Presentation on theme: "Pharmacophores in Chemoinformatics: 1. Pharmacophore Patterns & Topological Fingerprints Dragos Horvath Laboratoire d’InfoChimie UMR 7177 CNRS – Université."— Presentation transcript:
Pharmacophores in Chemoinformatics: 1. Pharmacophore Patterns & Topological Fingerprints Dragos Horvath Laboratoire d’InfoChimie UMR 7177 CNRS – Université de Strasbourg email@example.com
The Pharmacophore Way of Life – A Medicinal Chemist’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 Interactions Atoms are more or less hard spheres – squeezing them against each other causes a sharp rise in energy: –E rep =A ij d -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… –E att = - B ij d -6
The Interaction Saga: (2) Electrostatics & Solvation Coulomb charge-charge interactions are easy to compute, once the partial charges Q k are assigned on the atoms… –E Coul =Q i Q j /4 d … 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.
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, 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 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)=[D 1 (M), D 2 (M), …,D N (M)]; each D i 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!
Tricentric Pharmacophore Fingerprints: monitoring feature arrangement Topological: the distance between two features equals the (minimal) number of chemical bonds between them N N O N C l 9 4 11 Spatial: if stable conformers are known, use the distance in Ǻ between two features
Example: Binary Pharmacophore Triplets 33 3 3 6 7 4 34 4 35 Hp3-Hp3-Hp3Hp3-Hp3-Hp4Hp3-Hp3-Hp5 … Ar4-Hp3-Hp4Ar4-Hp3-Hp5 ………… Hp7-Ar4-PC6 … Hp3-HA5-Ar5 5 53 000…00……1………0……0… Basis Triplets: all possible feature combinations all possible feature combinations at a given series of distances… at a given series of distances… Hp4-HA5-Ar5 5 54 ? ? ? ? Pickett, Mason & McLay, J. Chem. Inf. Comp. Sci. 36:1214-1223 (1996) ………
First key improvement: Fuzzy mapping of atom triplets onto basis triplets in 2D-FPT 33 3 4 6 7 4 34 5 53 000…00…+6……+3…………0…5 5 4 Hp3-Hp3-Hp3Hp3-Hp3-Hp4Hp3-Hp3-Hp5 … Ar4-Hp3-Hp4Ar4-Hp3-Hp5 ………… Hp7-Ar4-PC6 … Hp3-HA5-Ar5Hp4-HA5-Ar5 ……… D i (m) = total occupancy of basis triplet i in molecule m.
Combinatorial enumeration of basis triplets Example: there are 36796 basis triplets, verifying triangle inequalities, when considering 6 pharmacophore types and 11 edge lenghts between E min =3 to E max =13 with an increment of E step =1: (3, 4, 5,…13) –Canonical representation: T 1 d 23 -T 2 d 13 -T 3 d 12 with T 3 ≥T 2 ≥T 1 (alphabetically). 4 6 7 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. 4 6 7 Ar4-Hp7-Hp6 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: 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.
Second key improvement: Proteolytic equilibrium dependence of 2D-FPT Ar5-NC5-PC8 Ar8-NC8-PC8 ? 12% 88%
Some ‘activity cliffs’ in rule-based descriptor space are smoothed out in 2D-FPT-space Neutral Cation Neutral Anion Neutral 90%Cation Neutral 50%Cation Neutral Anion Neutral 40%Cation Neutral 70%Cation
Successful QSAR model construction with 2D- FPT: predicting c-Met TK activity 25 variables entering nonlinear model 153 molecules for training: RMSE=0.4 (log units), R 2 =0.82 40 molecules for validation: RMSE=0.8 (log units), R 2 =0.53 8 validation molecules out 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? 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…
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 complemen- tary 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
ComPharm 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 Features Alk.Aro.HBAHDB(+)(-) 1X 11 X 12 X 13 X 14 X 15 X 16 2X 21 X 22 X 23 X 24 X 25 X 26 3X 31 X 32 X 33 X 34 X 35 X 36 4X 41 X 42 X 43 X 44 X 45 X 46 Reference Atoms 5X 51 X 52 X 53 X 54 X 55 X 56