Anna Linusson

Department of Chemistry

Umeå University

Sweden

Virtual Screening

Virtual screening

N

NH

O

O

OH

Cl

O

O

S N

N

S

O

O

OH

N N

NS O

HN

O

NN

OSNH

O

NOO

N

N N

NH2

Free energy of binding

++

000 ln STHKRTG A

)( 000

''

0

LaqRaqaqLRG

Experimentally determined inhibition constants =10-2 to 10-12 M, i.e. 10 pM to10

mM; pKi= 2 to 8 (at T=298 K)

A one order of magnitude change corresponds to 5.7 kJ/mol or 1.36 kcal/mol

(1kcal = 4.18 kJ)

Virtual screening

Development Validation

O

O

HO

OH

6 (Embelin)

Preparation Execution Evaluation

Outline

• Methodology

– Ligand-based

– Structure-based

• What can we expect?

• Virtual screening vs. HTS

• Case study

• Take home message

Virtual screening

• Enthalpy

– Ionic interactions

– Hydrogen bonds

– Dipole-dipole moments

– Aromatic interactions

– van der Waal interactions

• Binding entropy

– loss of conformational flexibility

Solvation/desolvation

Flexibility (degrees of freedom)

++

Virtual screening

• Similarity principle

• Receptor-based approach

A plethora of methods/approaches to

choose from

O

O

HO

OH

6 (Embelin)

Case dependent

Don’t trust retrospective studies

What do I know?

Years 1 16 2 3 4 5 6 7 8 9 10 11 12 13 14 15

No. of compounds Up to 10,000 10-15 1-8 1-3 1

First patent application

Clinical trial application

Product licence application

Drug Discovery Drug Development Target and lead identification

Lead optimisation Concept testing

Development for launch Launch

Clinical Development Phase I 50-150 people

Phase II 100-200 people

Phase III 500-5,000 people

Phase IV studies continue

Product life cycle support

Toxicology and pharmacokinetic studies (absorption, distribution, metabolism, excretion)

Pharmaceutical and analytical development

Process chemistry and manufacturing

Registration and regulatory affairs

Sales and marketing (preparation, promotion, advertising and selling)

Cost $3 million $225 million $450 million

Drug Discovery and Development

Virtual screening - methodology

Ligand-based

• Demands a (set of) known

ligands/active molecule/s

• These molecules may be

structurally similar or different

• Validated inactive molecules may

also be of use

• Demands a 3D-structure of the target

receptor (protein)

• These may be crystal structures,

NMR structures or homology models

• Several structures of the same target

bound to different ligands is an

advantage

Structure-based

Read more: Taboureau, O. et al. Chemistry & Biology, 2012, 19, 29-41.

Klebe, DDT, 2006, 11: 580-594

Shoichet, Nature, 2004, 432: 862-865

O

O

HO

OH

6 (Embelin)

Virtual screening – generic procedures

Development Validation

Preparation Execution Evaluation

• Source of chemicals (e.g. commercial)

• Selection of known ligand/s and/or

target/s

• Preparation of chemical structures – Chemicals and ligands: 2D, 2D->3D

– Macromolecules: X-ray, NMR, homology models

– Tautomers

– Protonation states

– Stereochemistry

– Conformations

• Experimental evaluation

• Confirmation of hits – Biologically

– Chemical structure

• Follow up

Ligand-based virtual screening

Screening methods

• Descriptor-based

• Shape-based

• Pharmacophore-based

• ...

Prerequisite

One or a set

of known

active

molecules

• Unsupervised (similarity search)

• Supervised (regression models)

O

O

HO

OH

6 (Embelin)

Descriptor-based screening

Preparation

• Calculation of descriptors

• Fingerprints

• Physical properties

• 3D-GRID descriptors

• Selection of similarity measure

(Tanimoto)

• Creation of a quantitative structure-

activity relationship

• Linear regression model

• Non-linear regression model

• Rule-based model

• Assessment of model quality

• Applicability domain

Based on the assumption that a similarity in compound physicochemical descriptors

(i.e. physicochemical properties) will lead to a similar biological action

Execution

• Estimation of similarity

• Prediction of the biological

activity of the compound library

structures using the QSAR model

Evaluation

• Use the ranking list from execution

• Cut offs

• Selection criteria (diversity)

• Additional filtering

• Clustering

Shape-based screening

The goal is to find compounds similar in 3D-shape to the query molecule(s)

Preparation

• Generation of 3D structures

• (Alignment of structures)

• Calculating volumes

• Creation of shape-query

• (Combine with electrostatics)

• Selection of similarity measure

Execution

• Screening of the compound

database (3D-structures) based on

overlapping volumes

• Multiple shape queries

• A good overlap will be ranked high

Evaluation

• Prioritization of hit compounds

• Cut offs

• Chemical features

• Combination of shape queries

Read more: Kirchmair et al, J. Chem. Inf.

Model. 2009, 49, 678–692

Pharmacophore-based screening

Preparation

“ A pharmacophore is an abstract (3D) description of chemical features in a

molecule, necessary for biological recognition ”

• Generation of low-energy 3D

structures of the active

molecules

• Alignment of structures

• Identification of 2-4 common

features

• Creation of 3D-pharmacophore

• Assign pharmacophore features

to a search chemicals

• Select measure

Align analogues

Create

pharmacophore

Pseudo-receptor

Pharmacophore-based screening

Execution

• Fit molecules (i.e. the

pharmacophore features) in the

database to the generated

pharmacophore

• A good match is ranked high

Evaluation

• Prioritization of hit compounds

• What about diversity among

the hits?

• Many different pharmacophores

Read more: Kim et al., Expert Opin. Drug

Discov., 2010, 5(3),205-222

Structure-based virtual screening

Molecular Docking

• Rigid receptor - flexible ligands

• “Soft” receptor - flexible ligands

• Multiple receptors

• Induced-fit docking

Prerequisite

Structure of known target

Docking-based virtual screening

Preparation

• Preparation of protein structure(s)

• Addition of hydrogens

• Optimization of H-bond networks

• Treatment of water

• Protonation state and tautomers

• Definition of binding site

• Optional settings depending on docking

software

• Inclusion of water

• Movement of side-chains

• Energy minimization of resulting

complexes

Structure-based virtual screening

Re-docking (and cross-docking) of the crystal structure ligand may be guide the choice of docking software

Docking-based virtual screening

Structure-based virtual screening

Execution

• Generation of molecule conformations

• Generation of docking-poses for each

molecule

• Judgment of clashes and interactions

between ligand and protein

• Depending on docking software

• addition and removal of water

molecules in the binding site

• docking filtering (removal unwanted

molecules based on e.g. absence of

specific interactions with the protein)

Docking-based virtual screening

Evaluation

• Scoring of docking poses using one

scoring function

• Scoring using several scoring functions

(consensus scoring or fused)

• Selection of a subset of molecules using

a simple scoring function and re-scoring

with a computational intensive function

• Biological evaluation

Choice of scoring function

may be guided by the scoring

of known active and inactive

molecules

Example of a “simple” scoring function that

estimates the free energy of binding (∆Gbind ):

∆Gbind= ∆Gsolv +∆Gint +∆Gconf +∆Ghbond +∆Gelect

Account for (some) flexibility

Multiple receptors

• Crystal structures of the same protein

with different conformations induced

by different ligands

• Conformation ensembles of a protein

generated by MD-simulations

• “Manual” alterations of proteins

structures (e.g. conformations of

flexible loops)

Soft receptors

• Mimics movement by varying the

Lennard-Jones potential of the

receptor atoms (i.e. the distance at

which atoms clash)

• Allowing for more “clashes”

• Variable binding site size

Ligated complexes usually performs better than apo-structures

The complex with the largest ligand is usually a good choice

Account for (some) flexibility

• Movements or position of hydrogen

atoms attached to hetero atoms (O, N

or S)

• Flips of histidine, aspargine and

glutamine side chains (i.e. tautomers)

• Allowing for movements of user-

specified receptor side chain and/or

backbone atoms.

• The entire complex is optimized (e.g.

the protein is forced to adopt to the

ligand)

Further reading: Kokh et al.,, Comput. Mol. Sci.,

2011, 1, 298–314

Demands a scoring function that can account for protein internal energy changes

Induced-fit docking

Water

• Tools to predict important waters

• Docking tools to account for waters

on/off

Crystal structure can guide for important waters Highly conserved waters can be included The benefit of included waters is questionable If any – then case dependentc

To include or not to include?

NB: It is not questionable that

water is important

Using multiple methods

Compound Library

106-108

104-105

102-103

Fast: Rigid/shape based filtering,

Fast: Pharmacophore/substructure searches,

Fast: High-throughput docking

Slow: Flexible docking

Fast: Rescoring,

Slow: Visual Inspection

ex. ROCS, FRED

ex. GOLD, Glide,

FlexX, ...

Biological evaluation

Which method/approach should I use?

• How to best use existing information about ligands and targets?

• What is your goal?

• Combining methods is often beneficial

Nicholls, JCAMD, 2008,

22:239-255

Warren et al., JCM, 2006,

49: 5912-5931

Case dependent

Don’t trust retrospective studies

What can we expect?

Marsden et al. OBC, 2004, 2:3267-3273

Enyedy and Egan, JCAMD, 2008, 22:161-168 Scoring functions cannot

predict binding affinities

What can we expect?

++

000 ln STHKRTG A

31 pitfalls: Agrafiotis and co-workers,

JCIM, 2012, 52:867-881

Pitfalls and traps: Hawkin et al., JCAMD,

2008, 22:170-179

Shoichet, Nature, 2004, 432: 862-865

Klebe, DDT, 2006, 11: 580-594

• Limitations in fundamental knowledge

• Assumptions made (flexibility, water, simplifications)

• Bad science (pre-treatments, statistics, parameters, validations)

Sources of errors in virtual screening

Knowledge-based driven approach

What can we expect?

Enrichment. Increase the likelihood of finding promising hits.

To what good? • Recover false negatives from HTS

and disclose false positives

• When only a smaller library can be

screened

Hit rates:

Virtual screening and HTS are complementary methods

Ferreria et al., JCM, 2010, 53: 4893-4905

Polgár et al. Comb. Chem. High Throughput Screening, 2011,

14, 889-897.

Outline

• Methodology

– Ligand-based

– Structure-based

• What can we expect?

• Virtual screening vs. HTS

• Case study

• Take home message

ADP-ribosyltransferases

• Post-translational

modification

• NAD+ as cosubstrate

• Mono- or Poly-

ribosylation

• Writers in epigenetics

The nomenclature... ARTD: diphteria toxin-like ADP-ribosyltransferase

also known as PARP: poly ADP-ribose polymerase

Docking-based virtual screening

...to find new small molecules that bind to ADP-ribosyltransferases

...to develop selective ligands for different ADP-ribosyltransferases

...to use the molecules as chemical tools in the elucidation of biological

functions

Andersson et al., JCM,

2012, 55: 7706-7718

Virtual screening

PARP15 PARP14

What do we know?

And what do we want?

Virtual screening methodology

Preparation • Re-docking and cross-docking of

crystal structures

• Evaluation of docking tools and

settings

Chemicals

• In house compound collection

• Protonation states, tautomers and

stereoisomers

• Determination of features and property

criteria

• Filtering of compound collection

Proteins

• Investigation of D-loop conformations of

all crystalized PARPs

• Selection of protein structures

• Selection of conformers

• Protein preparation (hydrogens,

tautomers, protonation states)

• Definition of binding site

Known binders

• Collection of list known binders based on

crystal structures and/or experimental

assays

• Protonation states, tautomers and

stereoisomers

Compound Database Target/s

Virtual screening methodology

Execution Evaluation

Docking score resemblance

Previously known binders

Compounds selected for testing

Compounds were selected based on scoring

similarities to known binders

Selection of compounds for wet lab

• 64 were chosen by our scoring method and 64 (47) were selected

solely based on Glidescore

• 111 molecules were selected for biological testing

• No enzymatic assay available!

Senisterra et al. Mol. BioSyst., 2009, 5, 217-223

Thermal shift assay

Hits for PARP14 and/or PARP15

ΔTm of 1.5-3.9 degrees

Identified by scoring resemblance Identified by Glidescore

Multiple protein structures

• Hits were identified from screening against all

protein structures

• 63% of the hits were identified from PARPs with

unaltered D-loop

• Three hits were exclusively identified from

structures with truncated loop (including the two

best hits)

Selectivity profile

PARP15 PARP14 PARP1

All 111 compounds experimentally tested for binding to PARP1

Hit validation

OH

O

O

NH

O

NH2

O

NHO

H2N

O OH

A16(E)

A16(Z)

Hit validation

A16(Z) A16(E)

Kd = 7.6 μM Kd = 11.2 μM

O

NHO

H2N

O OH

OH

O

O

NH

O

NH2

Isothermal titration calorimetry

Crystallization of A16 and PARP14

O

NHO

H2N

O OH

Resolution 1.9 Å Resolution 2.8 Å

Z-isomer A16

OH

O

O

NH

O

NH2

E-isomer A16

Binding mode of A16 to PARP14

Crystal structure of A16 (E and Z)

1

2

Docking pose of A16(E)

Case summary

• 16 binders (8 different structural classes)

=> hit rate of 14 %

• Our scoring method found structurally diverse

compounds

• Both isomers of A16 binds to PARP14 (~10μM)

• Crystal structures of the A16 isomers confirmed

docking poses

• The virtual screen provided good starting points to

develop selective inhibitors

Take home message

• Virtual screening increases the likelihood of finding

hits in a set

• Virtual screening is a knowledge-based method –

use available information

• Don’t trust retrospective studies

• Virtual screening is a complementary method to

HTS

Acknowledgements

Department of Chemisty, UmU

Linusson’s Lab

David Andersson

Lotta Berg

Brijesh Kumar Mishra

Cecilia Lindgren

Urszula Uciechowska

Cecilia Engdahl

Elofsson’s Lab

Sara Spjut

Anders Lindgren

Rémi Caraballo

Wittung-Stafshede’s Lab

Pernilla Wittung-Stafshede

Moritz Niemiec

Laboratories for Chemical Biology

Umeå (LCBU)

Karolinska institutet

Schüler’s Lab

Tobias Karlberg

Torun Ekblad

Ann-Gerd Thorsell

Johan Weigelt

Financial Support

The Swedish Research Council

Umeå University

Swedish Foundation for Strategic Research