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Com putational Com putational Chem istry for Crop Chem istry for Crop Protection ResearchProtection Research
Klaus-Jürgen Schleifer
Com putational Chem istry & Biology BASF SE, Ludw igshafen
13.09.2008 Istanbul, Som m erSchool, KJS 2
BASF – The Chem ical Com pany
The world’s leading chem ical com pany
O ffers intelligent system solutions and high-value products for alm ost all industries
Sales 2007: €57,951 m illion
Incom e from operations (EBIT) 2007: €7,316 m illion
Em ployeesat year-end 2007: 95,175
BASF at a Glance
2
13.09.2008 Istanbul, Som m erSchool, KJS 3
BASF s Portfolio
Plastics
Perform anceProducts
AgriculturalSolutions
O il & G as
Chem icals
FunctionalSolutions
13.09.2008 Istanbul, Som m erSchool, KJS 4
Perform anceProducts
AgriculturalSolutions
Chem icals
Plastics
O il & G as
FunctionalSolutions
BASF s Portfolio
3
13.09.2008 Istanbul, Som m erSchool, KJS 5
Insecticidesagainstharm fulinsectpests
Herbicidesagainstw eeds
Fungicidesagainst harm ful
diseases
CropCrop protectionprotection
Otherse.g. grow th regulators
AgriculturalSolutions
13.09.2008 Istanbul, Som m erSchool, KJS 6
Negligible residues in food
Ecologically harm less
Excellentefficacy(betterthan currentm arket
standards)
Dem ands of a new Active Ingredient for Crop Protection
Active ingredientresearch forcropprotection is a m ultidim ensional task!
No adverse effects on w ild life
4
13.09.2008 Istanbul, Som m erSchool, KJS 7
HT-screening
com pound library
Active – but w hat is its m ode of action ?
Target activity – and in the greenhouse?
greenhouse-screening
optim isation
new lead-structures & developm ent products
hits
O rganism -based M echanism -based
How do w e find new Active Ingredients?
13.09.2008 Istanbul, Som m erSchool, KJS 8
Developm ent Candidate
1
Dossier
0 10
Lead Product
8
ProductDevelopm ent
Years2 3 4 5 6 7 9
LeadIdentification Registration
Research Developm ent
LeadOptim ization
R&D cost per product around $ 250 m illion (industry average)
R&D Process
5
13.09.2008 Istanbul, Som m erSchool, KJS 9
Developm ent Candidate
1
Dossier
0 10
Lead Product
8
ProductDevelopm ent
Years2 3 4 5 6 7 9
LeadIdentification Registration
LeadOptim ization
R&D cost per product around $ 250 m illion (industry average)
R&D Process
M olecularM odellingSupport
13.09.2008 Istanbul, Som m erSchool, KJS 10
Negligible residues in foodExcellentefficacy
(betterthan currentm arketstandards)
Issues addressed by M olecular M odelling and Chem oinform atics
Ecologically harm lessEcologically harm less No adverse effects on wild lifeNo adverse effects on wild life
6
13.09.2008 Istanbul, Som m erSchool, KJS 11
Issues addressed by M olecular M odelling and Chem oinform atics
Excellentefficacy(betterthan currentm arket
standards)
chem oinform atics toolsforphys-chempropertycalculationselection rulesforscreening librariesabsorption & distribution effects
m olecularm odelling m ethodsforactivitypredictionrationalise SARsynthesis priorisation
Ecologically harm lessEcologically harm less No adverse effects on wild lifeNo adverse effects on wild life
13.09.2008 Istanbul, Som m erSchool, KJS 12
Issues addressed by M olecular M odelling and Chem oinform atics
Excellentefficacy(betterthan currentm arket
standards)
chem oinform atics toolsforphys-chempropertycalculationselection rulesforscreening librariesabsorption & distribution effects
m olecularm odelling m ethods foractivity predictionrationalise SARsynthesis priorisation
Ecologically harm lessEcologically harm less No adverse effects on wild lifeNo adverse effects on wild life
7
13.09.2008 Istanbul, Som m erSchool, KJS 13
M olecularM odelling TechniquesTw o M ain Categories
Ligand-BasedDrug Design
Structure-BasedDrug Design
no protein 3D-structure,no active ligands
no protein 3D-structure,active ligands
protein 3D-structure, active ligands
protein 3D-structure, no active ligands
Perform experim ents De novo design
Docking & scoringFree-energy –calculation
Pharm acophores
3D-QSAR
13.09.2008 Istanbul, Som m erSchool, KJS 14
Ligand-BasedDrug Design
Structure-BasedDrug Design
no protein 3D-structure,no active ligands
no protein 3D-structure,active ligands
protein 3D-structure, active ligands
protein 3D-structure, no active ligands
Perform experim ents De novo design
Docking & scoringFree-energy –calculation
Pharm acophores
3D-QSAR
M olecularM odelling TechniquesTw o M ain Categories
8
13.09.2008 Istanbul, Som m erSchool, KJS 15
find the com m on features that are im portant in binding to the biologically relevant receptor (in the absence of structural inform ation about the receptor)
correlate 3D-structural properties with biologicalactivity
considered properties:
electrostatic
donor/acceptor
steric
hydrophobic
use the resulting m odelto predictthe activityof new analogues
3D-QSAR M odels:CoM SIA-M ethodology*
targetactivity(IC50-values)
*Klebe et al., J. M ed. Chem ., 1994, 37, p. 4130-4146
13.09.2008 Istanbul, Som m erSchool, KJS 16
Exam ple 1: Strobilurines -Fungicides from Fungi
Buchenschleim rübling(Oudem ansiella m ucida)
O
O
O
O
O udem ansin A
Defensive chem icals isolated from fungi
Kiefernzapfenrübling(Strobilurus tenacellus) Strobilurin A
OO
O
M ode of action
Strobilurinesblock the fungalenergyproduction byinhibitionof the com plexIII of therespiratorychain.
I
NADH
NAD+
Succinate
Fum arate
H+ H+
Cytb *
III
Cytc12e-IV
H2O
1/2 O 2
H+
UQpool
CytcATP
Synthase
H+ADP
ATP
III
NADH
NAD+
Succinate
Fum arate
H+ H+
Cytb *
III
Cytc12e-IV
H2O
1/2 O 2
H+
UQpool
CytcATP
Synthase
H+ADP
ATP
III
NADH
NAD+
Succinate
Fum arate
H+ H+
Cytb *
III
Cytc12e-IV
H2O
1/2 O 2
H+
UQpool
CytcATP
Synthase
H+ADP
ATP
II
9
13.09.2008 Istanbul, Som m erSchool, KJS 17
3D-Q SAR M odel forStrobilurines
Input: Chem ically diverse ligandsw ith different activity levels
3D-conform ationgeneration
structuralalignm ent
very high activity (IC50 < 10-9)high activity (IC50 < 10
-8)120 strobilurine analoguesbroad activity range ( 10-10 < IC50 < 10
-5)
13.09.2008 Istanbul, Som m erSchool, KJS 18
3D-Q SAR M odel forStrobilurines
Input: Chem ically diverse ligandsw ith different activity levels
3D-conform ationgeneration
structuralalignm ent
calculateproperty fields
property field: steric dem and
10
13.09.2008 Istanbul, Som m erSchool, KJS 19
......E2
9.8120
...
E1Sn...
5.33
8.52
7.21
EnS2S1pIC50Cpd
Input: Chem ically diverse ligandsw ith different activity levels
3D-conform ationgeneration
structuralalignm ent
calculateproperty fields
correlate propertyfields w ith activity
( ) .........log 212150 +⋅++⋅+⋅+⋅++⋅+⋅+=− nn EzEmEkShSbSayIC
3D-Q SAR M odel forStrobilurines
13.09.2008 Istanbul, Som m erSchool, KJS 20
Input: Chem ically diverse ligandsw ith different activity levels
3D-conform ationgeneration
structuralalignm ent
calculateproperty fields
correlate propertyfields w ith activity
3D-Q SAR m odel
Training set(120 cpds):reproduction of experim ental data r2 = 0.95leave-one-outcross-validation q2 = 0.79
3D-Q SAR M odel forStrobilurines
11
13.09.2008 Istanbul, Som m erSchool, KJS 21
Input: Chem ically diverse ligandsw ith different activity levels
3D-conform ationgeneration
structuralalignm ent
calculateproperty fields
correlate propertyfields w ith activity
3D-Q SAR m odelPrediction of independent test set: r2pred = 0.78(32 com pounds)
3D-Q SAR M odel forStrobilurines
13.09.2008 Istanbul, Som m erSchool, KJS 22
PDB code: 1SQB & 1SQP
IC50 Predictions via Docking & Scoring?
12
13.09.2008 Istanbul, Som m erSchool, KJS 23
IC50 Predictions via Docking & Scoring?
Docking & Scoring 3D-Q SAR m odel
•docking of 32 com pounds into 1SQP
•10 com pounds w ithoutplausible poses
•poorquantitative correlation w ithIC50 values
•32 com pounds predicted w ithstrobilurine CoM SIA m odel
•r2pred = 0.78
13.09.2008 Istanbul, Som m erSchool, KJS 24
Exam ple 2: Protox Inhibitors asHerbicides
NN
N N
CO O HCO O H
HH
H HNN
N N
CO O HCO O H
H
H
Protoporphyrinogen IX Protoporphyrin IX
Protox*
Hem e
Fe²+
Chlorophyll
M g²+
*ProtoporphyrinogenOxidase
bronzing and necrosisof leaftissue
Biologicaleffect
13
13.09.2008 Istanbul, Som m erSchool, KJS 25
N
O
OR Xn
N
H
NH2
O ,S
N
Xn
Xn
Nhet-5
Xn
Nhet-6
Xn
Nhet-5
O ,S
Xn
Nhet-6
Xn
Chet
N,Chethet
Xn
biochem icalassay: pIC50-values from 3 to > 11
Scaffolds of Protox Inhibitors
13.09.2008 Istanbul, Som m erSchool, KJS 26
A/b B/c
a d
NH
NH
NH
NH
N
O
O ClO
O
Cl
A
B
a d
b c
CSD code SOCLUR
Pharm acophore HypothesisSubstrate tem plate based on CSD and QM
14
13.09.2008 Istanbul, Som m erSchool, KJS 27
N
Cl F
Cl
OF
FF
N
O
NH
NH
O BrO
O
N
N NS
Cl
F
OF
F
F
O
Resulting Alignm ent
318 protox-inhibitors
13.09.2008 Istanbul, Som m erSchool, KJS 28
Prediction of an externaldata set(n=20) with pIC50 from 6 to 10
r²pred = 0.95 SDEP = 0.24
# cpds. PC r²
317 6 0.87
317 6 0.96
318 6 0.97
q² LOO (L50% O ) SDEP LOO (L50% O)
0.69 0.78
0.90 0.43
0.95 (0.91) 0.30 (0.41)
Statistics derived from 3D-QSAR M odel
15
13.09.2008 Istanbul, Som m erSchool, KJS 29
N
N
O
OF3C
Cl
N
S
F
NF3C
Cl F
Cl
O
N
O
NH
NH
O BrO
O
NH
NH
NH
NH
R
R
Sound SAR?
13.09.2008 Istanbul, Som m erSchool, KJS 30
-
⊕-
⊕-
⊕⊕
NN
N N
CO O HCO O H
HHH H
NN
N N
CO O HCO O H
H
HH
H
H
H
NN
N N
COO HCO OH
H
H H
H
NN
N N
CO O HCO O H
H
H
H
H
NN
N N
CO O HCO O H
H
H
H
H
HNN
N N
CO O HCO OH
H
H
H
H
H
H
- H - H
- H- H
- H
- H
- H+ H
⊕⊕
Reaction Path of Proto-OxidationCam adro et al., Biochem J., 1991, 277, p. 17
16
13.09.2008 Istanbul, Som m erSchool, KJS 31
N
N
SN
F
FF
O
O
F Cl
Are Protox Inhibitors Transition State Analogues?
QM -optim ization of substrate and interm ediates 1, 2 & 4alignm entwith protox inhibitorinterm ediate 4 fits best
13.09.2008 Istanbul, Som m erSchool, KJS 32
pdb-code: 1SEZ
After the 3D-QSAR-M odel w as com pleted:
The Protox Structure!
INH
O
O
F
Cl
N
NBr
FF
F
17
13.09.2008 Istanbul, Som m erSchool, KJS 33
N
N
S
N
F F
F
O
O
F
Cl
N
N
S
N
F F
F
O
OF
Cl
O
O
F
Cl
N
NBr
FF
F
Docking of tw o Protox Inhibitors
13.09.2008 Istanbul, Som m erSchool, KJS 34
N
N
S
N
F F
F
O
OF
Cl
binding site entrance
Alignm entHypothesis vs. Docking Pose
18
13.09.2008 Istanbul, Som m erSchool, KJS 35
N
N
S
N
F F
F
O
OF
Cl
Alignm entHypothesis vs. Docking Pose
binding site entrance
13.09.2008 Istanbul, Som m erSchool, KJS 36
N
O
OO
O
N
NCl
IC50 = 7.4 10-10
Is M odelling just nice to have ?
Strobilurines: Pyraclostrobin
nontreated
N
O
OO
O
N
NCl
IC50 = 7.4 10-10
19
13.09.2008 Istanbul, Som m erSchool, KJS 37
Integral Part of the Innovation Chain!
Protox-Inhibitors: Saflufenacil
N
N O
O
NH
S
O
N
O O
F Cl
FF
F
IC50 = 4.2 10-9
nontreated KixorTM
IC50 = 4.2 10-9
N
N O
O
NH
S
O
N
O O
F Cl
FF
F
13.09.2008 Istanbul, Som m erSchool, KJS 38
Sum m ary & Conclusion
M olecularm odelling isa well-integrated discipline to support
lead identification and optim isation in agrochem icalresearch.
3D-QSAR m odelling is the m ethod of choice forlead optim isation
in the absence of protein-structures.
Consistentalignm enthypothesisis required forhigh-qualitym odels.
Rigorous validation is essential to assess theirpredictive power.
Uncertainties aboutbioactive conform ation & different ligand binding m odes.
Strobilurine-M odel:
Lead structure originates from a naturalproductfrom fungi.
Robust 3D-QSAR m odelto predictnew active strobilurine analogues.
3D-QSAR m odelperform sbetterin targetactivityprediction than docking scores.
Protox-M odel:
Alignm enthypothesis guided bysubstrate structure.
Resulting alignm entgives reason to transition-state-analogue hypothesis.
X-raystructure offers the possibilityform odelre-interpretation.
20
ThankThank youyou forforyouryourattentionattention!!