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Lecture 4
De Novo Design
Invited Guest Professorship
Université Louis Pasteur, StrasbourgUniversité Louis Pasteur, Strasbourg
Prof. Dr. Gisbert Schneider
Goethe-University, Frankfurt
9 Decembre 2008, (c) G. Schneider1
De novo design concepts
Requirements
• Structure sampling method
• Structure assessment method
• Grow• Link• Lattice• Stochastic
• Primary constraints
Implementations
• Structure assessment method
• Search method & Stop criterion
• Primary constraints(receptor, ligand)
• Secondary constraints
• Depth-first search (DFS)• Breadth-first search (BFS)• Random search• Evolutionary Algorithm• Monte Carlo / Metropolis• Exhaustive enumeration
2
Name Publication Building BlocksFragments Receptor Ligand DFSA BFSBRandom MCC EAD
Grow Link Lattice MDEStochastic
HSITE/ 2D Skeletons10,29,85 1989 X X X3D Skeletons30 1990 X X X XDiamond Lattice31 1990 X X X XBUILDER v126 1992 X X X X XLEGEND18 1991 X X X XLUDI11,12,86-88 1992 X X X X XNEWLEAD28 1993 X X X X XSPLICE58 1993 X X X XGenStar32 1993 X X X XGroupBuild16 1993 X X X XCONCEPTS37 1993 X X X XSPROUT15,55-57 1993 X X X X X XMCSS & HOOK23,25 1994 X X X XGrowMol19 1994 X X X X XMCDNLG59 1995 X X X XChemical Genesis20 1995 X X X X XDLD24,89 1995 X X X X
Fitting and clipping of planar skeletons
Building Blocks Primary target constraints Combinatorial Search Strategy Structure Sampling
PRO_LIGAND13,42,90-93 1995 X X X X X XSMoG39,40,94 1996 X X XBUILDER v227 1995 X X X XCONCERTS33 1996 X X X XRASSE21 1996 X X X XPRO_SELECT14,38 1997 X X X XSkelGen61,62 1997 X X X X XNachbar43,95 1998 X X X XGlobus47 1999 X X X XDycoBlock34,35 1999 X X X XLEA45 2000 X X X XLigBuilder22 2000 X X X X XTOPAS46 2000 X X X XF-DycoBlock36 2001 X X X XADAPT65 2001 X X X XPellegrini & Field44 2003 X X X X XSYNOPSIS53 2003 X X X XCoG48 2004 X X X X XBREED60 2004 X X X Exhaustive recombination3
Fragment Placing and Linking
Place fragments Find a suitable linker Link fragments
III
4
O
O
N
H
O
OH
O
OH
Olink
Ile56
N
H
placefragments
Link/Grow Strategy
Babine et al. (1995)Bioorg. Med. Chem. Lett. 5:1719
Ki = 16 µM
O O
OH
OO
grow
Asp37
O
O
Definebinding pocket
(FKBP-12)
Determineinteraction sites
place firstfragment
Phe46
O
O
N
H
5
N
H
N
H
O
OH
Lattice Strategy
O
O
O
O
Fill pocketwith lattice points
Find and connectinteraction points
OH
Assign molecularframework
Buildmolecule
6
Assemble, Dock & Score:Beware of “Unwanted Fragments”
Idea:• generate ligands outside the pocket• dock complete molecules• score
NH NH2
NH
O NH
Asp189
K. Bleicher, M. Stahl, G. Schneider(unpublished)
Insoluble!
S1 pocket
Lipophilicpocket
Thrombin
7
Too fine-grained (atomistic) optimization
“Wrong“ placement of start fragment(s)
Issues of Conventional Design Approaches
“Wrong“ placement of start fragment(s)
Unsuitable linker fragments
8
1. Generate a molecular skeleton based on molecular graph2. Assign real 3D substructure elements (e.g. SPROUT)
1. Link 2D molecular building blocks (SMILES, mol)
Molecule Assembly Strategies
A
B1. Link 2D molecular building blocks (SMILES, mol)2. Calculate 3D conformation (e.g. TOPAS)
• Directly link 3D molecular fragments (e.g. LUDI)
B
C
9
x
HNNH
BindingPocketInitial State
Level 1
Tree model of search space exploration by an automated structure generation method
• Grow strategy
• Depth-first search
xx
NH
O
NH
O
End State
Level 2
Designed Molecule
...
NHHO
O
NH
NN
• Structure-based
10
O
O
ON
N
OHN
N+
OH
O
O
OHO
O
OHN
NH
O
N
HO
O
OH
0.47
0.57
O
NH
NO
N
N
NN
N
N
HN
NH
O
NN0.66
Initial state
inde
x (s
imila
rity
to th
e te
mpl
ate
stru
ctur
e)
Search space exploration by an evolutionary algorithm
• Mutation / Selection
• Depth-first search
• Ligand-based (Reference: Gleevec®) Br NH
HNO
OOH
HN
NN
FN 0.81
NN
O
NH
NH
N
N
FN
0.92
NN
O
NH
NH
N
N
N
1.0
0.70
Br
NH
O
N
NHN
NN
N
N
HN
End state
Tan
imot
oin
dex
(sim
ilarit
y to
the
tem
plat
e st
ruct
ure)
(Reference: Gleevec®)
11
Generation of favourable ligand-binding positions
• CAVEAT (Lauri & Bartlett, 1984)
• GRID (Goodford, 1985 )
• LUDI (Böhm, 1992)
• MCSS (Miranker & Karplus, 1991) /CHARMM (Brooks, 1983)
12
CAVEAT (Lauri & Bartlett, JCAMD 1984, 8:51)
a) b) c)
HNO
NH
O NHO OH
3.17
IC50 = 2.4 µM
Zn2+
MMP-2
S1'
HNO
NH
O
Pro238
Leu181
de novodesign
Zn2+
S1' Gly219
13
LUDI (Böhm)
• Find interaction “hot-spots”HD: blueHA: redLipo: green
• Place fragments
Ligand H-bond donor
N
Receptor
H-bond acceptor
H
• Place fragments
• Link fragments
Böhm et al. (1996)
14
De novo Design with LUDI: Trypsin Inhibitors
Böhm et al. (1996)
15
OHO
NR
O
HO
HN
NH
O
OO
OH
HN
NH
O
OO
OH
NAD+ NADH + H+
DHODH
Dihydroorotate Orotate
De novo Design with SPROUT: DHODH Inhibitors
N
O
3.18
O
NR
X
Inhibitor template Selective inhibitor
3.19
Ala59
Structure-based design with SPROUT (Johnson et al.)
Dihydroorotate dehydrogenase (DHODH)of Plasmodium falciparum
16
De novo Design with BOMB: HIV-1 RT Inhibitors
OX
RPhenyl
N
NPyrazinyl
R
∆∆G in kcal/mol
0.0
-4.2 ± 0.4O
ClN
• Free Energy Perturbation (FEP) approach (Jorgensen et al. 2006 )
• Target: HIV-1 reverse transcriptase
X
NH
Het
Core structure
N
N
2-Pyrimidinyl
R
Pyrazinyl
S
N2-Thiazolyl
R
-11.4 ± 0.5
-9.6 ± 0.4
Cl
NH
N
NO
3.20
EC50 = 10 nM
à place core structure in receptor structureà optimize side-chains by FEP O
O
HO OHO O
O
O
HO O
Initial state
λ = 0
Intermediate state 1 Final state
λ = 1
Intermediate state 2
Step 1 Step 2 Step 3
17
CHARMM
18
19
HIV-Protease
• benzene minima
DHFR site points
• acceptors• donors• ring centroids• neutrals
20
Mol 1Mol 2
3D QSAR: Molecular Field Analysis
Probe 1 Probe 2 Probe 3
Mol 3
Mol N
21
HO O
NOH1
HOH
De novo design examples
• New antifungal agent• Candida / Mycobacterium lanosterol 14α-demethylase (CYP51)• MCSS fragement identification• LUDI fragment linking• no heme coordination (no CYP-P450 interaction)Ji et al. (2003) JMC 46:474
• New HIV-1 protease inhibitor (Ki = 42 nM)• BREED „preferred fragment“ approach
O
OHN
O
OHN
SO
O
NH
2
NH
ON
SH2N
3
• BREED „preferred fragment“ approach• First step: 4 reference molecules recombined• Second step: hybrid fusing with 100 reference structures
• New HIV-1 reverse transcriptase inhibitor (IC50 = 4.4 µM)• SYNOPSIS structure-based approach• First step: 28 designs with predicted low IC50
• Second step: expert inspection & selection, 18 synthesized• 10/18 with IC50 < 100 µM
Pierce et al. (2004) JMC 47:2768
Vinkers et al. (2003) JMC 46:2765
22
NHN
OHN
O 4
Recent combinatorial de novo design examples
• New Cdk-4 inhibitor (IC50 < 1 µM)• LEGEND with homology model• First step: candidate designs• Second step: combinatorial optimization of preferred scaffolds
(MW < 350 Da)• Third step: LUDI & LeapFrog for selectivity optimization
(side-chain optimization)
Honma et al. (2001) JMC 44:4628
CF3
F3CO
5
• New CB-1 ligand (IC50 = 0.3 µM)• TOPAS ligand-based approach• First step: designs assembled from GPCR-fragments• Second step: expert inspection & scaffold selection• 6-10% hit rate with IC50 < 10 µM
(side-chain optimization)
Rogers-Evans et al. (2004) QCS 23:426
23
Ant Colony OptimizationA Simple Combinatorial Case: Peptide Design
T-cell receptor
• Design of novel antigens presentedby MHC I (H-2Kb)
• Length: 8 residues (208 possibilities)
à Neural network ensemble + jury
Artificial ant system
(PDB: 1HSA, Madden et al. 1993)
à Artificial ant system
Assay confirms the predictions:
89% correct for binding peptides
95% correct for non-binding peptides
Hiss et al. (2007) PEDS 20, 99.Schneider et al. (1998) PNAS 95:12179.
24
Ant System for Combinatorial Design
25
De novo molecular design
DB
Known drugs and leadsKnown drugs and leads
Set of reactionsSet of reactions
Generate µ initialparent molecules
Generate λ new moleculesfrom µ parent(s)
Start
R10
OHO
DB
Stock of building blockswith reaction labels
from µ parent(s)
Determine fitness
Select µ best molecules
End
No
Yes
Terminate?
R10
NR7
OR4
R3OHO
R1
• Template compound(s)• Prediction tools• Biochemical assay
• Template compound(s)• Prediction tools• Biochemical assay
Gen
era
tio
n l
oo
p
26
([c;R1:1][10*]).([10*][c;R1:2])>>[c;R1:1]-[c;R1:2]([c;R1:1][10*]).([10*][c;R1:2])>>[c;R1:1]-[c;R1:2]
Aromatic-C + Aromatic-C
SMIRKS/ ReactionSMILES for Virtual Synthesis
AromaticReaction type & site index
+[10*] [10*]
Aromaticcarbon Member of
exactly one ring
Atom mapping index
Reaction type & site index
27
HN
F
F
O
HN
F
HN
O
HN
Rx1
O
F
F HN
F
Rx1NH ORx1
Rx1
N
O
Rx1O
NN Rx1
O
N
N
HNHN
O
F
F
Breeding new molecules
Virtual reactionVirtual reaction
MutationMutation
HN
F
F
O
HN
F
HN
O
N
NHN
CO
NHN
CO
HN
Rx1
O
F
F
HN
F
Rx1NH ORx1
Rx1
CO
Rx1NN
N
NHRx1
NH
Rx1 CO
Rx1
CO
N HN OHN
F
HN
O
F
F
HNCO N
NHN
CrossoverCrossover
28
A Universal Virtual Molecule Generator
HN
F
F
O
HN
F
HN
O
O
Parent Structure
NHN
F
F
O
HN
O
Step 1 Step 3
Child Structure
O
HN
F
F
O
OH
H2NO OH
H2N
F
NRandomly selecta reaction andretro-synthesize
Fragmented Parent Structure
Select one fragment andsubstitute by a new fragmentof the same type
HN
F
F
O
OH
H2NO OH
Fragmented Child Structure
Synthesize withreaction chosenin Step 1
Step 1
Step 2
Step 3
N
29
Compound Growth
B) Synthesis - Growth
HNON
ORx1
HN
F
F
O
HN
F
HN
O
F
Retro-SynthesisHN
F
HN
O
CO
NHRx1
HN
F
Rx1
HNO
Rx1Rx1
HNRx1Rx1HN
F
O
Rx1
HN
O
HN
Rx1
O
F
F
N
ORx1
HN
F
Rx1
NH ORx1
Rx1 NH ORx1
Rx1CO
Rx1NH
Rx1
NH ORx1
CO
NHRx1
30
Compound Shrinking
N
ORx1
HN
F
F
O
HN
F
HN
O
F
HNO
Rx1Rx1
F
A) Retro-Synthesis Synthesis - Shrinkage
N
HN
F
F
O
Rx1
HN
O
HN
Rx1
O
F
F
N
ORx1
HN
F
Rx1
HNO
Rx1Rx1
HN
F
Rx1
NH ORx1
Rx1
NH
NH2
Rx1
HN
HN
O
F
F
NH2
HN
Rx1
O
F
F
31
70
80
90
100
Random Search
How many iterations? How many compounds?
Limited resources: 300 compoundsà There is an optimum!
Trypsininhibition
Population size × Generations
1 x
300
2 x
150
3 x
100
5 x
60
10 x
30
15 x
20
20 x
15
30 x
10
37 x
8
50 x
6
60 x
5
75 x
4
100
x 3
150
x 2
IC50 [µM]
0
10
20
30
40
50
60
70
Evolution Strategy
preferred combinations
inhibition
32
Design Examples – Dopamine D3 Ligand
BP 897
• ‘recapped’ drugs
• Daylight Fingerprints
• Tanimoto Coefficient
NH
ON
NO
D3: 0.92 nMD2: 61 nMPilla et al. (1999) Nature 400:371-373
T = 0.88
ON
N
NH
O
NH
O
ON
N
HN
O
Cl
Cl
T = 0.80
N
O
NNH
T = 0.75
O
NNN
O
O
T = 0.7833
NH
O
N
N
O
Reference: BP 897
4-bond spacer
Design Examples – Dopamine D3 Ligand
3-bond spacer
ON
NHN
O
I
DesignD3: 396 nMD2: 117 nMHackling et al. (2003) J. Med. Chem. 46:3883-3899
ON
NHN
O
34
Reference compound:
Acetylpromazine
Lind et al. (2002)
lipophilic polar
De Novo Design of TAR RNA Ligands (1)
S
NO
N
Lind et al. (2002)
LIQUID pharmacophore model
of Acetylpromazine
Designed molecule in the same
LIQUID pharmacophore model
De novo Design:
F N
N
O F
FF
F
35
NN
N SF
Generation 20, Distance=1.12
O
N
Cl
ClN
N
FF
FO
N N
O
HOGeneration 1, Distance=5.16Start
De Novo Design of TAR RNA Ligands (2)
S
N
NO
Reference molecule
F
NO
FF
F F
N
Generation 60, Distance=0.88
Generation 40, Distance=1.02
F
F
NS
Generation 10, Distance=1.54
FFF
NS
N
O
Cl
Cl
Generation 5, Distance=1.75
O O
F
NO
N
End
Manually refined design
36
U40
U25
C39
G26
F
N NO
De Novo Design of TAR RNA Ligands (3)
Modified bioactive
de novo design
Superposition with
AcetylpromazineC24
FH2NNO
FNH
N
OMeO
OH
OOMe
O
a)
b) c) d)-e)
F
N NO
O
OH
Docking
37
De Novo Design of Bioactive Compounds:
Selected Success Stories
Kv1.5 potassium channel blocker Angew. Chem. Int. Ed. (2000) 39:4130
SO
O
NH
HO
HN
O
O
SO
ONH HN
O
hCB-1 cannabinoid receptor inverse agonists QSAR Comb. Sci. (2004) 23:426
NS
O
O
O
NO
CF3
F3CO
N
O
ON
O
O
F
ClCl
F
J. Med. Chem. (2008) 51:2115
Potent benzodioxole series
38
New Cannabinoid Receptor 1 (CB-1) Ligands
NS
O
O
O N
O
O
O
de novodesign
N
O
O R2
O
R1
Scaffold A
NF4.36, K i = 110 nM
Template structure
NR1
R2
Scaffold B
N
CF3
F3CO
Ki = 300 nM
• Virtual combinatorialchemistry hit
Roger-Evans et al. (2004) QCS 23:426. 39
The Kv1.5 Story
K+
SO
O
NH
HO
HN
O
OO
SO
ONH HN
O
Icagen template, IC50 < 1 µM design, IC50 = 7 µM
R1
de novodesign
SO
ONH HN
O
optimized, IC50 = 1 µM
R1
Pharmacophoremodel
SO
ONH HN
O
lead structure, IC50 < 1 µM
SO
ONH HN
O
Side-chain Linker Scaffold
variable constant
virtualcombichem
The target
Schneider et al. (2000)Angew. Chem. Int. Ed. 39:4130.
40
Design of a druglike hKv1.5 channel blocker
WDIRECAP Building
BlocksTOPAS
3bcombinatorialoptimization
24,563~ 46,000Reference molecule
aromaticnucleophilicsubstitution
aromaticnucleophilicsubstitution
1-fluoro-2-nitrobenzene o-anisidine reduction(Pd-catalytic hydrogenation)
reduction(Pd-catalytic hydrogenation)
condensationcondensation
41
W215
N98
G216
D189
W60DY60A
G219
A190
H57
L99S195
NH2NH
NH
O
6
1. Placement of fragments2. Combinatorial optimization• Preferred reaction (red. amination)• „needle“ approach
1DWB, 3.16 Å
Thrombin Inhibitors:Automatic vs. Manual Design
Ki = 10 nMBöhm et al. (1999) JCAMD 13:51
W215
N98
G216
D189
W60DY60A
G219
A190
3.1
W215
N98
G216
D189
W60DY60A
G219
A190
3.1
NN
NH2HN
O
OF
7
1. Placement of central scaffold2. Modelling3. Fluorine scan
1OYT, 1.67Å
1DWB, 3.16 Å
Ki = 6 nM
Böhm et al. (1999) JCAMD 13:51
Obst et al. (1997) Chem. Biol. 4:287Olsen et al. (2003) Angew. Chem. Int. Ed. 42:2507
42
COLIBREE®Combinatorial Library Breeding
COLIBREE®Combinatorial Library BreedingCombinatorial Library BreedingCombinatorial Library Breeding
43
Disassembly Rules
Ar Ar ArRetain building blocks withMW < 200 Da
à 7,184 Building Blocksavg MW = 141 ± 35 Da
Retain building blocks withMW < 200 Da
à 7,184 Building Blocksavg MW = 141 ± 35 Da
44
Linker Library
45
Molecule Dissection
46
Positive Design
47
Positive and Negative Design
PPARα(negative weight)
PPARγ(positive weight)
48
Thrombin inhibitors PPARα modulators
Land ho! Exploration of chemical space
SO H
N
O
O
NH
O
N
NH2H2N
NAPAP
HOO
OHN
O
S
N
CF3
GW590735
49
Let’s be positive!De novo design of Thrombin inhibitors
CATS descriptor no. 10: PP0 =
Top-ranking designed compounds O O
NH
OHN
SO
O
HN NH2
O
NH
O N
NHH2N
HN
SO
OSCl
O O
NH
OHN
SO
O
S
Cl
HN NH2
O O
NH
OHN
SO
O
HN NH2
O O
NH
OHN
SO
OF S
NHH2N
50
Climbing “Acid hill”:De novo design of PPARαααα agonists
CATS descriptor no. 19:
DN1 =
HO
O
Top-ranking designed compounds
OHO O
HN
O
O
Cl
OHO O
HN O
CF3
O
HOO
HNO
NS
O
HOO
HNO
NS
Cl
CF3
OCF3
OHO O
HN
O
O
OHO O
HN
O
N
OHO O
HN
O
O
I
HO
O
HOO
HNO
NS
CF3
O
HOO
HNO
NS
S
O
HOO
HNO
NS
Cl
Cl
51
Voyages to the unknown: Bioisosters
NO
HO
NH
O
Design 1
inactive!
PPARα
referenceHO
O
O
NH
OS
N
CF3
NO
HO
NH
ODesign 2
EC50 = 0.5 µM
52
Summary Statements
De novo design generatesnew scaffolds & ideas.
Scoring is the most critical part
Human intervention is essential
Accept moderate bioactivity of the designed compounds
53
Selected Reading
Books:
1. G. Schneider, K.-H. Baringhaus (2008) Molecular Design, Wiley-VCH:Weinheim.
2. H.-D. Höltje, W. Sippl, D. Rognan, G. Folkers (2008) Molecular Modeling, Wiley-
VCH:Weinheim.
3. R. O. Duda, P. E. Hart, D. E. Stork (2000) Pattern Recognition, Wiley:New York.
Review articles:
Y. Tanrikulu Y, G. Schneider (2008) Pseudoreceptor models in drug design: bridging ligand- and
receptor-based virtual screening. Nat. Rev. Drug Discov. 7:667-77.
M.J. Stoermer (2006) Current status of virtual screening as analysed by target class. Med.
Chem. 2:89-112.
P. Willett (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today
11:1046-53.
M. Congreve, C.W. Murray, T.L. Blundell (2005) Structural biology and drug discovery. Drug
Discov. Today 10:895-907.
G. Schneider, U. Fechner (2005) Computer-based de novo design of drug-like molecules. Nat.
Rev. Drug Discov 4:649-63.
54