Klaus- Juergen Schleifer Lecture

1

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


BASF s Portfolio

Plastics

Perform anceProducts

AgriculturalSolutions

O il & G as

Chem icals

FunctionalSolutions


Perform anceProducts


Chem icals

Plastics

O il & G as

FunctionalSolutions

BASF s Portfolio

3


Insecticidesagainstharm fulinsectpests

Herbicidesagainstw eeds

Fungicidesagainst harm ful

diseases

CropCrop protectionprotection

Otherse.g. grow th regulators



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


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?


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


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


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




standards)

chem oinform atics toolsforphys-chempropertycalculationselection rulesforscreening librariesabsorption & distribution effects

m olecularm odelling m ethodsforactivitypredictionrationalise SARsynthesis priorisation





standards)

chem oinform atics toolsforphys-chempropertycalculationselection rulesforscreening librariesabsorption & distribution effects

m olecularm odelling m ethods foractivity predictionrationalise SARsynthesis priorisation


7


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


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


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


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


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)






calculateproperty fields

property field: steric dem and

10


......E2

9.8120

...

E1Sn...

5.33

8.52

7.21

EnS2S1pIC50Cpd





correlate propertyfields w ith activity

( ) .........log 212150 +⋅++⋅+⋅+⋅++⋅+⋅+=− nn EzEmEkShSbSayIC








3D-Q SAR m odel

Training set(120 cpds):reproduction of experim ental data r2 = 0.95leave-one-outcross-validation q2 = 0.79


11







3D-Q SAR m odelPrediction of independent test set: r2pred = 0.78(32 com pounds)



PDB code: 1SQB & 1SQP

IC50 Predictions via Docking & Scoring?

12


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


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


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


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


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


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


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?


-

⊕-

⊕-

⊕⊕

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


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


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


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


N

N

S

N

F F

F

O

OF

Cl

binding site entrance

Alignm entHypothesis vs. Docking Pose

18


N

N

S

N

F F

F

O

OF

Cl

Alignm entHypothesis vs. Docking Pose

binding site entrance


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


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


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!!

Documents

Klaus- Juergen Schleifer Lecture