pharmacophores in chemoinformatics: 1. pharmacophore patterns & topological fingerprints dragos...
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Pharmacophores in Chemoinformatics:Pharmacophores in Chemoinformatics:
1. Pharmacophore Patterns & Topological 1. Pharmacophore Patterns & Topological FingerprintsFingerprints
Dragos HorvathDragos Horvath
Laboratoire d’InfoChimieLaboratoire d’InfoChimie
UMR 7177 CNRS – Université de StrasbourgUMR 7177 CNRS – Université de Strasbourg
[email protected]@chimie.u-strasbg.fr
The Pharmacophore Way of Life – A Medicinal The Pharmacophore Way of Life – A Medicinal Chemist’s DreamChemist’s Dream
• (Bio)Molecular Recognition is based on ligand-site interactions of extremely complicated nature– Understanding them requires a solid knowledge of statistical
physics and, therefore, of higher maths…
– But medicinal chemists hate maths… so they developed a simplified rule set to rationalize ligand binding.
• Functional groups of similar physicochemical behavior represent pharmacophore types: – Hydrophobic, Aromatic, Hydrogen Bond (HB) donors, Cations,
HB Acceptors, Anions.
– Now, we just need to know how each of the six types interacts with the site… welcome to the “pharmacophore” paradigm, farewell higher maths (for the moment, at least)
The Interaction Saga: (1) van der Waals The Interaction Saga: (1) van der Waals InteractionsInteractions
• Atoms are more or less hard spheres – squeezing them against each other causes a sharp rise in energy:– Erep=Aijd-12
• At distances larger than the sum of their « van der Waals spheres », an attractive term due to dipole-induced dipole interactions (London dispersion term) is predominant…– Eatt= - Bijd-6
The Interaction Saga: (2) Electrostatics & The Interaction Saga: (2) Electrostatics & SolvationSolvation
• Coulomb charge-charge interactions are easy to compute, once the partial charges Qk are assigned on the atoms…
– ECoul=QiQj/4d
• … and the solvent molecules are explicitly modeled – accountig for all the possible solvation shell structures, in order to estimate a solvation free energy.
• Alternatively, a continuum solvent model may be employed.
pi
ti
ui
vi
BEi;i
QiQk
BEk;k
pk
tk
npnt
np
neglected!
Eti
Epi
i
0 = Ep.np pi 1- ext
int
k
0 = Ep.np pk 1- ext
int
D. Horvath et al., J. Chem. Phys. 104, 6679 (1996)
The Interaction Saga: (2bis) The Hydrophobic The Interaction Saga: (2bis) The Hydrophobic EffectEffect
• The mysterious force that separates grease and water is not due to grease-grease van der Waals interactions being stronger than grease-water attraction!
• It is not of electrostatic nature either, because greasy alkyl chains have no charges!
• Actually, it’s not a force at all, but the consequence of the drift towards a more probable state of matter (?!)
• For practical purposes, however, it makes sense to believe that hydrophobes « attract » each other – for making hydrophobic contacts significantly improves binding affinity!
Physical Chemistry For Dummies: The RulesPhysical Chemistry For Dummies: The Rules
• Hydrophobes make favorable contacts with other hydrophobes (we do not want to know why!). Assume strenght proportional to the buried hydrophobic area.
• Hydrophobes in close contact to polar groups cause frustration, for they chase away the water molecules favorably solvating the latter and offer no substitute interactions
• Hydrogen bond donors seek to pair with acceptors, so that they may reestablish the water hydrogen bonds they lost
• Cations seek to pair with anions and avoid hydrophobes.• Shape is of paramount importance: groups of a same kind
may replace each other if they are shaped likely
BioIsoSteres – Equivalent Functional GroupsBioIsoSteres – Equivalent Functional Groups
• Wikipedia: bioisosteres are substituents or groups with similar physical or chemical properties that impart similar biological properties to a chemical compound
Pharmacophore PatternsPharmacophore Patterns
• The pharmacophore pattern of a molecule characterizes the relative arrangement of all its pharmacophore types– What pharmacophore types are represented?– How are they arranged (spatially, topologically) with
respect to each other ?– How can these aspects be captured numerically to yield
molecular descriptors of the pharmacophore pattern?
• Note: Pharmacophore patterns are essentially 3D. Since geometry is determined by connectivity, 2D “pharmacophore patterns” also make sense!
Exploiting Exploiting ppharmacophore harmacophore ppatternsatterns……
• N-dimensional vector D(M)=[D1(M), D2(M), …,DN(M)]; each Di encodes an element of the pharmacophore pattern– Allows meaningful quantitative definitions of molecular
similarity: • Neighborhood Behavior: Similar molecules - characterized by covariant
vectors - are likely to display similar biological properties
• As chemists do not easily perceive the pharmacophore pattern, such covariance may reveal hidden but real molecular relatedness…
– May serve as starting point for searching a binding pharmacophore – the subset of features that really participate in binding to a receptor
• Machine learning to select those elements Di that are systematically present in actives, but not in inactives of a molecular learning set!
Some Some eexamples of "xamples of "hhidden idden ssimilarity"imilarity"
0
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A1h
Alpha1
Alpha2
Beta1h
AT
1hB
ZD
cB
omb
B2h
CC
KA
hD
1hD
2hD
aUpt
ET
Ah
Galan
H1c
ML1
M1h
M3h
NK
1hN
PY
Muh
5HT
1Ah
5HT
1D5H
T2ch
5HT
3h5H
T6h
5HT
Upt
Sigm
a1V
1Ah
K-A
TP
Cl
CatB
Elast
PD
EII
PD
EIV
PK
CE
GF
-TK
PK
55fynH
IVP
NE
UP
Th
IL-8M
AP
kinC
GR
P
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NI N
N
S
Br
H
N
NON
Cl
Cl
I
N
N
N
N
N
O
NCl
O
H
Tricentric Pharmacophore Fingerprints: Tricentric Pharmacophore Fingerprints: monitoring feature amonitoring feature arrangementrrangement
• Topological: the distance between two features equals the (minimal) number of chemical bonds between them
N
N
O
N
Cl
99 4
11
• Spatial: if stable conformers are known, use the distance in Ǻ between two features
Example: Example: Binary Pharmacophore TriBinary Pharmacophore Tripletsplets
33 33
33
33
66
77
44
33 44
44
33 55
Hp3-H
p3-Hp3
Hp3-H
p3-Hp3
Hp3-H
p3-Hp4
Hp3-H
p3-Hp4
Hp3-H
p3-Hp5
Hp3-H
p3-Hp5
…… Ar4-H
p3-Hp4
Ar4-H
p3-Hp4
Ar4-H
p3-Hp5
Ar4-H
p3-Hp5
…… …… …… …… Hp7-A
r4-PC6
Hp7-A
r4-PC6
……Hp3-H
A5-A
r5
Hp3-H
A5-A
r555
55 33
0 0 0 … 0 0 … … 1 … … … 0 … … 0 …
Basis Basis TripletsTriplets::• all possible feature combinationsall possible feature combinations• at a given series of distances…at a given series of distances…
Hp4-H
A5-A
r5
Hp4-H
A5-A
r5
55
55 44
??
Pickett, Mason & McLay, J. Chem. Inf. Comp. Sci. 36:1214-1223 (1996)
………… ……
First key improvement: First key improvement: Fuzzy Fuzzy mapping of mapping of atom triplets onto basis triplets in 2D-FPTatom triplets onto basis triplets in 2D-FPT
33 33
33
44
66
77
44
33 44
55
55 33
0 0 0 … 0 0 … +6 … … +3 … … … … 0 …
55
55 44
Hp3-H
p3-Hp3
Hp3-H
p3-Hp3
Hp3-H
p3-Hp4
Hp3-H
p3-Hp4
Hp3-H
p3-Hp5
Hp3-H
p3-Hp5
…… Ar4-H
p3-Hp4
Ar4-H
p3-Hp4
Ar4-H
p3-Hp5
Ar4-H
p3-Hp5
…… ………… …… Hp7-A
r4-PC6
Hp7-A
r4-PC6
……Hp3-H
A5-A
r5
Hp3-H
A5-A
r5
Hp4-H
A5-A
r5
Hp4-H
A5-A
r5
………… ……
Di(m) = total occupancy of basis triplet i in molecule m.
Combinatorial enumeration of basisCombinatorial enumeration of basis tripletstriplets• Example: there are 36796 basis triplets, verifying triangle
inequalities, when considering 6 pharmacophore types and 11 edge lenghts between Emin=3 to Emax=13 with an increment of Estep=1: (3, 4, 5,…13)– Canonical representation: T1d23-T2d13-T3d12 with T3≥T2≥T1
(alphabetically).
44
66
77
Hp7-Ar4-PC6
Ar4-Hp7-PC6
– Out of two corners of a same type, priority is given to the one opposed to the shorter edge.
44
66
77
Ar4-Hp7-Hp6
Ar5-Hp6-Hp7
TriTripletplet matching pmatching procedurerocedure
• The triplet matching score represents the optimal degree of pharmacophore field overlap:– if corner k of the triplet is of pharmacophore type T, e.g. F(k,T)=1,
then it contributes to the total pharmacophore field of type T, observed at a point P of the plane:
)exp(),()(2
,
3
1Pk
kTTdTkFP
Horvath, D. ComPharm pp. 395-439; in "QSPR /QSAR Studies by Molecular Descriptors", Diudea, M., Editor, Nova Science Publishers, Inc., New York, 2001
Control parameters for tControl parameters for tririplet enumerationplet enumeration & & mmatchingatching in two 2D-FPT versions. in two 2D-FPT versions.
Parameter Description FPT-1 FPT-2
Emin Minimal Edge Length of basis triangles (number of bonds between two pharmacophore types)
2 4
Emax Maximal Triangle Edge Length of basis triangles 12 15
Estep Edge length increment for enumeration of basis triangles 2 2
e Edge length excess parameter: in a molecule, triplets with edge length > Emax+e are ignored
0 2
Maximal edge length discrepancy tolerated when attempting to overlay a molecular triplet atop of a basis triangle.
2 2
Hp = Ar
Gaussian fuzziness parameter for apolar (Hydrophobic and Aromatic) types
0.6 0.9
PC = NC
Gaussian fuzziness parameter for charged (Positive and Negative Charge) types
0.6 0.8
HA = HD
Gaussian fuzziness parameter for polar (Hydrogen bond Donor and Acceptor) types
0.6 0.7
l Aromatic-Hydrophobic interchangeability level 0.6 0.5
Number of basis triplets at given setup 4494 7155
Second key improvement: Second key improvement: Proteolytic Proteolytic equilibrium dependence of 2D-FPTequilibrium dependence of 2D-FPT
Ar5-N
C5-
PC8
Ar5-N
C5-
PC8
Ar8-N
C8-
PC8
Ar8-N
C8-
PC8
?12%
88%
Some ‘activity cliffs’ in Some ‘activity cliffs’ in rule-based descriptor rule-based descriptor spacespace are smoothed out in are smoothed out in 2D-FPT-space2D-FPT-space
•Neutral
•Cation
•Neutral
•Anion
•Neutral
• 90%C
ation
•Neutral
• 50%C
ation
•Neutral
•Anion •Neutral
•Neutral
•Neu
tral
• 40%
Cat
ion
•Neu
tral
• 70%
Cat
ion
Best Matching Candidates
Pharmacophore Pattern-Based Similarity Pharmacophore Pattern-Based Similarity Queries: Lead Hopping!Queries: Lead Hopping!
PharmacophoreHypothesis
AutomatedFingerprintMatching...
ReferenceFingerprint
Nearest Neighbors
Superposition-based Similarity Scoring
Potential Pharmacophore Fingerprint Library
?Docking
Some Some eexamples of "xamples of "hhidden idden ssimilarity"imilarity"
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A1h
Alpha1
Alpha2
Beta1h
AT
1hB
ZD
cB
omb
B2h
CC
KA
hD
1hD
2hD
aUpt
ET
Ah
Galan
H1c
ML1
M1h
M3h
NK
1hN
PY
Muh
5HT
1Ah
5HT
1D5H
T2ch
5HT
3h5H
T6h
5HT
Upt
Sigm
a1V
1Ah
K-A
TP
Cl
CatB
Elast
PD
EII
PD
EIV
PK
CE
GF
-TK
PK
55fynH
IVP
NE
UP
Th
IL-8M
AP
kinC
GR
P
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NI N
N
S
Br
H
N
NON
Cl
Cl
I
N
N
N
N
N
O
NCl
O
H
Successful Virtual Screening SimulationsSuccessful Virtual Screening Simulations
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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)
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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (FPT -2) Confirm ed Inactives (FPT-2)
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% R
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Ret
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ee
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om
pou
nds
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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)
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Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (O PT 3) Confirm ed Inactives (O PT3)Confirm ed Actives (PF) Confirm ed Inactives (PF)Confirm ed Actives (FPT -2) Confirm ed Inactives (FPT-2)
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D2
TK
Successful QSAR model construction with 2D-Successful QSAR model construction with 2D-FPTFPT: predicting c-Met TK activity: predicting c-Met TK activity
4
4.5
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9
4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9
Calculated pIC50
Exp
erim
enta
l pI
C50
.
Learning Set Compounds Validation Set Compounds
25 variables entering nonlinear model153 molecules for training: RMSE=0.4 (log units), R2=0.8240 molecules for validation: RMSE=0.8 (log units), R2=0.538 validation molecules out of 40 mispredicted by more than 1 log
What more could be done?What more could be done?
• 3D FPT version under study
– does it pay off to generate conformers? How many would you need to get better results than with 2D-FPT? What’s the best conformational sampler to use?
• Accessibility-weighted fingerprints?
– class to return (topological and/or 3D) estimate of the solvent-accessible fraction of an atom?
• Tautomer-dependent fingerprints?
– if tautomers and their percentage were enumerated like any other microspecies…
THE END
Pharmacophore HypothesesPharmacophore Hypotheses
(A): From individual Active Leads: 2D/3D• ALL features in the Lead assumed relevant for binding
(B): Consensus hypotheses from set of Leads: 2D/3D• Ignore features that can be deleted without losing activity
(C): Site-Ligand interaction models: 3D*• Select Ligand features shown to interact with the site in the
3D X-ray structure of the site-ligand complex.
(D): Active Site filling models: 3D*• Design a pharmacophoric feature distribution complemen-
tary to the groups available in the active site* In these cases, docking may be performed starting from pharmacophore –based
overlays
ComPharm Overlay…ComPharm Overlay…
- chosen conformer of the reference
- chosen conformer of the candidate
- pair of matching atoms
- 3 Euler angles- mirroring toggle
GA-controlledoverlay optimization
ComPharm Pharmacophoric FieldsComPharm Pharmacophoric Fields
• A descriptor of the nature of the molecule’s pharmacophoric neigh-borhood “seen” by every reference atom, assuming an optimal overlay of the molecule on the reference...
Pharmacophoric FeaturesAlk. Aro. HBA HDB (+) (-)
1 X11 X12 X13 X14 X15 X16
2 X21 X22 X23 X24 X25 X26
3 X31 X32 X33 X34 X35 X36
4 X41 X42 X43 X44 X45 X46R
efer
ence
Ato
ms
5 X51 X52 X53 X54 X55 X56