Pharmacophore identification

3D pharmcophore representation and analysis

1. S.Prasanth Kumar, S.Prasanth Kumar,
BioinformaticianBioinformatician
Drug Design
Pharmacophore IdentificationPharmacophore Identification
S.Prasanth Kumar, S.Prasanth Kumar,
BioinformaticianBioinformatician
S.Prasanth Kumar
Dept. of Bioinformatics
Applied Botany Centre (ABC)
Gujarat University, Ahmedabad, INDIA www.facebook.com/Prasanth Sivakumar
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2. Pharmacophore
A pharmacophore that indicates the key features of a series of
active molecules
In drug design, the term 'pharmacophore‘ refers to a set of features
that is common to a series of active molecules
Hydrogen-bond donors and acceptors, positively and negatively
charged groups, and hydrophobic regions are typical features
We will refer to such features as 'pharmacophoric groups'
H HBD HBA R

3. Bioisosteres
Bioisosteres, which are atoms, functional groups or molecules
with similar physical and chemical properties such that they
produce generally similar biological properties

4. 3D-Pharmacophores
A three-dimensional pharmacophore specifies the spatial relation-
ships between the groups
Expressed as distance ranges,angles and planes
A commonly used 3D pharmacophore for antihistamines contains
two aromatic rings and a tertiary nitrogen

5. Constrained Systematic Search
Deduce which features are required for activity
Angiotension-converting enzyme (ACE), which is involved in
regulating blood pressure
Four typical ACE inhibitors Captopril
Interacts with an
Arg residue of
enzyme
a zinc-binding group
H bonds to a hydrogen-bond donor in enzyme

6. Constrained Systematic Search
Systematic search over all
molecules
Combinatorial explosion
No systematic conformational
analysis
Considered
Reduces torsion angles of the rotatable bonds = reduced conformational space
Conformational space Not Explored
Systematic search over 20-30
molecules
Combinatorial explosion
associated with a systematic
conformational analysis
Exhaustiveness Systematic search
Choose the most conformationally
restricted molecules first
Selection

7. Constrained Systematic Search
Evaluated distance for
1
st
molecule
Permitted distances for
1
st
and 2
nd
molecule
4 points 5 distances

8. Ensemble Distance Geometry
Used to simultaneously derive a set of conformations with a previously
defined set of pharmacophoric groups overlaid
Special Feature : conformational spaces of all the molecules are
considered simultaneously
Nicotinic agonists (Previously defined sets: A,B and C)
N
1
= no. of atoms in
molecule 1
N
2
= no. of atoms in
molecule 2
N
3
= no. of atoms in
molecule 3
N
4
= no. of atoms in
molecule 4

9. Ensemble Distance Geometry
Distance matrix construction
Dimensions = sum of the atoms in all the molecules.
Specify lower and upper bounds
Lower bounds for atoms that are in different molecules = zero
molecules can be overlaid in 3D space
Upper bounds for pairs of atoms that are in different molecules = large value
Required to be superimposed in the pharmacophore
repeat

10. Ensemble Distance Geometry
A B C
Note: these are not pharmacophore features but pharmacophoric sets
A A BB C C
LB : 4.8 ˚A
UB : 5.1 ˚A
LB : 4.0 ˚A
UB : 4.3 ˚A
1.2 ˚A
No Bounds here
Remove distorted geometries
A
B
C
4.8 +/- 0.3 ˚A
1.2 ˚A
4.0 +/- 0.3 ˚A

11. Clique Detection Methods
When many pharmacophoric groups are present in the molecule
it may be very difficult to identify all possible combinations of
the functional groups
Clique is defined as a 'maximal completely connected subgraph'
Clique detection algorithms can be applied to a set of pre-
calculated conformations of the molecules
Cliques are based upon the graph-theoretical approach to molecular
structure

12. Clique Detection Methods
Graph G
G is not a completely connected
graph, because there is not an
edge between all the nodes.
subgraph S1 is not a
completely connected
subgraph, because
there is no edge between
nodes 1 and 8
S2 is a completely
connected sub-graph
S2 is not a clique,
because it is not a
maximal completely
connected subgraph;
S2 can be converted
into a clique C1 by
adding node 8
Another clique C2

13. Clique Detection Methods
Find similar pattern
cliques
O1(A)
O2(B)
O2(A)
O1(B)
H(A)
H2(B)
O1(A)
O1(B)
O2(A)
O2(B)
H(A)
H3(B) NEWLY ADDED NODE

14. Thank You For Your
Attention !!!