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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 and Bioengineering, Chonnam National Uiversity, Gwangju, South Korea
2Korea 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: [email protected]
Enabling Grids for E-sciencEWISDOM In silico Drug Discovery
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 testsDr. 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
TargetIdentification and validation- 2/5 years- 30% success rate
Leadidentification- 0.5 year- 65% success rate
Leadoptimization- 2/4 years- 55% success rate
Target discovery Lead discovery
Gene expression analysis,Target function prediction,Target structure prediction
De novo design,Virtual screening
Virtual screening,QSAR
TargetIdentification and validation- 2/5 years- 30% success rate
Leadidentification- 0.5 year- 65% success rate
Leadoptimization- 2/4 years- 55% success rate
Target discovery Lead discovery
Gene expression analysis,Target function prediction,Target structure prediction
De novo design,Virtual screening
Virtual screening,QSAR
From Dr. Vincent Breton
A first step towards in silico drug discovery: virtual screening
In silico virtual screening Starting from millions
of compounds, select a handful of compounds for in vitro testing
Very computationally intensive but potentially much cheaper and time effective than typical in vitro testing
Com
putationaldemand
Starting compound database
Starting target structure model
Filter, preparation
Docking, scoring, filter
Predicted binding models
Post-analysis
Define binding site
Visual evaluation
Visual evaluation
Visual evaluation
Compounds for assay
Protein surface
Ligand
Water
Protein surface
Ligand
Water
From Dr. Vincent Breton
• 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
Discoveries of novel inhibitor for human intestinal maltase
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 22.4 years
Duration of experiments 3.2 days
Maximum numbers of concurrent CPUs 4700 CPUs
Crunching Factor 2556
Distribution Efficiency 54.4 %
www.themegallery.com
Processing in virtual screening
Scoring based on docking score( 308,307)
454,000 chemical compounds from Chembridge
Interaction with key residues
2974 compounds selected
2574 compounds selected
Key interactionsbinding models
clustering
In vitro test
42 compound selected
Autodock 3
WIS
DO
M
Chimera and ligplot
Wet Laboratory
Cloning and expression of human intestinal maltase in Pichia pastoris
PCR
M P
2.7Kb
M 1 2 C 1 2 C
Set 1 Set 2
Primer set 1 : α-factor - Internal
Primer set 2 : α-factor – 3’AOX1
Conditions for HMA expression
→ Culture 500 ml in 2 L flask at 30 and 200 rpm℃
→ 0.5% methanol
→ ~4 days
→ enzyme reaction : 90 min at 37 ℃
(50 mM maltose)
0 24 40 48 96h Glc 0 24 40 48 96h
Control Enzyme activity
Primarily in vitro Inhibition assay
Inhibition at 100 μM
Kinetic characterization of hit compounds
→ Competitive inhibitor
→ Ki = 19.8 ± 1.2 μM
Inhibitor (M)
-20 0 20 40 60 80
1/v
(mg
/Un
its)
0.00
0.02
0.04
0.06
0.08
→ Competitive inhibitor
→ Ki = 19.6 ± 0.9 μM
Inhibitor (M)
-20 0 20 40 60 80
1/v
(mg
/Un
its)
0.00
0.02
0.04
0.06
0.08
Acarbose (M)
-20 0 20 40 60
1/v
0
1
2
3
4
5
6
→ Competitive inhibitor
→ Ki 19.4 ≒ μM
17 18 acarbose
Chemical structure, physiochemical properties and inhibition activity of the indentified hits with HMA
Compound No
Chemical structure
Lowest energy
M.W
(g/mol)
clogP Ki
(μM)
IC50 (µM)
Type of inhibition
17 -16.43 473 3.04 19.8 ±1.2
58±4 competitive
18 -16.44 429 3.56 19.6±0.9
55±3 competitive
Acarbose -12.62 645.605
19.4 52±4 competitive
www.themegallery.com
Hydrogen bond interactions with key residues of two hit compounds in active site of protein
(A)
(B)
(C)
A)
Inhibitor
0 uM 10 uM 25 uM 50 uM 100 uM
Rel
ativ
e ac
tivi
ty (
%)
0
20
40
60
80
100
120
140
160
AcarboseNo.17 No.18
Docking experiment of two hit compounds with human pancreatic α-amylase
Human pancreatic α-amylase PDB ID: 1XCX
Acarbose187899 258532
A
CD
Number Name of compounds
Binding energy(kcal/mol)
1 IAB -15.69
2 17 -12.99
3 18 -12.89
Biotechnol. Lett. 2011 Nov;33(11):2185-9
Active site
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.
Discovery of Novel inhibitor of 3CL protease of SARS
www.themegallery.com
Processing in virtual screening
Scoring based on docking score( 308,307)
454,000 chemical compounds from Chembridge
Interaction with key residues
1468 compounds selected
1065 compounds selected
Key interactionsbinding models
clustering
In vitro test
53 compound selected
Autodock 3.0
WIS
DO
M
Chimera and ligplot
Wet Laboratory
Cloning and expression of 3CL-protease of SARS in E. coli BL21 (DE3)
Transformation into E.coli DH5α
RE digestion
pET28a
3CL-932bp
940 C1min
530 C
30 s
720 C
940 C5 min
1min720 C
5 min
25 cycles
Colony-PCR of E.coli BL21 (DE3)
M B U W1 W2 W3 E1 E E3 E4 E5 E6 E7 E8 E9 M
3CL protease
Ni-NTA purification
45
31
Primarily Inhibition study
Km = 10.17 ± 1. 4 μM(3CL protese from E.coli BL21(DE3)
* Inhibitor at 100 μM
Compound No
Free binding energy
(kcal.mol-1)
IC50 (μM)
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
IC50 of hit compounds against 3CLpro of SARS
N
NN
S
SN
H2N
1
O
NH
N
HN
O
O
OH
OH3C
CH3
4
O
CH3
H3C
H3CO
NHO
ON
OH
O
CH3
CH3
CH3
2
NN
O
S
NN
SNH O
O
5
HN
OO
H
H3C
O
N+OO-
HN N
CH3
CH3
6
N
O
N
OOH
N+
OO
-O
SN
CH3
CH3
H3C
O
HN
O
NH
NCH3
CH3
O
N+ O-O
3
7
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 H-
bonds 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.