Transcript
BIOCHEMICAL APPROACHES FOR
DRUG DESIGN
SEMINAR BY:
T.SRAVYA
Drug design
Drug designing is an process of finding new medications based on the knowledge of the biological target.
TWO APPROACHES
1. RECEPTOR BASED APPROACH
2. GENE BASED APPRAOCH
Receptor Structure
Known Unknown
Structure BasedDrug Design
Analog BasedDrug Design
Docking
Homology ModelingReceptor Mapping
REQUIREMENTLead Compound and
derivatives with biologicaldata
REQUIREMENTA Model Receptor
Molecular DynamicsSimulations
Rigid Docking
FlexiDock
Monte CarloSimulations
Simulated Annealing
Quantum Mechanical(BRABO) ANN
GA
PCA
CoMFACoMSIA
QuantumMechanicalDescriptors
QuantumMechanics forAlignment
SYBYL, INSIGHT II, CERIUS2, MOE, AMBER (CDAC), DOCK, AUTODOCK
SINGLE MOLECULE
QSAR
Receptor based Drug Design
Structure based Ligand based
What is Docking?
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Docking attempts to find the “best” matching between two molecules
Docking of Ligand to the Active site of Protein
3D Structure of the Complex
Experimental Information: The active site can be identified based on the position of the ligand in the crystal structures of the protein-ligand complexes
If Active Site is not KNOWN?????
Building Molecules at the Binding Site
Identify the binding regions Evaluate their disposition in space
Search for molecules in the library of ligands for similarity
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Put a compound in the approximate area where binding occurs and evaluate the following:
Do the molecules bind to each other?If yes, how strong is the binding?How does the molecule (or) the protein-ligand complex
look like. (understand the intermolecular interactions)Quantify the extent of binding.
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• Computationally predict the structures of protein-ligand complexes from their conformations and orientations.
• The orientation that maximizes the interaction reveals the most accurate structure of the complex.
• The first approximation is to allow the substrate to do a random walk in the space around the protein to find the lowest energy.
Ligand in Active Site Region
Ligand
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Examples of Docked structures
HIV protease inhibitors COX2 inhibitors
Exact Receptor Structure is not always known
• Receptor Mapping
The volume of the binding cavity is felt from the ligands. This receptor map is derived by looking at the localized charges on the active ligands and hence assigning the active site.
Receptor Map Proposed for Opiate
Narcotics(Morphine, Codeine, Heroin, etc.)
*6.5Å
7.5-8.5Å
Flat surface for aromatic ring
Cavity for part of piperidine ring
Focus of charge
Anionic site
R1R2
R3
Homology modeling
Predicting the tertiary structure of an unknown protein using a known 3D structure of a homologous protein(s) (i.e. same family).Assumption that structure is more conserved than sequence
How to construct homologousmodel?
• Find homologous sequence• Select the template sequence of known• structure• Align the template and the target structure• Build the model
•And finally… Build the model .It is the moment to use a MODELLER program
•In input: target, template sequence and theiralignment
•In output: the 3D structure responding ofthe constraints
Quantative Structure-Quantative Structure-Activity RelationshipsActivity Relationships
What is QSAR?
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002
Why QSAR?
• QSAR models are derived from a series of (similar) molecules with known activity (training set)
• If a statistically relevant QSAR model has been found, it can be applied to new molecules in this series (test set) in order to predict their activity before biological testing (or even before synthesis!)
Introduction to QSAR
Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002
Why QSAR?
• QSAR models are derived from a series of (similar) molecules with known activity (training set)
• If a statistically relevant QSAR model has been found, it can be applied to new molecules in this series (test set) in order to predict their activity before biological testing (or even before synthesis!)
Introduction to QSAR
QSAR and Drug Design
Compounds + biological activity
New compounds with improved biological activity
QSAR
Statistical Concept
Pi = k(d1, d2, d3,…. Dn) • pi= biological
activity• d1, d2 =
descriptors( calculated structural properties)
QSAR MODELLING APPROACHES
3D-QSAR A.CoMFA
VARIABLE QSAR SELECTION APPROACHES A.LINEAR MODEL B.NON LINEAR
Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002
1. Superimpose 3D models of molecules("Alignment")
2. Generate a regular grid around the molecules
3. Calculate and tabulate steric and electrostatic interaction energy of each grid point and each molecule
Compound Number Biol. Activity Steric Interaction
S001
ElectrostaticInteraction
E001
Steric Interaction
S002
ElectrostaticInteraction
E002
Steric Interaction
S003
ElectrostaticInteraction
E003
Steric Interaction
S004
ElectrostaticInteraction
S004...
1 1.07
2 0.09
3 0.66
4 1.42
5 -0.62
6 0.64
7 -0.46
3D QSAR - The CoMFA Approach
Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002
Compound Number Biol. Activity Steric Interaction
S001
ElectrostaticInteraction
E001
Steric Interaction
S002
ElectrostaticInteraction
E002
Steric Interaction
S003
ElectrostaticInteraction
E003
Steric Interaction
S004
ElectrostaticInteraction
S004...
1 1.07
2 0.09
3 0.66
4 1.42
5 -0.62
6 0.64
7 -0.46
3D QSAR - The CoMFA Approach
4. Derive a QSAR equation (typically by PLS analysis)
Biol. Activity = Const. + a( S001) + b( E001) + c( S002) + d( E002) + ...
5. Apply equation to test setor contour fields of same coefficients
For the capsaicin example, CoMFA predicted Log EC50=-0.21!
CoMFA 3D-QSAR
Problems:The molecules must be optimally aligned.Flexibility of the molecules.
3D-QSAR of CYP450cam with CoMFA
Maps of electrostatic fields: BLUE - positive chargesRED - negative charges
Maps of steric fields:GREEN - space filling areas YELLOW - space conflicting areas
ACHIEVEMENTS
• Forecasting of biological activity•Selection of proper substituents •Drug receptor interactions•Pharmacokinetic information•Bioisosterism
GENE BASED APPROACH
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Gene-based Approach
Gene Therapy
Transfer of a therapeutic gene into the target tissue and maintenance of the gene function for an acceptable time
Potential Target Diseases for Gene Therapy
Disease Deficient Gene Affected Tissue
Cystic Fibrosis CFTR Lung, intestine Familial
hypercholesterolemia LDL Receptor Liver
Emphysema 1-AT Liver Hemophilia A and B Factor VIII and IX Blood plasma Duchenne muscular
dystrophy Dystrophin Muscles
-thalassaemia -globin Erythrocytes Phenylketonuria PAH Liver
Cancer Various Various Parkinsons Dopamine synthesis Brain Alzheimers Apo E/amyloid inhibition Brain
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Gene-based Approach
Gene Therapy – Basic Steps
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Gene-based Approach
Gene Therapy – Basic Steps
• Discover Genes
• Design Replacements
• Deliver to cell / body
• Ensure Incorporation
• Detect Function
• Ensure Stability
• Test Toxicity
• Test long-term effects
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Gene-based Approach
Gene Therapy – Delivering Genes
Basic Methods for Gene Transfer in Cell Culture
Method Efficacy Stability
Physical Methods Electroporation Moderate Short/long Microinjection High Short/Long
Particle Bombardment High Short
Chemical Methods Calcium phosphate Low/moderate Short/long
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Delivering Genes – Cells
THANK YOU
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