Pharmacophore generation for virtual screening Olivier Taboureau [email protected] Computational Chemical Biology CBS-DTU
Pharmacophore generation for virtual screening
Olivier Taboureau [email protected]
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