This document describes the process of in silico drug design to target the hemagglutinin protein of the H1N1 influenza virus (swine flu). The key steps include: identifying hemagglutinin as the target, generating a homology model, identifying the active site, screening existing drugs and generating new ligands, performing rigid and flexible docking simulations to identify ligands that bind strongly to the active site, and selecting the best candidate to inhibit hemagglutinin function and potentially treat swine flu infections. The overall process takes an in silico approach to rational drug design against a specific viral protein target.

1. In silico DRUG DESIGNING of haemagglutin protein
Palwinder KAUR

2. AIM
• To design a drug against
HEMAGGLUTININ protein
that has been taken as a
drug target for H1N1
flu ( SWINE FLU).

3. 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.

4. WHAT IS DRUG DISCOVERY PROCESS ?
• The whole drug discovery process takes at least
10 to 15 years .

5. 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

6. TYPES OF IN SILICO DRUG DESIGNING
IN SILICO
DRUG
DESIGNING
LIGAND BASED
DRUG
DESIGNING
STRUCTURE
BASED DRUG
DESIGNING

7. 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

8. 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

9. 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

10. RETRIEVAL OF PROTEIN SEQUENCE OF HAEMAGGLUTININ

11. BLAST OF PROTIEN SEQUENCE WAS PERFORMED

12. STEPS OF HOMOLOGY MODELLING
TEMPLATE
IDENTIFICATION ALIGNMENT
BACKBONE
MODELLING
LOOP
REFINEMENT &
SIDE CHAIN
MODELLING
MODEL
GENERATION
MODEL
OPTIMIZATION
MODEL
REFINEMENT

13. GENERATION OF MODEL USING EASY MODELER
• First three templates are
selected and their
structure are downloaded
from pdb.

14. • After that download the
structure of first three
templates from
pdb 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 .

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

16. •Look for the bad contacts .
 To remove bad contacts we use Spdbv
for energy minimization .
 Upload the structure on Spdbv .

17. Repeat the step until the bad contacts became 0

18. ACTIVE SITE IDENTIFICATION
•Active site visualization on PYMOL

19. 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

20. DESIGN THE LIGAND USING CHEMSKETCH
After this convert this format using
Open babel in PDB format to perform
rigid docking and to view the
structure in 3D

21. VISUALIZATION USING PYMOL

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

23. 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 :

24.  Running Process
 Running Grow

25. 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

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

27. 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 .

28. 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

29. 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