in silico drug designing
Post on 14-Jul-2015
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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.
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
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 .
• Look for the bad contacts .
To remove bad contacts we use Spdbvfor energy minimization .
Upload the structure on Spdbv .
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
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 :
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
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
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