In silico DRUG DESIGNING of haemagglutin protein

Palwinder KAUR

AIM

• To design a drug against HEMAGGLUTININ protein that has been taken as a drug target for H1N1flu ( SWINE FLU).

WHAT ARE DRUGS ?

•The term "drug" means articles intended for use in the diagnosis, cure, treatment, or prevention of disease in man or other animals.

WHAT IS DRUG DISCOVERY PROCESS ?

• The whole drug discovery process takes at least 10 to 15 years .

IN SILICO DRUG DESIGNING

• In Silico drug designing is defined as the identification of the drug target molecule by employing bioinformatics tools .

• To analyze the target structures for possible binding/ active sites.

•Generating candidate molecules, checking for their drug likeness.

•Docking these molecules with the target

• Ranking them according to their binding affinities.

• Further lead optimization to improve binding characteristics

TYPES OF IN SILICO DRUG DESIGNING

IN SILICO DRUG

DESIGNING

LIGAND BASED DRUG

DESIGNING

STRUCTURE BASED DRUG DESIGNING

H1N1 FLU ( SWINE FLU )• In the past, the people who caught it had direct

contact with pigs.

• That changed several years ago, when a new virus emerged that spread among people who hadn't been near pigs.

• Swine flu is contagious

• SYMPTOMS :

Body aches Cough

Fatigue Chills

STEPS FOR STRUCTURE BASED DRUG DESIGNING

DISEASE SELECTION

TARGET SELECTION

HOMOLOGY MODELLING

ACTIVE SITE IDENTIFICATION

INHIBITOR GENERATION

RIGID DOCKING

LIGAND GROWING

FLEXIBLE DOCKING

BINDING AFFINITY

DEVELOPMENT OF DRUG AGAINST SWINE FLU

• Target identification : After searching different databases and reading different research papers I came to know about the various target proteins of swine flu .

TARGET PROTEIN IDENTITY TEMPLATE

HAEMAGGLUTININ 81% 4F15

RETRIEVAL OF PROTEIN SEQUENCE OF HAEMAGGLUTININ

BLAST OF PROTIEN SEQUENCE WAS PERFORMED

STEPS OF HOMOLOGY MODELLING

TEMPLATE IDENTIFICATION ALIGNMENT

BACKBONE MODELLING

LOOP REFINEMENT &

SIDE CHAIN MODELLING

MODEL GENERATION

MODEL OPTIMIZATION

MODEL REFINEMENT

GENERATION OF MODEL USING EASY MODELER

• First three templates areselected and their structure are downloadedfrom pdb.

• After that download the structure of first three templates frompdb and upload the three files on easy modeler .

Then click on align template and after that click on align query with the template and then click on generate model .

Now for the verification of structure upload the structure on SAVS .

• Look for the bad contacts .

To remove bad contacts we use Spdbvfor energy minimization .

Upload the structure on Spdbv .

Repeat the step until the bad contacts became 0

ACTIVE SITE IDENTIFICATION •Active site visualization on PYMOL

SELECTION OF INHIBITOR• We can search the drugs on drugbank for swine flu . The purpose of doing this

is to find the similar structure for designing the seed molecule for our receptor . Hence I took 15 drugs and their structure from the drugbank

CAFFEINE CHLOROPHENAMINE DIPHENHYDRAMINE

DOXYLAMINE FENTEROL

DESIGN THE LIGAND USING CHEMSKETCH

After this convert this format usingOpen babel in PDB format to perform rigid docking and to view thestructure in 3D

VISUALIZATION USING PYMOL

RIGID DOCKING USING HEX• The Hex software fits the ligand in the free space near the active site

LIGAND GENERATION • For growing small ligand into full pharmacore molecule which

is to be used in the flexible docking is done by Ligbuilder .

• Running files using Ligbuilder :

Running Pocket :

Running Process

Running Grow

SCREENING OF LIGAND ON BASIS OF BINDING AFFINITY

• The process molecule of ligbuilder is performed to 10 inhibitors . Out of which 6 are selected on the basis of lipinski’s rule of five and other factors like mutagenic , tumurogenic , irritant and drug likeness .

• The structures of drug molecules generated were seen on pymol and after that drawn on molinspiration to find and the best of them and also to check the effectiveness of drug they were again drawn on Osiris to check whether any of the drug is mutagenic , turmurogenic etc

• From the logp values and molecular weight following drug was selected for flexible docking .

FLEXIBLE DOCKING USING AUTODOCK

• Autodock is run by taking two input files : Receptor and ligand in .pdb format . The output of Ligbuilder is in .mol2 format . It needs to be convert .mol2 files into .pdb format. The conversions are done using Openbable software . Autodock performs docking by setting the grid describing the target protein .

VISUALIZATION OF THE RESULTANT DRUG MOLECULE USING DISCOVERY STUDIO• After performing flexible docking we used discovery studio to see the interaction between

the target and the drug . The best of them is selected on the basis of interaction between them . As it shows OH type of bond linkage between the active site ( trp 77 ) and the selected drug

CONCLUSION

• In the selection of new drug candidates, many efforts are focused on the early elimination of compounds that might cause several side effects or interact with other drugs. In silico techniques help in this regard and they are going to become a central issue in any rigid drug discovery process.

• In silico technology alone cannot guarantee the identification of new, safe and effective lead compound but more realistically future success depend on the proper integration of new promising technologies with the experience and strategies of classical medicinal chemistry