Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”

1. Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi” Protein modeling by fragmental approach: connecting global homologies with local peculiarities Alessandro Pedretti

2. Molecular docking Molecular dynamics Protein modelling Structure-based studies • In order to perform structure-based studies as: • ligand optimization; • virtual screening; • signal transduction; • substrate recognition. • the 3D structure of the biological target is required. • Unluckily, the experimental structure (X-ray diffraction or NMR) is not available for all proteins.

3. GFGPHQRLEKLDSLLS… 1D structure Protein modelling 3D structure Comparative modelling Protein modelling Ab-initio modelling What’s the protein modelling ? • The protein modelling allows to obtain the 3D structure of a protein from its aminoacid sequence (primary structure): • It can be classified into two main approaches:

4. Structures obtained by experimental approaches (X-ray and NMR). Comparative modelling • It’s based on the assumption: proteins with high homology of sequence should have similar folding. Target sequence 3D structure database 3D template Homology > 70 % Alignment Between target and template Rough 3D model To refinement workflow

5. Ab-initio modelling • It’s based on physical principles and geometric rules obtained by sequence and structure analysis of the 3D experimental models. Target sequence Folding prediction Application of physical and geometric rules Multiple solutions Global optimization By MM and stochastic approaches Rough 3D model To refinement workflow

6. Comparative vs. ab-initio modelling *Models that are structurally similar due to the common template. • The possibility to obtain structural “clones” is very high, submitting whole query sequences of protein families with high homology to a limited number of 3D templates (e.g. transmembrane proteins).

7. Fragmental approach Target sequence Fragmentation in structural domains Done on the basis of information included in databases and/or domain finder tools. Folding prediction of each fragment Trough multiple comparative modelling procedures. By geometric superimposition with the 3D structure of the global template, using molecular modelling tools as VEGA ZZ. Assembling using the global 3D template Rough model To refinement workflow

8. Model refinement procedure Rough model VEGA ZZ + NAMD Missing residues Side chains add Hydrogens add Energy minimization Structure check Final model

9. 17 subunit types a1, b1, , d,e a2-10, b2-4 • The complete model didn’t exist. • The design of selective a4b2 ligands is problematic due to the low information about the binding mode. Human a4b2 subtype Muscle Nervous system Human a4b2 nicotinic receptor • The nicotinic acetylcholine receptors (nAchRs) are composed by five subunits assembled around a central pore permeable to cations. • The therapeutic interest on nicotinic ligands is highlighted by diseases involving the nAchRs as: Alzheimer’s and Parkinson’s disease, autism, epilepsy, schizophrenia, depression, etc. Pedretti A. et Al., Biochemical and Biophysical Research Communications, Vol. 369, 648–53 (2008).

10. 4 transmembrane domains 2 cytoplasmic loops 1 extracellular loop 2 terminal domains Fragmentation Primary structure MM refinement Final monomer Monomer modeling SwissProt Folding prediction of each fragment Fugue The docking results were filtered the Torpedo Californica nAChR structure. Helices assembly by molecular docking ESCHER NG Full assembly Side chains VEGA ZZ Hydrogens VEGA ZZ + NAMD

11. Side view Top view Complex assembling 2x a4 Multistep docking: a4 + b2 → a4b2 2 a4b2 → (a4)2(b2)2 b2 + (a4)2(b2)2 → (a4)2(b2)3 + a4b2 ESCHER NG 3x b2

12. Nicotine Epibatidine ABT-418 Citisine A-85380 VEGA ZZ FRED 2 NAMD Ligand Binding site selection Trp182, Cys225, Cys226 in a4 + Docking Minimization a4b2 receptor Final complex Model validation • The soundness of the resulting model was checked docking a set of know nicotinic ligands: • All these ligands were simulated in their ionized form.

13. Trp82 b2 Cys225 a4 Asn134 b2 Cys226 a4 Phe144 b2 Trp182 a4 Docking results • After the final MM minimization, the docking scores were recalculated by Fred 2 (ChemGauss2 scoring function): a4b2 – nicotine complex

14. Metabotropic receptor Ionotropic receptor Human glutamate transporter EAAT1 • L-glutamate is the main excitatory neurotransmitter in the CNS. Synaptic cleft Axon Dendrite Excitatory effects Glutamate EAAT1-5 • It can also overactivate the ionotropic receptors, inducing a series of destructive processes involved in acute and chronic neurological diseases (e.g. amyotrophic lateral sclerosis, Alzheimer’s disease, epilepsy, CNS ischemia, etc). Pedretti A. et Al., ChemMedChem, Vol. 3, 79-90 (2008).

15. EAAT ligand classification • They can be classified in: • Natural substrates. • Substrate inhibitors. • Non transported uptake blockers. • The last two classes are interesting because in pathological conditions, when the electrochemical gradient is damaged, EAATs can operate in reverse mode, overactivating the post-synaptic receptors. • Research aims: • Human EAAT-1 3D structure by homology modeling. • Pharmacophore models for all ligand classes.

16. Fragmentation Primary structure Folding prediction of each fragment Fugue The assembly was carried out using the crystal structure of glutamate transporter homologue from Pyrococcus horikoshii. MM refinement Final monomer VEGA ZZ + NAMD Monomer modeling The domains were found aligning the sequences of EAAT1 and glutamate transporter from Pyrococcus horikoshii. SwissProt Full assembly Hydrogens VEGA ZZ Side chains

17. Complex assembling ESCHER NG VEGA ZZ + NAMD Monomer Homotrimer • Complex refinement protocol: • 1 ns of simulation time; • restrained transmembrane segments; • final conjugate gradients minimization. DEEP surface

18. Docking Mopac 7 FlexX Ligand Minimization Complex EAAT1 monomer Docking studies • Two ligand subsets were docked: • natural substrates and competitive substrates inhibitors (16); • non-transported blockers (16). • The following procedure was applied to all ligands: • The docking analyses were focused on residues enclosed in a sphere centered on Arg479 (TM4). Mutagenesis studies showed this residue plays a pivotal role in the substrate interaction.

19. Docking results: substrate inhibitors Met451 Val449 Arg479 Thr450 Gln204 EAAT1 – (2S, 4R)-methylglutamate complex Gln445 pKm = 4.88 (±0.04) – 1.52 (±0.12) Vover N = 15, r2 = 0.93, s = 0.11, F = 174.11 Where Vover is maximum overlapping volume between the ligand and EAAT1 computed by FlexX.

20. Leu448 Ile465 Val449 Ile468 Thr450 Trp473 Arg479 Gln445 Gln204 Docking results: non-transported blockers EAAT1 – L-TBOA complex pIC50 = 0.4446(±0.07) – 0.141(±0.02)ScoreFlexX N = 16, r2 = 0.77, s = 0.55, F = 43.46

21. Natural and substrate inhibitors Non-transported blockers En = excluded volume An = H-bond acceptors P = ionisable group (positively charged) Y = hydrophobic region L-glutamate TFB-TBOA Pharmacophore mapping • The two pharmacophore models were obtained by Catalyst 4 software. • Both models highlight the key features required for the interaction. • Mapping the docking results onto the pharmacophores, it’s possible to highlight the two approaches are successfully overlapped.

22. Conclusions • We obtained the full model of two transmembrane protein through the fragmental approach. • Performing molecular docking studies, we highlighted the main interaction between ligands and the proteins that were confirmed by experimental data, obtained by mutagenesis studies. • Although the number of considered ligands isn’t statistically relevant, we obtained good relationships between the docking scores and the experimental data, confirming the soundness of both models. • All these results show the power and the goodness of the fragmental approach that is able to overcame the problems introduced by global homologies and the possibility to obtain structural clones.

23. www.vegazz.net www.ddl.unimi.it Acknowledgments • Giulio Vistoli • Cristina Marconi • Cristina Sciarrillo • Laura De Luca