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FKPPL workshop May 2012 BUI The Quang Prof. Vincent Breton Prof. Doman Kim Prof. NGUYEN Hong Quang Prof. PHAM Quoc Long Grid enabled in silico drug discovery

Grid enabled in silico drug discovery

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Grid enabled in silico drug discovery. BUI The Quang Prof. Vincent Breton Prof. Doman Kim Prof. NGUYEN Hong Quang Prof. PHAM Quoc Long. Laboratories involved. CNU, Gwangju , Korea IFI, Hanoi, Vietnam INPC, Hanoi, Vietnam KISTI, Seoul, Korea LPC, Clermont-Ferrand, France. - PowerPoint PPT Presentation

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Page 1: Grid enabled in  silico  drug discovery

FKPPL workshop May 2012

BUI The Quang

Prof. Vincent Breton

Prof. Doman Kim

Prof. NGUYEN Hong Quang

Prof. PHAM Quoc Long

Grid enabled in silico drug discovery

Page 2: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 2

Laboratories involved

- CNU, Gwangju, Korea- IFI, Hanoi, Vietnam- INPC, Hanoi, Vietnam- KISTI, Seoul, Korea- LPC, Clermont-Ferrand, France

Page 3: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 3

Content

• Activity in Korea• Activity in Vietnam• Activity in France

Page 4: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 4

Human maltase:

- A α-glucosidase, belongs to glycosides hydrolase

family 31 (EC 3.2.1.20 and 3.2.1.3) and located on

chromosome 7 with 868 amino acids and contains

five distinct protein domains.

- Important target in treatment of diabetes type 2.

Credit: Doman Kim

Activity in Korea - Publication 1

Page 5: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 5

Scoring based on docking score( 308,307)

454,000 chemical compounds from Chembridge

Interaction with key residues

3016 compounds selected

2616 compounds selected

Key interactionsbinding models

clustering

In vitro test

42 compound selected

Filtration process

Total numbers of docking 308,307

Total size of output results 16.3 GB

Estimated duration by 1CPU 22.4 years

Duration of experiments 3.2 days

Maximum numbers of concurrent CPUs 4700 CPUs

Crunching Factor 2556

Distribution Efficiency 54.4 %

Statistics of data challenge deployment on WISDOM production environment

Credit: Doman Kim

Page 6: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 6

Inhibition on human maltase & pancreatic α-amylase

Inhibitor

0 uM 10 uM 25 uM 50 uM 100 uM

Rel

ativ

e ac

tivity

(%)

0

20

40

60

80

100

120

140

160

AcarboseNo.17 No.18

Compound No.

Lowest Energy

M.W

(g/mol)

Ki

(μM)IC50 (µM) Type of

inhibition

17 -16.43 473 19.8 ±1.2 58±4 Competitive

18 -16.44 429 19.6±0.9 55±3 Competitive

Acarbose -12.62 645.61 19.4 52±4 Competitive

Unlike acarbose, compounds 17 and 18 were competitive inhibitors exclusively for HMA without any in vitro inhibition for human pancreatic alpha-amylase.

Credit: Doman Kim

Page 7: Grid enabled in  silico  drug discovery

FKPPL workshop May 2012

1) A global outbreak of Severe Acute Respiratory Syndrome (SARS) between March 2003 and July 2003 caused over 8,000 cases and 774 deaths (9.6%) (World Health Organization).

2) The 3C-like protease (3CLpro) is needed for SARS-CoV replication and is a promising drug target.

World Health Organization. Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003 (based on data as of the 31. December 2003). http://www.who.int/csr/sars/country/table2004_04_21/en/.

Activity in Korea - Publication 2

Credit: Doman Kim

Page 8: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 8

Docking parameters:

Ga_run=50, Ga_pop_size=250, Ga_num_generation=27000, Ga_num_evaluation= 2500000

Virtual screening on WISDOM production environment

Virtual screening 3CL protease of SARS on WISDOM production environment

54 compounds were selected for In vitro assay

308,307 compounds

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

Credit: Doman Kim

Page 9: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 9

Activity in Vietnam

• The WPE platform– Used in WISDOM projects(Wide In Silico Docking On Malaria)

for discovery of medicine for malaria– Developed by LPC Clermont-Ferrand laboratory – Reduce many time for finishing these challenges

• The DIRAC platform– DIRAC: Distributed Infrastructure with Remote Agent Control– DIRAC forms a layer between a particular community and

various compute resources to allow optimized, transparent and reliable usage

• Comparison between the WPE & DIRAC platform– Performance– Stability– Capability of submission of pilot agent

Page 10: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 10

Comparison of performance

100 500 1000 5000 100000

100

200

300

400

500

600

700

DIRAC

WPE 100

WPE 200

WPE 500

WPE 1000

Number of tasks

Tim

e (m

inu

te)

DIRAC is faster than WPE

WPE is faster than DIRAC

DIRAC submit faster pilot agent than WPE one WPE’s agent execute many task while one DIRAC’s agent

execute only one task

DIRAC is faster than WPE

WPE is faster than DIRAC

Page 11: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 11

Comparison of stability

Percentage of DIRAC pilot agent is terminated by unknown cause is less than WPE platform

Pilot agent of DIRAC is more stable than pilot agent of WPE

DIRAC WPE 100 WPE 200 WPE 500 WPE 10000%

5%

10%

15%

20%

25%

30%

Rate of WPE’s & DIRAC’s pilot agents is terminated by unknown cause(test with Autodock)

Page 12: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 12

Comparison of capability of submission of pilot agent

Rate of DIRAC pilot agent submission successful to grid is higher than WPE platform

DIRAC platform submits better pilot agent to grid than WPE platform

DIRAC WPE 100 WPE 200 WPE 500 WPE 10000%

20%

40%

60%

80%

100%

120%

Rate of pilot agent submission successful to grid

Page 13: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 13

Result from INPC laboratory

• Database of natural products isolated from biodiversity in Vietnam (~1000 ligands)

• Anti-malarial tests on 43 compounds with 2 biological targets: chloroquine-susceptible T96 and chloroquine resistant K1

• Ongoing research to look for compounds with anti-malarial bioactivity by virtual screening on the database of natural products (2011) – Key protein: protein plasmepsin II (1LEE)– Use of AUTODOCK software for virtual screening

Page 14: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 14

Activity in France

Design a virtual screening system for multi-user

SchedulerGrid resource

Problem of virtual screening on grid - Speed of machine are different- Task arrive in random process- Available duration of machine are random

Find out the policy for maximizing the throughput of all users

Workflow of research- Find out the policy and prove by theory- Valid on the grid simulator SIMGRID- Programming scheduler on the DIRAC platform

Page 15: Grid enabled in  silico  drug discovery

FKPPL workshop , May 2012 15

Thank you for your attention