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Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition Carol A. Kemp, Jack U. Flanagan, Annamaria J. van.

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Presentation on theme: "Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition Carol A. Kemp, Jack U. Flanagan, Annamaria J. van."— Presentation transcript:

1 Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition Carol A. Kemp, Jack U. Flanagan, Annamaria J. van Eldik, Jean-Didier Mare´chal, C. Roland Wolf, Gordon C. K. Roberts,§ Mark J. I. Paine & Michael J. Sutcliffe J. Med. Chem. 2004

2 Cytochrome P450 (Cyp450) Group of oxidative enzymes Exits in all lineages Membrane protein (ER, mitochondria) Metabolite thousands of endogenous and exogenous compounds

3 Importance of Cyp 2D6 Oxidation of >50 drugsInhibited by drugs Cytochrome P450 2D6 Analgesics (pain killers) Beta Blockers (cardiovascular diseases) Quinidine (heart rhythm disturbance) fluoxertine (depression)

4 Research Goals Previous work: HM + docking position metabolism site above heme Typical (basic nitrogen) substrates Screening a database for CYP2D6 inhibitors Can 3D method improve over 2D approach Asses model accuracy

5 Comparative Modeling of 2D6 FSSP = Fold classification & Secondary Structure Alignment (DALI) Bacterial P450Mammalian P450

6 Model Validation Does a sequence fit a structure ? 1.Buried area 2.% side chain buried with polar atoms 3.Secondary structure Errat non covalently pairs interactions ( CC, CN, CO, NN, NO, OO ) 9 residue sliding window

7 Screened Available Databases Ekins ( 21 compounds ) Strobl (30 compounds ) Docking Software:GOLD 2.0 Genetic algorithm Full ligand flexibility partial protein flexibility Energy functions partly based on conformational and non-bonded contact information from the CSD 12 ring systems r 2 = 0.561 ring system r 2 = 0.36

8 Creating an Additional Dataset NCI database (compounds tested for treating cancer) Weight ~ Ekins & Strobl datasets < 4 Ring Systems Availability 33 Compunds Basic Nitrogen & Aromatic Group

9 Consistency with known inhibition measurements Cyp450 2D6 Small Molecule AMMC demethylase Inhibition AMMC Ekins / Strobl

10 Predicting inhibition using Docking Cutoffs: IC 50 < 10 µM = inhibitor -30 kJ/mol = predicted inhibitor Predictions: 13 correct 7 false positives

11 Questions for discussion 1.Is the method applicable for large scale database scanning ? (~7 min CPU on a one processor Silicon Graphics R14) 2.Can substrate affinity be predicted with the same accuracy ? 3.Are positions reliable enough for predicting drug-drug interactions ?

12 Thank you for your attention


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