Introduction of CADD and its methods : 2006

1. “Science is for Society not for Publications”
Computer Aided Drug Designing
in Biology
Girinath G Pillai, Research Fellow,
State Inter University Centre of Excellence in Bioinformatics,
University of Kerala. Thiruvananthapuram.
giribio@scientist.com
http://giribio.weebly.com

2. Overview
• Introduction
• Drug Discovery
• Virtual Screening
– Types
– Screening Process
• Discovery of New Inhibitors
• Interpretations
• Tools / Softwares
• Summary
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3. Bioinformatics ?

4. Why? & Where?

6. Molecular Biology; Information Science
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7. Biological Data
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8. Drug Discovery Process
• Most drug molecules inhibit the activity of a
specific protein by blocking its active site.
• Examples of proteins targeted by drugs.
• HIV protease (ritonavir, viracept….)
• Dihydrofolate reductase
Target Lead Lead Pre-Clinical Clinical Market
discovery Identification Optimization test Trials Entry
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10. Discovering Lead Compounds
VS/HTS
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12. Computer Aided Drug Designing
• Why does it bind in this way?
• How can we make tighter
binding, more specific
compounds?
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13. Advantages of Virtual Screening
• “hit” or success rate can be 10 – 30%,
whereas in HTS it is usually <0.1%
• avoids the cost of assaying compounds that do
not fit the binding site.
• provides a clear structural hypothesis for ligand
binding mode and interactions with the protein.
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14. Virtual Screening
Ligand-based virtual screening
• The 3D structure of the biological target is unknown and a
set of geometric rules and/or physical-chemical properties
(pharmacophore model) obtained by QSAR studies are used
to screen the database.
Structure-based virtual screening
• It involves molecular docking calculations between each
molecule to test and the biological target (usually a
protein). To evaluate the affinity a scoring function is
applied. The 3D structure of the target must be known.
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15. Ligand Based
• 3D-QSAR method that search for relationship
between the biological activity of a set of compounds
(with specified alignment) and their three-dimensional
physicochemical properties (so-called molecular fields).
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16. Structure based
+
Docking software
Ligand Receptor
• The complex quality is evaluated by the
score.
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Ligand – receptor complex

17. How Protein is a Target/Receptor?
• Similarity Analysis
• Motif Identification
• Subcellular Location
• Instability Index
• Aliphatic Index
• Half Life Period
• Protein Surface Scan
• Secondary Structure Analysis
• Modelling & Simulation
• Electrostatic Analysis
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Girinath G. Pillai @ VIT, Vellore

18. Automated docking methods
• The scoring (or energy) function.
• The stratergy used to search for the lowest score
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19. Docking Tools
Ligand flexibility Suitability for
Method Scoring function
sampling large-VS
Force field or
Dock Incremental build High
contact score
FlexX Incremental build Empirical score High
Conformational
Slide Empirical score High
ensembles
Conformational Gaussian score or
Fred High
ensembles empirical scores
Gold Genetic algorithm Empirical score Low
Glide Exhaustive search Empirical score Low
AutoDock Genetic algorithm Force field Low
LigandFit Monte Carlo Empirical score Low
Pseudo-Brownian Mixed force field
ICM sampling local and empirical Low
minimization score
QXP Monte Carlo Force field Low
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20. Molecular
Docking
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21. Virtual Screening
Computational or in silico analog of biological screening
ACD
WDI
NCI
X-ray Crystallography
NMR
homology modeling
Ranking
HITS
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22. Docking Process - Conformers
Kubinyi, H.:
“Success Stories of Computer-Aided Design”, in:
Computer Applications in Pharmaceutical Research and Development, Sean Ekins (Ed.), Wiley
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23. Docking Search Method
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24. Dynamics & Simulation
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25. Thank You
www.giribio.weebly.com
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