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Case Study: Dopamine D 3 Receptor Anthagonists Chapter 3 – Molecular Modeling 1.

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Presentation on theme: "Case Study: Dopamine D 3 Receptor Anthagonists Chapter 3 – Molecular Modeling 1."— Presentation transcript:

1 Case Study: Dopamine D 3 Receptor Anthagonists Chapter 3 – Molecular Modeling 1

2 Today’s lecture 2 Dopamine D 3 Receptor Anthagonists Building a pharmacophore model 3D QSAR analysis

3 Dopamine Receptor 3 5 different subtypes: D 1, D 2, D 3, D 4, D 5 Defects is related to several diseases Parkinson’s disease, schizophrenia etc. Medical treatment Limited by side effects from drugs binding to various subreceptors Need selectivity!

4 Building a pharmacophore model 4 5 ligands (D 3 receptor antagonists) High affinity Known steric and electrostatic information Structure: Highly potent

5 Building a pharmacophore model 5 Strategy Decompose molecule into fragments Molecular allignment using FlexS One treated flexible One treated rigid

6 Building a pharmacophore model 6 Rigid part SYBYL: Simulated annealing Low T conformation Two clusters (conformation family) rigid

7 Building a pharmacophore model 7 Flexible part: Fit onto rigid part FlexS flexible

8 Building a pharmacophore model 8 The spacer Generally flexible Examined in detail: quite rigid overlap

9 Building a pharmacophore model 9 Simulated annealing on bicyclic ring system 3 conformations

10 Building a pharmacophore model 10 Aromatic/Amidic residue Assumed planar Include this restriction in previous examination planar

11 Building a pharmacophore model 11 Systematic search 10 degree increment Tripos force field → 992 conformations

12 Building a pharmacophore model 12 Compound 1 fitted on all 992 conformations with FlexS Highest rated = binding conformation of these fragments Compound 1

13 Building a pharmacophore model 13 Now we have the conformation of all fragments Recombine fragments Pharmacophore model!

14 Building a pharmacophore model 14 Molecular interaction fields with GRID C=O N-H ST-127 ST-84 ST-205 ST-86 H-bond acceptor Basic nitrogen

15 Building a pharmacophore model 15 ST-127 ST-84 ST-205 ST-86

16 Building a pharmacophore model 16

17 Building a pharmacophore model 17

18 3D QSAR Analysis 18 With a pharmacophore model Arrange potent molecules or fragments in their bioactive conformation Guideline for designing next- gen. enhanced compounds

19 3D QSAR Analysis 19 40 D 3 antagonists Fitted to the pharmacophoric conformation (model) Superimposed onto each other (FlexS) Refined with SYBYL (steepest decent)

20 3D QSAR Analysis 20 Calculate GRID interaction fields for all 40 ligands Now with alot of probes 14580 probe-ligand interactions per compound! 14580: Too many variables! Will introduce noise

21 3D QSAR Analysis 21 To overcome the problem Filter out variables with only few values Filter out variables with low change (<10 -7 kcal/mol) If they all lie in a small interval they can be disregarded

22 3D QSAR Analysis 22 Next: Set up a PLS model (Partial Least Square) It can handle a statistical model with more energy values than compounds The energy values are correlated with each other Many of them are not important for the biological activity We can use a few different algorithms in the problem GOLPE to reduce the number of variables D-optimal (good >1000 variables) Fractional Factorial Design (FFD)

23 3D QSAR Analysis 23 Each time: Cross validate with Leave One Out (LOO) Make a model with all the compounds except one Predict its activity Do it with all compounds

24 3D QSAR Analysis 24 A Fractional Factorial Design (FFD) method determines the predictivity of each variable Each variable is classified as either Helpful for predictivity Destructive for predictivity Uncertain Only helpful variables are included in the PLS model Good to use after D-optimal has reduced the variables to a few thousand

25 3D QSAR Analysis 25 High cross validation value

26 3D QSAR Analysis 26 LOO cross validation in final model

27 3D QSAR Analysis 27 Validation of the 3D QSAR method Many variables were treated Chance correlation? Test with scrample set Randomly assign the binding affinities of the ligands Generate PLS model and reduce variables as before Cross validate with LOO

28 3D QSAR Analysis 28 Prediction of External ligands Try with some different type of structures that also shows reasonable binding activity towards the receptor Lies within ± 0.5 SDEP = 0.57

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