Transcript Oct 06/AMJ Computational decision support for drug discovery Property profiling and virtual screening of small molecule libraries Anne Marie Munk Jørgensen.

Oct 06/AMJ
Computational decision support for drug
discovery
Property profiling and virtual screening
of small molecule libraries
Anne Marie Munk Jørgensen
Lundbeck
Oct 06/AMJ
Focus solely on treatment of diseases in the central nervous system
(CNS)
•depression
•Psychoses
•Migraine
•Alzheimer
•Sleep disorders
5000 people worldwide – app 800 in R & D
Outline
Oct 06/AMJ
o Introduction: What is a small molecule drug?
o Introduction: Drug Discovery process
o How can computational methods help during the
drug discovery phase?
o Compound Library profiling according to
• Predicted physical-chemical properties
• Predicted biological activities (virtual screening)
o Protein Homology modelling study
A small molecule drug
Oct 06/AMJ
… is a compound (ligand) which binds to a protein, often a receptor
and in this way either initiates a process (agonists) or inhibits the
natural signal transmitters in binding (antagonists)
The structure/conformation of the ligand is complementary to the
space defined by the proteins active site
The binding is caused by favourable interactions between the ligand
and the side chains of the amino acids in the active site.
(Electrostatic interactions, hydrogen bonds, hydrophobic
contacts…)
The ligand binds in a low energy conformation < 3 kcal/mol
Binding site complementarity
Oct 06/AMJ
HIV-Portease inhibitor
JACS,V.16,pp847 (1994)
H-bond donating
H-bond accepting
Hydrophobic
Flo98, Colin McMartin.
J.Comp-Aided Mol. Design,
V.11, pp 333-44 (1997)
Example of ligand binding
Oct 06/AMJ
1UVT,
Trombin
Inhibitor
No vacancy!
Oct 06/AMJ
Drug Discovery
Oct 06/AMJ
Disease
Molecular target
Proof-of-concept
Drug discovery program
Natural ligand
Bioinformatics
Molecular Modelling
Computational Chemistry
X-ray crystallography
Screening
SAR
Lead
In-vivo
pharmacology optimisation
In-vitro
pharmacology
Drug
Development
1-2-3
Chemistry
Mission Of Computatonal chemistry
group
Oct 06/AMJ
Use Computational Chemistry
analyses to identify the ”right”
compounds to screen
i.e. molecules that can modulate targets and
have suitable properties to become drugs
Choosing the right compounds to screen
Oct 06/AMJ
Compounds should have properties so that they can be
turned into drugs  property profilling
Select subsets based on either:
 diversity: select small representative sets (10,000100,000) from large numbers (1-4,000,000)
 target-specific knowledge:
 select focused sets based on common features needed for
a whole target class (e.g. kinases, G-protein coupled
receptors …)
 use ”virtual screening” to computationally screen the screening file (e.g 2,000,000 compounds) to identify compounds
for experimental biochemical screening (e.g. 1,000)
Knowledge-based screening
Oct 06/AMJ
Exploit any knowledge of the target and/or active ligand(s) and/or gene family
Virtual (in silico) Screening:
 computationally screen the compound file to
identify subsets that are enriched in actives
for biological evaluation
 can also be applied to virtual libraries (to identify
compounds/libraries to synthesise or purchase
Oct 06/AMJ
…………… Property profilling…………
Lipinski statistics on marketed CNS drugs
Oct 06/AMJ
MW
# hydrogen
acceptors
# hydrogen
donors
logP
# rotatable bonds
1
CNS Like,
Drug Like1 present work,
90% limit.
< 500
149.4 – 446.6
< 10
1-5
<5
0-3
<5
NR
-0.3 – 4.9
0 – 8.4
Lipinski et al., Adv.Drug Del. Revs. 23 (1997) 3.
Rule of 5
”Chemical space” navigator
Oct 06/AMJ
Global Positioning System (GPS)
PCA
1Oprea
& Gottfries, J. Comb. Chem 2001
CNS model
Oct 06/AMJ
PCA
12 descriptors 
3 components,
R2X=0.71
Blue dots define::
CNS drug space
CNS ”world” sub classes
Oct 06/AMJ
O
O
F F
O
O
F
O
N
Chiral
O
O
Br
N
N
N
H
N
O
O
N
N
O
O
O
H
N
O
Model used to predict ”CNS-likeness”
Oct 06/AMJ
N
O
N
O
F
O
I
O
O
N
O
O
N
I
I
N
O
O
O
O
Chiral
N
H
N
O
N
N
N
N
N
O
H
O
O
O
N
N
N
N
H
H
O
N
N
S
N
O
O
O
O
O
O
O
O
S
N
O
O
O
N
O
O
O
N
Other ways to predict drug properties
Oct 06/AMJ
I have talked about overall profiling of a large
number of compounds…… in terms of CNS-likeness
… now I will turn to talk about prediction
of two very important ADME parameters: solubility and
permeability….
Methods based on
Quantitative Structure Property Relationship
Technique
Oct 06/AMJ
PCA: principal component analysis
PLS: projection of latent structures by means of partial least squares
generalized regression to model the association
between a set of X parameters and a Y parameter
R2 shows how well the model fits the data; Q2 shows how well a model
fits the data if the data is not included in the modelling part (leave
one out).
Aqueous Solubility
Oct 06/AMJ
QSPR model
n=775,R2=0.84, Q2=0.83
8 2D descriptors, Cerius2
Most important descriptors:
logP, hba*hbd, hba, hbd
Drugs: –6 < logS < 0;
If error of 1 log unit is OK 
model predicts 60-80% of the
compounds correctly
Journal of Medicinal Chemistry, 2003, Vol. 46, No. 17
Permeability
Oct 06/AMJ
QSPR
N= 13
R2=0.93 Q2= 0.83
Key descriptors:
PSA> Odbl >N-H >
..NPSA >SA
Polar descriptors important and
…. size matters….
Simple Rule: PSA < 120 Å2
Journal of Medicinal Chemistry, 2003, Vol. 46, No. 4
Virtual Screening Methods
Oct 06/AMJ
Docking
Active
molecule
Active
Inactive
Similarity
O
Fingerprint
NH2
Fit to site
Active
site
Cherry-picked
for biological
screening
QSAR
Statistical (Bayesian)
1000000010…
1010000010…
1000000011…
|
Ar
16
|
Me
Pharmacophore
15
14
40
13
35
30
12
25
11
20
10
15
9
10
8
5
8
9
10
11
12
13
14
15
16
0
MN-1
MN-2
MN-3
MN-4
Search database or virtual library
Predict molecular property or in vitro activity
||
NH2 CO
2D fingerprints to use in virtual screening
Oct 06/AMJ
C-N
N
O
…01000100110001….
N
C-O
N
F
MACCS1 keys – 166 bits
acc
11
6
acc
6
…010000000100010….
hyd MOE2, TGT and GpiDAPH
1
2
MDL Information systems Inc. San Leandro, CA
Chemical Computing Group
~10.000 bits
Similarity
Oct 06/AMJ
Similarity measure – the Tanimoto coefficient1:
TC= Bc / (B1 + B2 – Bc)
1
Willet et al. J. Chem. Inf. Comput. Sci. 1998, 38, 983-996
Similarity search, Enrichment
Oct 06/AMJ
208 actives in pool of 10.000 structures
O
H
S
S
O
O
O
S
Chiral
O
N
O
N
O O
O
O
O
O
O
O
90
80
70
60
50
40
30
20
10
ETA
60
MACCS
GPI
Random
% Active retrieved
% Active retrieved
ACE
50
40
MACCS
30
GPI
20
Random
10
0
0
0.5
1
2
4
6
8
10 12 14 16 18 20
% top ranking
Test set from C. Federico, Pfizer
0.5 1
2
4
6
8
10 12 14 16 18 20
% top ranking
PLS-DA on keys GPCR target
Oct 06/AMJ
….. If knowledge of more than one active compound….
Scatter Plot
188 active
compounds
R2Y=0.61
Q2Y=0.57
1.2
26 false
positives
1
657 Inactive
compounds
0.8
0.6
Could also be done
by use of SVM or
Bayesian statistics
0.4
17 false
negatives
0.2
0
-0.2
0
100
200
300
400
Obs num
500
600
700
800
3D Pharmacophore Fingerprints of ligands
Oct 06/AMJ
 Break molecule down into
its pharmacophoric elements
 Generate conformational
models
Acceptor
Hydrophobic
Acceptor
Donor
Aromatic
Acid
Base
011010100010100000
 Combine to create binary
pharmacophore fingerprint or
histogram incorporating
frequency of occurrence
 For each conformer, determine
all 2/3/4 point distance
combinations of pharmacophoric
groups
100
90
90
80
80
70
60
50
40
30
20
70
Oct 06/AMJ
60
Percent active retrieved
100
70
60
50
40
30
20
50
40
30
20
10
10
10
0
0
0
20
18
16
14
12
10
8
6
4
2
1
0.5
0.5
1
2
4
Top ranking percent
6
8
10
12
14
16
18
0.5
20
50
30
20
10
Percent active retrieved
50
Percent active retrieved
50
40
40
30
20
10
0
0
4
6
8
10
12
Top ranking percent
14
16
18
20
6
8
10
12
14
16
18
20
14
16
18
20
TXA2
60
2
4
PAF
60
1
2
Top ranking percent
60
0.5
1
Top ranking percent
5HT3
Percent active retrieved
ETA
HMG
Percent active retrieved
Percent active retrieved
ACE
40
30
20
10
0
0.5
1
2
4
6
8
10
12
Top ranking percent
14
16
18
20
0.5
1
2
4
6
8
10
12
Top ranking percent
Figure 2: Enrichment curves showing how many of the actives are
retrieved in the top n percent of the ranked data set.
2D
3DNlimit
3DCluster
3D
Random
Pharmacophore modelling
Oct 06/AMJ
Observations that modification of some parts of a ligand
results in minor changes of activity, whereas modifications of
other parts of the ligand result in large change of activity.
Pharmacophore element: Atom or functional group essential for
biological activity
3D Pharmacophore mode: Collection of pharmacophore elements
including their relative position in space
Can be used for virtual screening…..
Selective Serotonin Reuptake Inhibitors
(SSRIs)
From TCAs to SSRIs and Beyond
Oct 06/AMJ
CH 3
N
NHCH 3
CN
CH 3
O
N
N
CH 3
NHCH 3
CH 3
O
F 3C
F
Br
Cl
zimelidine
citalopram
cipramil/celexa
fluoxetine
prozac/fontex
28.04.1971
14.1.1976
10.1.1974
First synt. Aug 1972
First synt. May 1972
Cl
sertraline
zoloft
1.11.1979
F 3C
F
NH
NH
N
O
NH 2
O
N
H
indalpine
12.12.1975
paroxetine
paxil/seroxat
30.1.1973
O
O
O
fluvoxamine
fevarin
20.3.1975
The mechanism of SSRI’s
Oct 06/AMJ
Pharmacophore modelling example
Oct 06/AMJ
Fluoxetine
Paroxetine
Citalopram
Sertraline
Chapter 13. Pharmacophore Modeling by Automated Methods: Possibilities and Limitations M.Langgård,
B.Bjørnholm, K.Gundertofte In "Pharmacophore Perception, Development, and use in Drug Design". Edited by
Osman F. Güne International University Line (2000)
3D docking
Oct 06/AMJ
Docking
Active
molecule
Similarity
Active
Inactive
Fit to site
Active site
Cherry-picked
for biological
screening
Structure-based (Docking and Scoring)
Oct 06/AMJ
Need to be able to predict the binding energy
of a ligand to a protein binding site
 many issues make this computationally difficult, as a
free energy of binding is required, need to consider
solvation/water molecules, flexibility of the site and
ligand, polarisation etc
Shape is often used as an initial goodness of fit
measure, then optionally with electrostatics /
pharmacophoric match
 this can be reversed, using electrostatics /
pharmacophores as the primary molecular recognition
Docking experiments
Oct 06/AMJ
RMSD to the X-ray 3D structure lower than 2.00 Å,
100 ligand-protein complexes from Didier Rognan dataset
Oct 06/AMJ
Homology modelling…….. if you do not have
an X-ray of your target….
Model of the serotonian transporter – the biological
target of the SSRI’s….
Jørgensen et al., ChemMedChem, in press
Human Solute Carriers (SLC) Glt(ph))
298 Genes – 43 gene families
1XFH (Pyrococcus horikosh
Oct 06/AMJ
LeuT
2A65 (Aquifex aoelicus)
LacY
GlpT
1PV7, 1PW4 (E.coli))
SERT, NAT, DAT..)
Hediger, M.A. et al, Pflugers Arch – Eur. J. Physiol (2004), 447:465-468
2C3E (Bovine)
Aquifex aeolicus Leucine Transporter
(1.65Å)
Oct 06/AMJ
Bacterial homologue of neurotransmitter:sodium symporters
=>Template for SERT
Yamashita et al. Nature 2005,437, 215-223
Sequence alignment of transporters
Oct 06/AMJ
23% sequence identity between LeuT and hSERT. Key residues conserved
TM1
TM2
LeuT
hSERT
gSERT
hDAT
hNET
1
75
115
56
52
MEVKREHWATRLGLILAMAGNAVGLGNFLRFPVQAAENGGGAFMIPYIIAFLLVGIPLMWIEWAMGRYGGAQGHGTTPAIFYLLWRNRFA
HQGERETWGKKVDFLLSVIGYAVDLGNVWRFPYICYQNGGGAFLLPYTIMAIFGGIPLFYMELALGQYHRNGCISIWRKI......CPIF
ELGDRETWSKKIDFLLSVIGYAVDLGNVWRFPYICYQNGGGAFLIPYTIMAIFGGIPLFYMELALGQYHRNGCISIWRKI......CPIF
EAQDRETWGKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVW.KI......CPIL
DAQPRETWGKKIDFLLSVVGFAVDLANVWRFPYLCYKNGGGAFLIPYTLFLIIAGMPLFYMELALGQYNREGAATVW.KI......CPFF
LeuT
hSERT
gSERT
hDAT
hNET
91
159
199
139
135
KILGVFGLWIPLVVAIYYVYIESWTLGFAIKFLVGLVPEPPPNATDPDSILRPFKEFLYSYIGVPKGDEPILKPSLFAYIVFLITMFINV
KGIGYAICIIAFYIASYYNTIMAWALYYLISSFTDQLPWTSCKNS.20.STSPAEEFYTRHVLQIHRSKGLQDLG.GISWQLALCIMLIF
( )
( )
KGIGFAICIIDLYVASYYNTIMAWVFYYLVSSFTTELPWTSCNNA.20.SISPAEEFYTRQVLQVHRSNGLDDLG.GISWQLTLCLLLIF
( )
KGVGFTVILISLYVGFFYNVIIAWALHYLFSSFTTELPWIHCNNS.25.GTTPAAEYFERGVLHLHQSHGIDDLG.PPRWQLTACLVLVI
( )
KGVGYAVILIALYVGFYYNVIIAWSLYYLFSSFTLNLPWTDCGHT.26.KFTPAAEFYERGVLHLHESSGIHDIG.LPQWQLLLCLMVVV
LeuT
hSERT
gSERT
hDAT
hNET
181
264
304
249
246
SILIRGISKGI.ERFAKIAMPTLFILAVFLVIRVFLLETPNGTAADGLNFLWTPDFEKLKDPGVWIAAVGQIFFTLSLGFGAIITYASYV
TVIYFSIWKGV.KTSGKVVWVTATFPYIILSVLLVRGATLPG.AWRGVLFYLKPNWQKLLETGVWIDAAAQIFFSLGPGFGVLLAFASYN
IIVYFSIWKGV.KTSGKVVWVTATFPYVILFILLVRGATLPG.AWRGVLYYLKPEWQKLLATEVWVDAAAQIFFSLGPGFGVLLAYASYN
VLLYFSLWKGV.KTSGKVVWITATMPYVVLTALLLRGVTLPG.AIDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYN
IVLYFSLWKGV.KTSGKVVWITATLPYFVLFVLLVHGVTLPG.ASNGINAYLHIDFYRLKEATVWIDAATQIFFSLGAGFGVLIAFASYN
LeuT
hSERT
gSERT
hDAT
hNET
270
352
392
337
334
RKDQDIVLSGLTAATLNEKAEVILGGSISIPAAVAFFGVANAVAIAKAG.AFNLGFITLPAIFSQTAGGTFLGFLWFFLLFFAGLTSSIA
KFNNNCYQDALVTSVVNCMTSFVSGFVIFTVLGYMAEMRNEDVSEVAKDAGPSLLFITYAEAIANMPASTFFAIIFFLMLITLGLDSTFA
KFHNNCYQDALVTSTVNCLTSFVSGFVIFTVLGYMAEMRNEDVSEVAKDMGPSLLFITYAEAIANMPASTFFAIIFFLMLLTLGLDSTFA
KFTNNCYRDAIVTTSINSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKD.GPGLIFIIYPEAIATLPLSSAWAVVFFIMLLTLGIDSAMG
KFDNNCYRDALLTSSINCITSFVSGFAIFSILGYMAHEHKVNIEDVATE.GAGLVFILYPEAISTLSGSTFWAVVFFVMLLALGLDSSMG
LeuT
hSERT
gSERT
hDAT
hNET
359
442
482
246
423
IMQPMIAFLEDEL...KLSRKHAVLWTAAIVFFSAHLVMFLN...KSLDEMDFWAGTIGVVFFGLTELIIFFWIFGADKAWEEINRGGII
GLEGVITAVLDEFPHVWAKRRERFVLAVVITCFFGSLVTLTFGGAYVVKLLEEYATGPAVLTVALIEAVAVSWFYGITQFCRDVKEMLGF
GLEGVITGVLDEFPHVWSKRREFFVLGLIIICFLGSLATLTFGGAYVVKLFEEYATGPAVLTVVFLEAVAVAWFYGITQFCNDVKEMLGF
GMESVITGLIDEF.QLLHRHRELFTLFIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVGQFSDDIQQMTGQ
GMEAVITGLADDF.QVLKRHRKLFTFGVTFSTFLLALFCITKGGIYVLTLLDTFAAGTSILFAVLMEAIGVSWFYGVDRFSNDIQQMMGF
LeuT
hSERT
gSERT
hDAT
hNET
443
532
572
515
512
KVPRIYYYVMRYITPAFLAVLLVVWAREYIPKIMEETH...WTVWITRFYIIGLFLFLTFLVFLAERRRNHESA.............
SPGWFWRICWVAISPLFLLFIICSFLMSPPQLRLFQYNYPYWSIILGYCIGTSSFICIPTYIAYRLIITPGTFKERIIKSITPETPT (12)
APGWYWRVCWVAISPIFLLFVTCSFLSNPPELRLFDYNYPYWTTVVGYCIGTSSIICIPIYMAYRLIITPGTLKERILKSITPETAT (12)
RPSLYWRLCWKLVSPCFLLFVVVVSIVTFRPPHYGAYIFPDWANALGWVIATSSMAMVPIYAAYKFCSLPGSFREKLAYAIAPEKDR (19)
RPGLYWRLCWKFVSPAFLLFVVVVSIINFKPLTYDDYIFPPWANWVGWGIALSSMVLVPIYVIYKFLSTQGSLWERLAYGITPENEH (19)
TM3
TM4
TM5
TM6
TM8
TM7
TM9
TM11
TM10
TM12
Procedure
Oct 06/AMJ
20 initial
models
Homology
modeling
(MODELLER)
LeuT-SERT
Outliers
removed
Energy minimization
Flexible region
Backbone+sidechains:
TM1+6 coil
TM3+8 bend
Sidechains:
atoms within
8.5Å of escit
Fixed region
everything else
Na1, Na2, Cl
100 refined
models
Homology
modeling (MOE)
SERT (XA)EscitalopramSERT
Escitalopram
XA(G) of nonconserved and/or
problematic AA
SERT escitalopram model
SERT 5-HT model
Molecular
dynamics
simulations
Serotonin Transporter Model
Oct 06/AMJ
Intracellular
TM6
TM1
TM3
Membrane
TM8
Extracellular
Homology Model: 5-HT Binding Site
Oct 06/AMJ
F341
A173
I172
F335
G442
Y176
Y95
D98
3D picture_2
Pratuangdejkul et al.
Curr. Med. Chem.
2005, 12, 2389
Refinement by MD simulations
Oct 06/AMJ
Resume
Oct 06/AMJ
Computational methods for
o
o
o
o
Compound library property profiling, Chem GPS
Solubility and permeability QSPR predictions
Different virtual screening experiments
Homology model of SERT
Drug Discovery: like finding a needle in a
haystack….
Oct 06/AMJ
Computational chemistry efforts ~ Trying to reduce the size of
the hay stack….
Serendipity
Oct 06/AMJ
“To look for the needle in the
haystack and coming out with the
farmer’s daughter”
Arvid Carlsson