Discovery of novel human intestinal maltase i nhibitors on WISDOM - - PowerPoint PPT Presentation

Discovery of novel human intestinal maltase i nhibitors on WISDOM environment and in vitro confirmation

Hwa-Ja Ryu, Hanh Thi Thanh Nguyen, Sehoon Lee, Soon wook Hwang, Ana Lucia Dacosta, Vincent Breton, Doman Kim

Virtual screening: Use of high-performance computing to analyse large databases of chemical compounds in order t

• identify possible candidates

Phases of a pharmaceutical development

3

Virtual Screening

• Computational methods

– Pharmacophore based search – Structure based docking

• Requirements

– 3D Structure of target – Databases of small molecules – A method to dock and score bound small molecule

LIGAND RECEPTOR

Potential inhibitors

+

Protein + Inhibitor Protein Future drug ?

What is docking?

Autodock 3.0 Chimera Wet Laboratory

Molecular docking Complex visualization in vitro

500 000 329

50

10

in vivo

Grid Strategy of drug design

Chemical compounds : Chembridge – 500,000 Targets : Human maltase glucoamylase (2QMJ) Millions of chemical compounds available in laboratories High Throughput Screening 1-10$/compound, nearly impossible Molecular docking (Autodock) Computational data challenge Hits screening using assays performed on living cells Leads Clinical testing Drug

High throughput virtual docking

7

Large Computational resources

Grid computing is applying the resources of many computers in a network to a single pr

• blem at the same time - usually to a scientific or technical problem that requires a g

reat number of computer processing cycles or access to large amounts of data. Example : “EGEE (Enabling Grids for E- sciencE) is providing a produc tion quality grid infrastructure spanning more than 30 countri es with over 150 sites”

• The grid infrastructure allow to deploy large scale computer-based in-silico scre

ening : – Docking programs are often restricted by the computational time, due to the eno rmous number of possibilities that should be examined; – More computational time = more accuracy for the scoring function – Efficiency is especially required for drug design

• WISDOM initiative aims to demonstrate the relevance and the impact of the gri

d approach to address drug discovery for neglected and emerging diseases.

Grid-Enabled Virtual screening

Use of GRID COMPUTING to speed up the whole process

9

Database of small molecules

• Drug-like: MDDR (MDL Drug Data Report) >147,000 compounds, CMC (Comp

rehensive Medicinal Chemistry) >8,600 compounds

• Non-drug-like: ACD (Available Chemicals Directory) ~3 millions compounds
• CSD (Cambridge Structural Database, www.ccdc.cam.ac.uk): 264,000 compound

s

• Corporates Databases: few millions in pharmaceuticals companies
• Virtual libraries (Combinatorial chemistry)
• ZINC, a free database of commercially-available compounds for virtu

al screening. ZINC contains over 4.6 million compounds in ready-to- dock, 3D formats

• Chembridge database (www.chembridge.com): 454,000 compound

s

Finding Inhibitors of Human Intestinal Maltase

Human Intestinal Maltase (HMA)

• α-glucosidase in the brush border of the small intestines responsible

for digestion of maltose oligosaccharides into glucose

• Inhibition of the enzyme activity

→ retardation of glucose absorption → decrease in postprandial blood glucose level

• Important target in treatment of diabetes type 2 and obesity
• α-glucosidase inhibitors – Acarbose (Glucobay), Miglitol (Glyset),

Voglibose (Voglib) with side-effects

• Need to discover alternative inhibitors with greater potency and

fewer side-effects

Sim L et. al. 2008J Mol Biol. 375(3):782-92

Binding information of acarbose with human maltase

Scoring based on docking score ( 308,307)

454,000 chemical compounds from Chembridge

Interaction with key residues

3016 compounds select ed 2616 compounds selected

Key interactions binding models clustering

In vitro test 42 compoun d selected

Filtration process

Total numbers of docking ¡ 308,307 ¡ Total size of output results ¡ 16.3 GBytes ¡ 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 datachallenge deployment

• n WISDOM production environment

Hydrogen bond interactions with Key residues of two hit compounds in active site of protein

17 18

In active site of HMA

18 17 Hydrogen bonding of 2 hit compounds

Cloning and expression of human maltase in Pichia pastoris

PCR ¡ M ¡ ¡ ¡ ¡ ¡P ¡ 2.7Kb ¡

0 ¡ ¡ ¡24 ¡40 ¡ ¡48 ¡96h ¡Glc ¡ ¡ ¡0 ¡ ¡ ¡24 ¡ ¡40 ¡ ¡ ¡48 ¡96h ¡ ¡ ¡

Control ¡

• 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) Enzyme activity

Enzyme Kinetics Analysis

Inhibitory activity of the indentified hits with HMA

Compound No. 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 -6.655 19.4 52±4 competitive

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 7007617

• 12.99

3 7002209

• 12.89

Active site pocket

Acarbose ¡ ¡ 17 ¡ 18 ¡ Active site poc ket

Inhibitory activities of the identified 2 hits

• n human pancreatic α-amylase

Conclusions

 Identification ¡of ¡novel ¡HMA ¡inhibitors ¡through ¡structure-­‑based ¡virtual ¡ screening ¡ ¡  After ¡ ¡datachallenge ¡of ¡308,307 ¡compounds, ¡ ¡42 ¡compounds ¡were ¡selec ted ¡for ¡in ¡vitro ¡inhibition ¡assay. ¡  Inhibitory ¡activities ¡of ¡compound ¡No.17 ¡and ¡18 ¡showed ¡a ¡good ¡inhibiti

• n ¡comparable ¡to ¡that ¡of ¡acarbose. ¡

 In ¡contrast ¡to ¡acarbose, ¡the ¡potent ¡inhibitors ¡show ¡no ¡inhibition ¡of ¡hu man ¡pancreatic ¡α-­‑amylase. ¡It ¡may ¡overcome ¡the ¡side-­‑effects ¡of ¡acarbo

• se. ¡

 Cytotoxicity ¡will ¡be ¡examined. ¡ ¡

Acknowledgements