Pharmacophore generation for virtual screening

Olivier Taboureau otab@cbs.dtu.dk

Computational Chemical Biology CBS-DTU

Plan •  Pharmacophore: main interest in drug discovery,

definition, chemical features. •  Different aspect to take account (pH, conformation,

alignment, binding site). •  Pharmacophore flexibility (example with hERG

potassium channel). •  Automatic identification of pharmacophores.

Pharmacophore: main interest in drug discovery,

definition, chemical features.

•  Chemoinformatics refers to the building and use of chemical databases and linked the information related to (like chemical and/or biological properties) for the identification or optimisation of new drugs.

•  An important part of drug design is the prediction of small molecules binding to a target protein. (docking can do that with small set of compounds)

•  A pharmacophore is a reasonable qualitative prediction of binding by specifying the spatial arrangement of a small number atoms of function groups. The prediction can be done on large databases.

Chemoinformatics and drug design

A pharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response. A pharmacophore does not represent a real molecule or a real association of functional groups, but a purely abstract concept that accounts for the common molecular interaction capacities of a group of compounds towards their target structure.

Pharmacophore definition

Pharmacophore: chemical features •  The chemical features can be hydrogen bonds acceptors, hydrogen bond

donors, charge interactions, hydrophobic areas, aromatic rings, positive or negative ionizable group.) The shape or volume is also considered.

•  Pharmacophores represent chemical functions, valid not only for the curretly bound, but also unknown molecules.

Hyd Acc

Acc

Acc & Don

Aro

Start to be complex !!!

Atom is acceptor if he can attract an hydrogen (nitrogen, oxygen or sulfur and not an amide nitrogen, aniline nitrogen and sulfonyl sulfur and nitro group nitrogen), and donor if he can give an hydrogen.

Donor

Acceptor

Acceptor

Donor

Don Acc Acc Acc & Don

Acc & Don Acc Acc

Acc & Don

Aro ring center

Example 1

Effect of pH Acceptor

Donor

pH = 7

pH = 1

Example 2 with 3 inhibitors

•  Dopamine (2 rotations and 2 OH groups)

•  Apomorphine (no rotations)

•  5-OH DPAT (one OH group and many rotation)

Agonist at D2 receptor

Example 2 Active agonists define important groups:

–  -Aromatic ring –  -meta OH group –  -N atom, right distance from aromatic ring –  -other molecular ”scaffolding does NOT show a consensus.

Different aspects to take account

Which conformation?

•  Some drugs are rigid (e.g, strychnine, a Glycine receptor antagonist)

•  But most drugs have some conformational flexibility, and can have different shapes (e.g, sildenafil)

Pharmacophore should be presented only by high energy conformation (Xray, NMR, minimisation, stochastic search)

•  Alignment from models

With a group of compounds

•  Can be messie!!!

•  Alignment from Xray structures

•  Can be messie too !!!

•  Binding site activity is flexible too

Sometime problem with antagonists

•  Antagonist active conformation may be different from agonist •  Extra binding site: ”umbrella effect” •  Example with D2 receptor

Receptor Active site

Receptor Active site

Umbrella effect

Receptor

Active site

Receptor

Active site

Antagonists may bind at an additional site Example with µ opioids

agonist antagonist

Agonist binding site

Antagonist binding site

Pharmacophore flexibility

A pharmacophore is usually obtained by connecting the average spatial positions of the pharmacophoric points of all the molecules. But sometime, several binding site. Therefore, several pharmacophores can be a solution.

Example with hERG channel blockers

Are features of the site unique to hERG?

Automatic pharmacophore identification •  As many protein structures are described as sets of points,

pharmacophore identification is commonly reduced directly to the problem of finding common points to all functional ligand conformations.

 From X-ray crystallography measure X-ray structure with drug at the active site (can

sometime be done) or infer binding by measuring distance between likely binding groups.

 From comparison of active compounds The traditional way to identify binding groups.

 Automatic identification of pharmacophores (GALAHAD, Pharmacophore elucidation…)

Pharmacophore identification

Automatic Pharmacophore Elucidation

Automatic pharmacophore elucidation

 Identify low-energy conformations

 Run through multi-objective GA

 From an optimized sets of conformers, a hypermolecular alignment in Cartesian space is done.

 Score models based on 3D geometric consistency

generate a collection of pharmacophore queries from a collection of compounds some or all of which are active against a particular biological target.

Automatic pharmacophore elucidation in MOE

It is based on pharmacophore alignment

Automatic pharmacophore elucidation in MOE

Automatic pharmacophore elucidation in MOE

Reminder Weakness •  2D pharmacophore is faster but less accurate compared to 3D

pharmacophore.

•  It’s based only on the structure and conformation. No interactions with the proteins is integrated.

•  It’s sensitive to physicochemical features.

Advantages •  Pharmacophore can be used for virtual screening on a large database

•  It does not need to know the binding site

•  It can be used for the design optimization of a drugs and for the design of new scaffolds.

•  It can be run on 2D conformation

Time for a break