Transcript Kein Folientitel

COSMO-RS:
From quantum chemistry
to Cheminformatics
Andreas Klamt
COSMOlogic GmbH&Co.KG, Leverkusen, Germany
and Inst. of Physical Chemistry, University of Regensburg, Germany
Thermophysical data prediction methods
MD/MC
simple, well
explored solvents
latitudes of
solvation
≠
Quantum Chemistry
with dielectric
solvation models
like PCM
soft
biomatter or COSMO
water
horizon of
COSMO-RS
-OCH3
MD / MC
force-field
simulations
alkanes
gas
phase
solid
-C(=O)H
phase
-Car
horizon of gasphase methods
quantum
chemistry
Group contribution methods
UNIFAC, CLOGP,
LOGKOW, fingerprints,.. etc.
fitted parameters:
CLOGP:~ 1500
UNIFAC: ~5000 +50% gaps
Dielectric Continuum Solvation Models (CSM)
solute molecule embedded in a dielectric continuum,
self-consistent inclusion of solvent polarisation
(screening charges) into MO-calculation (SCRF)
- Born 1920, Kirkwood 1934, Onsager1936
- Rivail, Rinaldi et al.
- Katritzky, Zerner et al.
- Cramer, Truhlar et al. (AMSOL)
- Tomasi et al. (PCM) - Orozco et al.
- Klamt, Schüürmann (COSMO)
e.g. DMol3/COSMO and others
COSMO =
COnductor-like Screening Model,
just a (clever) variant of dielectric CSMs
Density Functional Theory (DFT)
is appropriate level of QC!
COSMO almost as fast as gasphase!
programs: DMol3, Turbomole,
Gaussian98_release2001
- empirical finding: cavity radii should be about 1.2 vdW-radii
up to 25 atom:< 24 h on LINUX PC
- promising results for solvents water, alkanes, and a few other solvents
But CSMs are basically wrong and give a poor,
macroscopic description of the solvent !
How to come to the latitudes of solvation?
state of ideal screening
home of COSMOlogic
COSMO-RS
latitudes of
solvation
water
Quantum Chemistry
with dielectric
solvation models
acetone
like COSMO
or PCM
horizon of
COSMO-RS
solid
-OCH3
MD / MC
simulations
alkanes
-C(=O)H
QM/MM
horizon of gas- Car-Parrinello
bridge of
symmetry
state
-Car
phase methods
gas
phase
native home of
computational chemistry
Group contribution methods
UNIFAC, CLOGP,
LOGKOW, etc.
COSMO-RS:
1) Put molecules into ‚virtual‘ conductor (DFT/COSMO)
2) Compress the ensemble to approximately right density
3) Remove the conductor on molecular contact areas
(stepwise) and ask for the energetic costs of each step.
In this way the
(2) molecular
interactions reduce to pair
(1)
interactions of surfaces!
s >> 0
hydrogen bond
' 0
s <<
electrostat. misfit
+
+
_
_
s_
_ ++
_ + s'
+
ideal contact
(3) specific
interactions
Gmisfit (s , s ' )  aeff
'
2
(s  s ' ) 2
Ghb (s , s ' )  aeff chb (T ) min{0, ss 's hb }
2
COSMO-RS
For an efficient statistical thermodynamics reduce the ensemble of
molecules to an ensemble of pair-wise interacting surface segments !
Water
5

p
water
(s) (amount of surface)
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
s [e/A ]
2
Screening charge distribution on molecular surface
reduces to "s-profile"
0.020
A. Klamt, J. Phys. Chem., 99 (1995) 2224
COSMO-RS
For an efficient statistical thermodynamics reduce the ensemble of
molecules to an ensemble of pair-wise interacting surface segments !
(same approximation as is UNIFAC)
X
p (s )
25
20
Water
Methanol
Acetone
Benzene
15
Chloroform
Hexane
10
5
0
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
s [e/A ]
2
Screening charge distribution on molecular surface
reduces to "s-profile"
0.015
0.020
Why do acetone and chloroform
like each other so much?
0.0
Acetone (calculated)
-0.2
Chloroform (calculated)
Acetone (experiment, Rabinovich
et al.)
ln(g )
-0.4
Chloroform
(experiment,Rabinovich et al.)
-0.6
Aceton (experiment, Apelblat et
al.)
-0.8
Chloroform (experiment, Apelblat
et al.)
-1.0
-1.2
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Mole fraction of acetone (1)
0.7
0.8
0.9
Because their s-profiles are
almost complementary!
1.0
Statistical Thermodynamics
• Replace ensemble of interacting molecules by an ensemble S of interacting
pairs of surface segments
• Ensemble S is fully characterized by its s-profile pS(s)
( pS(s) of mixtures is additive! -> no problem with mixtures! )
• Chemical potential of a surface segment with charge density s is exactly(!) described by:
 Eint (s ,s ')  S (s ') 
S (s )   kT ln  ds ' pS s ' exp

kT


s-potential:
affinity of solvent for
specific polarity s
chemical potential of solute X in S:
 SX   ds p X s  S s    kT ln gASSX ,comb
activity coefficients  arbitrary liquid-liquid equilibria
combinatorial contribution:
solvent size effects
5
s-profiles
and
s-potentials of
representative liquids
X
p (s )
0
W ater
Methanol
Acetone
5
Benzene
Chloroform
0
0.70
Hexane
5
hydrophobicity
0.30
-0.005
0.000
s [e/A2]
0.005
0.010
2
-0.010
0.015
0.020
0.10
Water
X
-0.015
 (s ) [kJ/mol A ]
0
0.020
0.50
-0.10
Methanol
affinity for
HB-donors
-0.30
affinity for
HB-acceptors
Acetone
Benzene
Chloroform
Hexane
-0.50
-0.020
-0.015
-0.010
-0.005
0.000
s [e/A2]
0.005
0.010
0.015
0.02
2
1
0
-1
a) DGhydr (in kcal/mol)
-2
-11
2
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
1
0
-1
b) log Pvapor (in bar)
-2
-4
-3
-2
-1
0
1
2
2
1
Residuals
0
-1
c) log KOctanol/Water
-2
-2
-1
0
1
2
3
4
5
6
2
alkanes
alkenes
alkines
alcohols
ethers
carbonyls
esters
aryls
diverse
amines
amides
N-aryls
nitriles
nitro
chloro
water
Results of parametrization based on DFT
(DMol3: BP91, DNP-basis
1
650 data
17 parameters
rms = 0.41 kcal/mol
0
-1
d) log KHexane/Water
-2
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
2
A. Klamt, V. Jonas, J. Lohrenz, T. Bürger,
J. Phys. Chem. A, 102, 5074 (1998)
1
0
-1
e) log KBenzene/Water
-2
-4
-3
-2
-1
0
1
2
3
4
5
2
1
0
-1
f) log KEther/Water
-2
-3
-2
-1
0
1
2
3
meanwhile:
COSMOtherm5.0 with Turbomole BP91/TZVP
rms = 0.36 kcal/mol
Applications to Phase Diagrams and Azeotropes
1.0
0.9
Binary Mixture of
1-butanol (1) and water
at 60° C
0.8
0.7
y
0.6
Calculated
Experiment
0.5
0.4
0.3
0.2
miscibility gap
0.1
0.0
0.0
1.0
0.1
0.2
0.3
0.4
0.5
x
0.6
0.7
0.8
0.9
1.0
1.0
Binary mixture of
Butanol(1) and Heptane (2)
November
at 50° C 2002:
0.8
0.6
Binary mixture of
ethanol (1) and benzene (2)
at 25° C
0.8
COSMOtherm wins the VLE prediction contest
of Nat. Inst. of Standards (NIST)
and American Inst. of Chem. Engineers (AICHE)
y
Calculated
Experiment
y
0.6
Calculated
Experiment
0.4
0.4
0.2
0.2
0.0
0.0
0.0
0.2
0.4
x
0.6
0.8
1.0
0.0
0.2
0.4
x
0.6
0.8
1.0
Chemical Structure
Phase Diagrams
Flow Chart of
1.0
COSMO-RS
B ina ry M ixt ure o f
B ut a no l a nd Wa t e r
at 60° C
0.8
0.6
y
Calculated
Experiment
0.4
Equilibrium data:
activity coefficients
vapor pressure,
solubility,
partition coefficients
Quantum Chemical
Calculation with COSMO
(full optimization)
0.2
miscibility gap
0.0
0.0
0.2
0.4
x 0.6
0.8
1.0
s-potential of mixture
sigma-potential
0.1
0.05
0
-0.02
-0.01
0
0.01
-0.05
-0.1
s-profiles
of compounds
sigma-profiles
-0.15
-0.2
14
ideally screened molecule
energy + screening charge
distribution on surface
12
10
vanillin
w ater
Fast Statistical
Thermodynamics
acetone
8
6
Database of
COSMO-files
(incl. all common
solvents)
4
other compounds
DFT/COSMO
2
-0.02
0
-0.01
0
0.01
screening charge density [e/A²]
0.02
s-profile
of mixture
COSMOtherm
0.02
How to come to the latitudes of solvation?
state of ideal screening
home of COSMOlogic
COSMO-RS
latitudes of
solvation
water
Quantum Chemistry
with dielectric
solvation models
acetone
like COSMO
or PCM
horizon of
COSMO-RS
solid
-OCH3
MD / MC
simulations
alkanes
-C(=O)H
QM/MM
horizon of gas- Car-Parrinello
bridge of
symmetry
state
-Car
phase methods
gas
phase
native home of
computational chemistry
Group contribution methods
UNIFAC, CLOGP,
LOGKOW, etc.
Extension of COSMOtherm to multi-conformations
Unfortunately, many molecules have more than one relevant conformation
COSMOtherm can treat a compound as a set of several conformers
- each conformer needs a COSMO calculation
- conformational population is treated consistently
according to total free energy of conformers
(by external self-consistency loop)
„Conformational analysis of cyclic acidic -amino acids
in aqueous solution - an evaluation of
different continuum hydration models."
by Peter Aadal Nielsen, Per-Ola Norrby, Jerzy W. Jaroszewski, and Tommy Liljefors
(private comm., Ph.D. thesis)
Method
Solvent
rms
Model
(kJ/mol)
AM1
SM5.4A
4.6
PM3
SM5.4P
13.6
AM1
SM2.1
7.4
HF/6-31+G* C-PCM
3.1
HF/6-31+G* PB-SCRF
4.7
AMBER*
GB/SA
13.2
MMFF
GB/SA
18.5
rms (4 points)
(kJ/mol)
5.6
16.2
9.0
3.8
5.8
16.2
19.9
Max Dev
(kJ/mol)
9.2
20.5
16.7
5.9
8.8
24.3
31.4
BP-DFT/TZVP COSMO-RS 2.2
2.6
4.8
COSMO-RS was evaluated as a blind test !!!
1
Water Solubility log(xH2O)
calculated with COSMOtherm
0
-1
-2
Dataset taken from Jorgensen and Duffy (BOSS)
-3
R2= 0.90
rms=0.66
n = 150
Experiment
-4
-5
-6
-7
DGfus < 0
-8
DGfus > 0
-9
McFarland Test Set
questionable
-10
-11
X
logS
-12
G
X
S
X
fus
X
= (µ X-µ S+ min(0,Gfus))/1.365
= 0.54 µ
X
X
water
- 0.18*N
ringatom
+0.0029*volume
-13
-13
-12
-11
-10
-9
-8
-7
-6
-5
Calculated
Stable model: No changes required for pesticides!
A.Klamt, F. Eckert, M. Hornig, M. Beck, and T. Bürger:
J. Comp. Chem. 23, 275-281 (2002)
-4
-3
-2
-1
0
1
COSMOtherm prediction of drug solubility in diverse solvents
(blind test performed with Merck&Co., Inc., Rahway, NJ, USA)
all predictions are
relative to ethanol
solvents:
triethylamine
heptane
Water
1-Propanol
2-Propanol
DMF
Ethyl Acetate
Methanol
Heptane
Toluene
Chlorobenzene
Acetone
Ethanol
Acetonitrile
Triethylamine
Butanol
Ionic Free Energies of Hydration
by COSMOtherm-Ion-Extension
Free energy of Hydration [kcal/mol] for Ions
-50
-60
Calculated
-70
-80
-90
-100
-110
-120
-120
-110
-100
-90
-80
Experiment
-70
-60
-50
Applications of COSMOtherm to Ionic Liquids
ln(gamma_inf) calc. / exp. (T=314/333K)
in 4-methyl-n-butylpyridinium BF4
Lit: Andreas Heintz, Dmitry V. Kulikov, Sergey P. Verevkin, J. Chem.
Eng. Data 2001, 46, 1526-1529
exp.: J.G. Huddleston,University of Alabama
COSMOtherm
6
5
4
3
2
non-aromatic
compounds
1
aromatic compounds
0
0
2
4
6
exp.
COSMOtherm appears to work well for Ionic Liquids
formicacid
aceticacid
chloroaceticacid
dichloroaceticacid0
trichloroaceticacid
n-pentanoicacid
2,2-dimethylpropanoicacid
benzoicacid
oxalicacid0
maleicacid3
fumaricacid
carbonicacid0
latest results for
bases (pKb):
phenol
similar rms pentachlorophenol
slope betweenethanol
0.59 and 0.71
2,2,2-trichloroethanol
hypochlorousacid
hypobromousacid
hypoiodousacid
nitrousacid
COSMOtherm first principle pKa prediction
( A. Klamt, et. al. J. Phys. Chem. A, Nov. 2003)
18.00
pKa = 0.59 Gdiss /(RTln10) +0.88
16.00
2
N=60 R =0.978, rms=0.49
pKa_exp
14.00
12.00
10.00
all
8.00
6.00
alcohols
4.00
carboxylic acids
2.00
inorganic acids
0.00
0
10
20
Gdiss
subst. phenols
30
40
N-acids (uracils,
imines)
sulfurousacid
phosphoricacid2
boricacid
5-fluorouracil
5-nitrouracil
cis-5-formyluracil
thymine
trans-5-formyluracil
Uracil
and others
s-Moment Approach
 S (s )   c f i (s ) with
i  2
i
S
f i (s )  s
0.70
i
for i  0 and
0.50
s-potential
m
0.30
0.10
if
0
f  2 / 1 (s )  f acc / don (s )  
 s  s hb if
 s  s hb
 s > s hb
Water
Acetone
Hexane
-0.10
-0.30
-0.50
-0.70
-0.020
-0.015
-0.010
-0.005
0.000
Now the chemical potential of a solute X in this matrix S is:
 SX
  p (s )  S (s ) ds   p
X
with M iX   p X (s ) f i (s )ds
X
m

(s ) cSi f i (s )
i 2
ds 
m
 cSi M iX
i 2
 s  moments of solute X
The coefficients can now be derived from experimental (log.) partition data
by linear regression. => s-moments are excellent QSAR-descriptors for
general partition behaviour of molecules.
“The solvent space is approximately 5-dimensional!“
Zissimos, et al.: ‘A comparison between the two general sets of linear free energy descriptors
of Abraham and Klamt‘, J. Chem. Inf. Comput. Sci., 42, 1320-1331 (2002)
 s -moment models for ADME proprties as
logBB, intestinal absorption, logHSA, …
0.005
0.010
0.015
0.020
s-moment logBB regression
logBB = 0.0046 area -0.017 sig2 -0.0029 sig3 +0.19
n = 103, r² = 0.71, rms = 0.40
data from: "Modeling Blood-Brain Barrier Partitioning Using Topological Structure
Descriptors", Rose, Hall, Hall, and Kier, MDL-Whitepaper, 2003
1.5
minimum_COSMO_conf.
1.0
CORINA_optimized
exp.
0.5
0.0
-2.0
-1.5
-1.0
-0.5
0.0
-0.5
-1.0
-1.5
-2.0
calc.
0.5
1.0
1.5
s-moment logKHSA regression
logKHSA = 0.0081 area -0.016 sig2 -0.013 sig3 +0.145 sigHacc+0.88
n = 82, r² = 0.69, rms = 0.33
data from: Kier, Hall, Hall, MDL-Whitepaper, 2002
1.5
1.0
logK(HSA) [exp.]
0.5
0.00811599
0.01641931
7
1
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
logK(HSA) [calc.]
0.5
1.0
1.5
COSMO-RS for Percentage Intestinal Absorption (PIA)
Klamt, Diedenhofen, Connolly*, Jones* (submitted) *) GlaxoSmithKline
log KIA  0.0040M0 - 0.0053M2 - 0.0024M3 - 0.113Macc - 0.117Mdon  1.37
100
training set: n=38,
rms=12.5%
high quality test set: n=107,
rms=12.8%
questionable test set:
n=24, rms=22%
90
80
PIA exp.
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
PIA calculated by COSMO-KIA
80
90
100
Prediction of Soil Sorption
Journal of Environmental Toxicology and Chemistry, in print
7
Training Set rms=0.63
6
Test Set rms=0.72
5
Linear (Training Set
rms=0.63)
exp. logKoc
4
3
2
1
0
-1
-1
0
1
2
3
COSMO-KOC
4
5
6
7
COSMOmic: Simulation of molecules in micelles and membranes
Concept:
-define layers of membrane
(shells of micelle)
-get probability to find a certain atom
of surfactant in each layer (e.g. from MD)
-convert this into a s-profile p(s,r)
for each layer r using the COSMO-file
of the surfactant
-use COSMOtherm to calculate µ(s,r)
considering each layer as a liquid mixture
o
o
-now calculate the chemical potential of a solute X in a certain
postion and orientation by summing the chemical potentials
of its segments in the respective layer.
-sample the chemical potentials all positions and orientations of X
-construct a total partition sum and get the probability to find the
solute in a certain depth and orientation.
-also get the average volume expansion in each layer
- get a kind of micelle or membrane-water partition coefficient
The tool COSMOmic facilitates all the previous steps together with COSMOtherm
Perspective: self-consistent treatment of new surfactants; CMC prediction
COSMOfrag: A fast shortcut of COSMOtherm
suited for HTS-ADME prediction
1) large database of precalculated drug-like compounds (about 45000)
2) for new compound find most similar fragments in database
3) compose COSMO surface from surface fragments (write a meta-file)
4) do usual COSMOtherm: solubility, partition properties
advantages:
-about 1 sec. per compound!
-you can add your typical inhouse structures to database
-simple refinement of calculations
COSMOfrag ports COSMO-RS to Cheminformatics!
COSMOfrag:statistics and examples
log(xH2O) [exp.]
Prediction of
Soil Sorption Coefficients
with COSMOfrag
6
exp. data
5
4
3
2
1
Trainingsset rms=0.72 (0.63)
0
Testset rms=0.81(0.72)
0
1
2
3
COSMOfrag
4
5
Water Solubility
-10with COSMOfrag
-8
-6
0
-4
-2
0
-2
-4
-6
Dataset of Jorgensen
-8
and Duffy
rms: 0.71 (0.66)
-10
6
log(xH2O) [meta]
Ligand – Recptor Binding
Mouth of the Retinal
binding pocket
50
sprofiles of the
binding pocket of
bacteriorhodopsin and
retinal
45
40
35
30
25
20
Retinal
Bacteriorhodopsin
binding pocket
15
10
5
Retinal
0
-0.03
0.03
0.02
0.01 treat enzymes
0
-0.01
-0.02
Meanwhile
we
can approximately
and receptor pockets.
The goal is to describe ligand receptor binding (incl. desolvation) based
COSMO polarization cahrge densities s.
COSMOsim
bio-isoster search based on s-profiles
examples by Dr. M. Thormann, Morphochem AG
If the physiological distribution and the drug-receptor binding are governed
by the COSMO s-profiles, it is reasonable to use these for drug-similarity searching:
- search for molecules with maximum similarity of s-profiles
in order to find molecules with similar interactions, but different chemistry
-search is only based on surface polarity (s) and not on structure
 scaffold hopping
- either search over full COSMO-files of COSMOfrag-DB (48000 compounds)
-screen millions of candidate compounds using the COSMOfrag method
-Refine your search by explicit COSMO calculations on the most similar ~500 compds.
Lit:
M. Thormann, A. Klamt, M. Hornig and M. Almstetter, "COSMOsim:
Bioisosteric Similarity Based on COSMO-RS s-Profiles”, J. Chem. Inf. Model. 46, (2006).
A.Bender, A. Klamt, K. Wichmann, M. Thormann, and R.C. Glen,
„Molecular Similarity Searching Using COSMO Screening Charges (COSMO/3PP)“,
in M.R. Berthold et al. (Eds.): CompLife 2005, LNBI 3695, pp. 175–185, 2005.Springer,
Berlin Heidelberg 2005
Example 1: propionic acid
CCC(=O)O
ZFQCMUCKI
0
1
OC(=O)C=C
ITPZMBCLI
1
0.8169
CCCC(=O)O
IAVMXKDKI
2
0.7996
CC=CC(=O)O
RGQGEAHMI
3
0.791
CC(=C)C(=O)O
WCMTTAFLI
4
0.765
CC=CC(=O)O
VGZSDPDLI
5
0.7584
CC(C)C(=O)O
DGWQYNDKI
6
0.7487
OCC1CO1
SDLNNSMIA
7
0.7269
CC(O)C#N
HTYYARCJZ
8
0.7233
Oc1nnns1
NBAKLRQLI
9
0.7171
CC(O)C(=O)O
WOJBMNDKV
10
0.7109
CC(=O)O
CZWYICCKI
11
0.7052
Clc1nnn[nH]1
JMAKWZALI
12
0.7041
CC(=NO)C
EZHYEWAJI
13
0.6983
OCCC(=O)O
FFBMJKDKI
14
0.6978
CC(=O)C=NO
HOMSZUGLI
15
0.6919
Oc1csnn1
UMBRJEKLI
16
0.6885
OC(=O)C1CCC1
CUOCJIGKI
17
0.6817
OCCS
HLKLSJLHI
18
0.6804
CC1CC1C(=O)O
GXSEIQGKP
19
0.6767
propionic acid similars
12
10
p7
8
p8
6
p9
p12
4
p13
p0
2
p15
0
-0.03 -0.02 -0.02 -0.01 -0.01 0
-2
p7
p12
0.01 0.01 0.02 0.02 0.03
p9
p8
p13
p15
Example 2: Metabotropic Glutamate Receptor Ligands
Synthesis and Pharmacology of Metabotropic Glutamate Receptor Ligands
Grube-Jörgensen et al., ISMC 2004P239
Drugs of the Future 2004 (29) Suppl. A: XVIIIth Symposium on MEDICINAL CHEMISTRY
16
14
a
12
b
10
c
d
8
6
4
2
0
-0.02
A
B
C
D
a
b
c
d
A
1.000
0.711
0.666
0.697
0.396
0.440
0.459
0.488
B
0.711
1.000
0.852
0.835
0.406
0.487
0.459
0.530
C
0.666
0.852
1.000
0.857
0.378
0.461
0.455
0.507
D
0.697
0.835
0.857
1.000
0.357
0.437
0.403
0.492
a
0.396
0.406
0.378
0.357
1.000
0.665
0.679
0.642
b
0.440
0.487
0.461
0.437
0.665
1.000
0.742
0.792
c
0.459
0.459
0.455
0.403
0.679
0.742
1.000
0.700
d
0.488
0.530
0.507
0.492
0.642
0.792
0.700
1.000
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
Tanimotoprime coefficients for COSMOsim matrix
Glu (A), ibotenic acid (B), and thioibotenic acid (C) are
known mGluR agonists.D is novel and does also show
mGluR agonist activity with mGluR subtype specificity
most similar to that of C. The querie of d to our inhouse
database containing > 2.000.000 sigma profiles employing
the Tanimotoprime coefficient retrieves b at rank 3
with a similarity of 0.792.
(M. Thormann, Morphochem, 2004)
COSMO-RS: From Quantum Chemistry to Cheminformatics
• The quantum-chemically derived surface polarization charge densities s provide a novel
and very rich description of molecular interactions in liquids and pseudo-liquids phases,
combing electrostatics, hydrogen bonding and “hydrophobic interactions“ in one picture.
• COSMO-RS provides a novel, extremely fast and efficient way to do thermodynamics
based on s-profiles.
• drug solubility and many important ADME properties can be calculated with COSMO-RS
• Quantum chemical DFT/COSMO calculations are reasonably feasible for a few hundred
or thousand drug-like molecules.
• COSMOfrag derives approximate s-profiles for druglike compouds in a second.
• COSMOsim enables drug-similaity screening based on s-profiles
-----------------------------------Outlook: Ligand recepor binding based on s-profiles
Hope you enjoyed the trip to the latitudes of solvation!
state of ideal screening
For references
see:
www.cosmologic.de
home of COSMOlogic
COSMO-RS
latitudes of
solvation
water
Quantum Chemistry
read my book (Elsevier, 2005)
with or
dielectric
COSMO-RS:
From Quantum Chemistry to
solvation models
acetone
like Thermodynamics
COSMO
Fluid Phase
and Drug Design
or PCM
horizon of
COSMO-RS
solid
-OCH3
MD / MC
simulations
alkanes
-C(=O)H
state
-Car
QM/MM
horizon of gas- Car-Parrinello
bridge of
symmetry
phase methods
We are looking for an excellent
Group contribution
methods
to join our
phase cheminforatics expert
UNIFAC, CLOGP,
team!LOGKOW, etc.
gas
native home of
computational chemistry
Ideas for drug drug-receptor
binding with COSMOtherm
-we need the s-profile
of the receptor once
(QM/MM? not yet solved)
- we simply have the s-profile
of the ligands
(even from COSMOfrag)
Idea 1: generate scoring function
from COSMO-RS surface
interaction model
Idea 2: consider receptor pocket
as a kind of pseudo-liquid
(overestimated receptor
flexibility,
but may be interesting)
Both simply include desolvation
Sigma profiles of Enzymes
calculated with linear-scaling AM1/COSMO
(MOZYME in MOPAC2002)
Some common features:
• Large charge
distribution in the
region around s = 0.
• Carbonyl oxygen
between 0.01 and 0.02.
• Charged side chains in
the outer regions
(s<-0.02 ands>0.02)
1000
900
800
700
Bacteriorhodopsin
600
Barnase
Isomerase
500
BPTI
Crambin
400
Papain
HIV-1 Protease
300
200
100
0
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Bacteriorhodopsin and Retinal
Mouth of the Retinal
binding pocket
50
45
Sigma profiles of the
binding pocket of
bacteriorhodopsin and
retinal
40
35
30
25
20
Retinal
15
Bacteriorhodopsin
binding pocket
10
5
Retinal
0
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
A few shots of the binding pocket
Amino Acids: Sigma profiles on two
computational levels
Alanine, AM1
14
Alanine
Alanine, BP/SVP
Glutamic acid, AM1
12
Glutamic acid, BP/SVP
Histidine, AM1
10
Glutamic
acid
Histidine, BP/SVP
8
6
-COOH
4
N lone pair
2
Histidine
-COOH
0
-0.03
-0.02
-0.01
0
0.01
0.02
0.03