method
for screening thereof. Korea Patent Pending, 10-2011-0003078 (Jan 11, 2011)
Conclusion
After datachallenge of 308,307 compounds, 42 compounds of
HMA and 53 compounds of 3CLpro of SARS were select for in
vitro assay;
The 2 compounds and 7 compounds for HMA and SARS,
respectively were identified IC50;
All of these compounds were showed the competitive
inhibition.
The inhibitors could be stabilized by the formation of Hbonds with catalytic residues and the establishment of
hydrophobic contacts at the opposite regions of the active site.
Further study
 Virtual screening of nature compounds , chembridge ligand library, Chemdiv
ligand library, and Zinc with:
- Influenza virus: N1 from H1N1.
- Malaria: falcipain 2, 3.
- Sars;
- Diabetes type 2;
Acknowledgements
Enzyme in vitro tests:
Hwa-Ja Ryu, Hee-Kyoung Kang, Sun Lee (CNU, in vitro test),
In silico data challenge and analyses (WISDOM):
KISTI, Korea
Soon-Wook HWANG, Seungwoo RHO, et al.
CNRS-IN2P3-LPC, Clermont-Fd, France
Vincent BRETON et al.
The Laboratory Functional Carbohydrate Enzymes
and microbial Genomics.