3D pharmcophore representation and analysis

1. S.Prasanth Kumar, Bioinformatician Drug Design Pharmacophore Identification S.Prasanth Kumar, Bioinformatician S.Prasanth Kumar Dept. of Bioinformatics Applied Botany Centre (ABC) Gujarat University, Ahmedabad, INDIA www.facebook.com/Prasanth Sivakumar FOLLOW ME ON ACCESS MY RESOURCES IN SLIDESHARE prasanthperceptron CONTACT ME [email_address]

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 B B 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

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