Pharmacophores in Chemoinformatics: 1. Pharmacophore Patterns & Topological Fingerprints Dragos Horvath Laboratoire dInfoChimie UMR 7177 CNRS Universit de Strasbourg [email protected] The Pharmacophore Way of Life A Medicinal Chemists 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 Interactions 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 & Solvation Coulomb charge-charge interactions are easy to compute, once the partial charges Qk are assigned on the atoms Eti ECoul=QiQj/4d
ti nt np and the solvent molecules are explicitly modeled BEi;i accountig forBE all k;the k possible solvation shell structures, in pi Q tk i order to estimate aQsolvation free energy. k ui Epi neglected! solvent model may be employed. Alternatively, a continuum np pk k = Ep.np pk 1- ext 0 int vi i = Ep.np pi 1- ext 0 int D. Horvath et al., J. Chem. Phys. 104, 6679 (1996)
The Interaction Saga: (2bis) The Hydrophobic Effect 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, its 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 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 Groups Wikipedia: bioisosteres are substituents or groups with similar physical or chemical properties that impart similar biological properties to a chemical compound Pharmacophore 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 pharmacophore patterns 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 D i that are systematically present in actives, but not in inactives of a molecular learning set! Some examples of "hidden similarity" 100 CGRP MAPkin IL-8 NEUPTh HIVP PK55fyn EGF-TK PKC PDEIV PDEII Elast CatB Cl K-ATP V1Ah Sigma1 5HTUpt 5HT6h
60 Br 60 N 0 100 H 90 S N N 70 I 80 N 50 N N 70 O N I 80 N Cl 0 100 H N 60 O N
N 80 40 10 40 30 20 10 50 40 30 20 10 Tricentric Pharmacophore Fingerprints: monitoring feature arrangement Topological: the distance between two features equals the (minimal) number of chemical bonds between them Cl O N 9 N N 11 4
Spatial: if stable conformers are known, use the distance in between two features Example: Binary Pharmacophore Triplets Basis Triplets: all possible feature combinations at a given series of distances 3 3 3 3 4 3 5 4 1 7 6 0
Pickett, Mason & McLay, J. Chem. Inf. Comp. Sci. 36:1214-1223 (1996) 0 0 3 C6 -P r4 7 -A Hp 0 ? 4 r5 -A A5 4 -H Hp 0 5
5 r5 -A A5 3-H Hp 0 3 5 4 p5 -H p3 4-H p4 Ar -H p3 4 -H Ar p5 -H p3 3 -H Hp p4 -H p3 3 -H p3 Hp
-H p3 3 -H Hp 0 5 First key improvement: Fuzzy mapping of atom triplets onto basis triplets in 2D-FPT 3 3 3 3 4 4 +3 Di(m) = total occupancy of basis triplet i in molecule m. C6 -P r4 7 -A Hp
+6 0 7 6 r5 -A A5 4-H Hp 0 4 5 r5 -A A5 3-H Hp 0
p5 -H p3 4-H p4 Ar -H p3 4 -H Ar 0 5 3 5 4 p5 -H p3 3 -H Hp p4 -H p3 3 -H p3 Hp -H p3 3 -H Hp 0 5
0 Combinatorial enumeration of basis triplets 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: (alphabetically). T1d23-T2d13-T3d12 with T3T2T1 Hp7-Ar4-PC6 4 7 Ar4-Hp7-PC6 6 Out of two corners of a same type, priority is given to the one opposed to the shorter edge. Ar4-Hp7-Hp6 4 7 6 Ar5-Hp6-Hp7
Triplet matching procedure 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: 3 2 T (P) F(k,T)exp( T dk,P) k 1 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 triplet enumeration & matching in two 2D-FPT versions. Parameter Description 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 Aromatic-Hydrophobic interchangeability level 0.6 0.5 Number of basis triplets at given setup 4494 7155 l FPT-1 FPT-2 Second key improvement: Proteolytic equilibrium dependence of 2D-FPT 88% Ar 8NC 8PC 8 12% Ar 5NC 5PC 8 ? Some activity cliffs in rule-based descriptor space are smoothed out in 2D-FPT-space N
7 eutr 0% al Ca tio n N 4 eutr 0% al Ca tio n al r t u Ne utral Ne tral n Neu Catio 50% al r t u Ne ion An ral Neut ation C 90% ral Neut n o Cati al
r t u Ne ion An Pharmacophore Pattern-Based Similarity Queries: Lead Hopping! Pharmacophore Hypothesis Nearest Neighbors Reference Fingerprint ? Superposition-based Similarity Scoring Automated Fingerprint Matching... Potential Pharmacophore Fingerprint Library Best Matching Candidates Docking Some examples of "hidden similarity" 100 CGRP MAPkin IL-8 NEUPTh HIVP PK55fyn EGF-TK PKC PDEIV
20 90 N 50 Cl 60 Br 60 N 0 100 H 90 S N N 70 I 80 N 50 N N 70 O N I 80 N Cl
0 100 H N 60 O N N 80 40 10 40 30 20 10 50 40 30 20 10 Successful Virtual Screening Simulations C o n fir m e d A c t iv e s ( P F ) C o n f i r m e d A c t i v e s ( FO PP TT - 32 )) C o n f ir m e d A c tiv e s ( P F ) C o n f i r m e d A c t i v e s ( FO PP TT - 32 )) C o n fir m e d In a c tiv e s ( P F )
C o n f i r m e d I n a c t i v e s ( FO PP TT -32 )) 7 % R e t r ie v e d S e e d C o m p o u n d s % R e tr ie v e d S e e d C o m p o u n d s 90 80 70 60 50 40 30 20 10 D2 45 40 35 30 25 TK 20 15 10 % R e tr ie v e d S e e d C o m p o u n d s 50 % R e tr ie v e d S e e d C o m p o u n d s 6 5 4 3 2 1 0
8 0 5 7 6 5 4 3 2 1 0 90 0 % R e tr ie v e d S e e d C o m p o u n d s 45 40 % R e tr ie v e d S e e d C o m p o u n d s C o n fir m e d In a c t iv e s ( P F ) C o n f i r m e d I n a c t i v e s ( FO PP TT - 32 )) 35 30 25 20 15 10 80 70 60 50 40 30 20 10 5
0 0 0 20 40 60 80 100 120 S e le c tio n S iz e 140 160 180 200 0 20 40 60 80 100 S e le c t io n S iz e 120
140 160 180 200 Successful QSAR model construction with 2DFPT: predicting c-Met TK activity . Learning Set Compounds Validation Set Compounds 9 8.5 8 Experimental pIC50 7.5 7 6.5 6 5.5 5 4.5 4 4 4.5 25 variables entering nonlinear model 153 molecules for training: RMSE=0.4 (log units), R2=0.82 540 molecules 5.5 6 validation: 6.5 7 7.5(log units), 8 for
RMSE=0.8 R28.5 =0.53 9 Calculated 8 validation molecules outpIC50 of 40 mispredicted by more than 1 log 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? Whats the best conformational sampler to use? Accessibility-weighted fingerprints? class to return (topological and/or 3D) estimate of the solventaccessible fraction of an atom? Tautomer-dependent fingerprints? if tautomers and their percentage were enumerated like any other microspecies THE END Pharmacophore 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 complementary to the groups available in the active site * In these cases, docking may be performed starting from pharmacophore based overlays ComPharm Overlay - chosen conformer
of the reference - chosen conformer of the candidate - pair of matching atoms - 3 Euler angles - mirroring toggle GA-controlled overlay optimization Reference Atoms ComPharm Pharmacophoric Fields 1 Pharmacophoric Features Alk. Aro. HBA HDB (+) (-) 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 X46 5 X51 X52 X53 X54 X55 X56 A descriptor of the nature of the molecules pharmacophoric neighborhood seen by every reference atom, assuming an optimal overlay of the molecule on the reference...
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