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

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. Molecular docking Molecular dynamics Protein modelling

What’s the protein modelling ? The protein modelling allows to obtain the 3D structure of a protein from its aminoacid sequence (primary structure): GFGPHQRLEKLDSLLS… Protein modelling1D structure 3D structure It can be classified into two main approaches: Protein modelling Comparative modelling Ab-initio modelling

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

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 Multiple solutions Global optimization Rough 3D model Folding prediction To refinement workflow Application of physical and geometric rules By MM and stochastic approaches

Comparative vs. ab-initio modelling 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). ComparativeAb-initio 3D templateYesNo SuccessHighLow Computational timeLowVery high Structural “clones”*YesNo *Models that are structurally similar due to the common template.

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

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

Human  4  2 nicotinic receptor The nicotinic acetylcholine receptors (nAchRs) are composed by five subunits assembled around a central pore permeable to cations. 17 subunit types  1,  1, ,    2-10,  2-4 Muscle Nervous system 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. Human  4  subtype The complete model didn’t exist. The design of selective  4  2 ligands is problematic due to the low information about the binding mode. Pedretti A. et Al., Biochemical and Biophysical Research Communications, Vol. 369, 648–53 (2008).

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

Complex assembling + 2x  4 3x  2 4242 Side view Top view Multistep docking:  4 +  2 →  4  2 2  4  2 → (  4) 2 (  2) 2  2 + (  4) 2 (  2) 2 → (  4) 2 (  2) 3 ESCHER NG

Model validation The soundness of the resulting model was checked docking a set of know nicotinic ligands: Nicotine EpibatidineABT-418CitisineA All these ligands were simulated in their ionized form. Ligand  4  2 receptor + Docking Binding site selection Trp182, Cys225, Cys226 in  4 Binding site selection Trp182, Cys225, Cys226 in  4 Minimization Final complex VEGA ZZFRED 2NAMD

Docking results After the final MM minimization, the docking scores were recalculated by Fred 2 (ChemGauss2 scoring function): Compound Ki (nM) Score (Kcal/mol) Epibatidine A Citisine Nicotine ABT Cys225  4 Cys226  4 Trp182  4 Phe144  2 Asn134  2 Trp82  2  4  2 – nicotine complex

Human glutamate transporter EAAT1 Pedretti A. et Al., ChemMedChem, Vol. 3, (2008). L-glutamate is the main excitatory neurotransmitter in the CNS. Glutamate Synaptic cleft Excitatory effects AxonDendrite Metabotropic receptor Ionotropic receptor 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).

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.

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

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

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: Ligand Minimization Docking EAAT1 monomer Complex Mopac 7FlexX 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.

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

Docking results: non-transported blockers pIC 50 = (±0.07) – 0.141(±0.02)Score FlexX N = 16, r 2 = 0.77, s = 0.55, F = Ile468 Trp473 Arg479 Gln204 Gln445 Thr450 Val449 Leu448 Ile465 EAAT1 – L -TBOA complex

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

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.

Acknowledgments Giulio Vistoli Cristina Marconi Cristina Sciarrillo Laura De Luca