Transcript A short course on VEGA ZZ

Università degli Studi di Milano
Dipartimento di Scienze Farmaceutiche “Pietro Pratesi”
Protein modeling by fragmental approach:
connecting global homologies
with local peculiarities
Alessandro Pedretti
Structure-based studies
• In order to perform structure-based studies as:
– ligand optimization;
Molecular docking
– virtual screening;
– signal transduction;
– substrate recognition.
Molecular dynamics
the 3D structure of the biological target is required.
• Unluckily, the experimental structure (X-ray diffraction or NMR) is not
available for all proteins.
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…
1D structure
Protein modelling
3D structure
• It can be classified into two main approaches:
Comparative modelling
Protein 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 structure database
3D template
Alignment
Rough 3D model
Structures obtained by
experimental approaches (X-ray
and NMR).
Homology > 70 %
Between target and template
To refinement workflow
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
Rough 3D model
By MM and stochastic approaches
To refinement workflow
Comparative vs. ab-initio modelling
Comparative
Ab-initio
3D template
Yes
No
Success
High
Low
Computational time
Low
Very high
Structural “clones”*
Yes
No
*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).
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
procedures.
Assembling using the
global 3D template
By geometric superimposition with the 3D
structure of the global template, using
molecular modelling tools as VEGA ZZ.
Rough model
comparative
To refinement workflow
modelling
Model refinement procedure
Rough model
Missing residues
Side chains add
Hydrogens add
Energy minimization
Structure check
Final model
VEGA ZZ
+
NAMD
Human a4b2 nicotinic receptor
• The nicotinic acetylcholine receptors (nAchRs) are composed by five
subunits assembled around a central pore permeable to cations.
17 subunit types
a1, b1, , d, e
a2-10, b2-4
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
a4b2 subtype
• The complete model didn’t exist.
• The design of selective a4b2 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
Fragmentation
Primary structure
SwissProt
Fugue
Folding prediction of
each fragment
ESCHER NG
Helices assembly by
molecular docking
4 transmembrane domains
2 cytoplasmic loops
1 extracellular loop
2 terminal domains
The docking results were filtered
the Torpedo Californica nAChR
structure.
Full assembly
VEGA ZZ
Side chains
Hydrogens
VEGA ZZ + NAMD
MM refinement
Final monomer
Complex assembling
Side view
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
Top view
Model validation
• The soundness of the resulting model was checked docking a set of know
nicotinic ligands:
H3C
Cl
N
N
N
H
N
N
N
H3C
Epibatidine
NH
N
O
H3C
Nicotine
NH
H
ABT-418
N
O
Citisine
A-85380
• All these ligands were simulated in their ionized form.
Ligand
VEGA ZZ
FRED 2
NAMD
+
Binding site selection
Docking
Minimization
Trp182, Cys225, Cys226 in a4
a4b2 receptor
Final complex
Docking results
• After the final MM minimization, the docking scores were recalculated by
Fred 2 (ChemGauss2 scoring function):
Trp82 b2
Compound
Ki
(nM)
Score
(Kcal/mol)
Epibatidine
0.009
-48.7
A-85380
0.05
-45.1
Citisine
0.16
-42.6
Nicotine
1.0
-38.9
ABT-418
4.6
Cys225 a4
Asn134 b2
Cys226 a4
Phe144 b2
Trp182 a4
-35.9
a4b2 – nicotine complex
Human glutamate transporter EAAT1
• L-glutamate is the main excitatory neurotransmitter in the CNS.
Synaptic cleft
Axon
Dendrite
Metabotropic receptor
Excitatory
effects
Glutamate
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).
Pedretti A. et Al., ChemMedChem, Vol. 3, 79-90 (2008).
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
Fragmentation
Primary structure
SwissProt
Fugue
Folding prediction of
each fragment
Full assembly
VEGA ZZ
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.
Hydrogens
Side chains
VEGA ZZ + NAMD
MM refinement
Final monomer
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
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
Mopac 7
FlexX
Minimization
Docking
Complex
EAAT1 monomer
• 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
Met451
Val449
Arg479
Gln204
EAAT1 – (2S, 4R)-methylglutamate complex
Thr450
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.
Docking results: non-transported blockers
Leu448
Ile468
Ile465
Val449
Thr450
Trp473
Arg479
EAAT1 – L-TBOA complex
Gln445
Gln204
pIC50 = 0.4446(±0.07) – 0.141(±0.02)ScoreFlexX
N = 16, r2 = 0.77, s = 0.55, F = 43.46
Pharmacophore mapping
Natural and
L-glutamate
substrate inhibitors
En = excluded volume
An = H-bond acceptors
Non-transported
TFB-TBOA
blockers
P
Y
= ionisable group (positively charged)
= hydrophobic region
• The
two pharmacophore
models
were
by Catalyst it’s
4 software.
Mapping
the docking results
onto
theobtained
pharmacophores,
possible to
highlight
the two
approaches
successfully
overlapped.
• Both
models
highlight
the keyare
features
required
for the interaction.
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
• Laura De Luca
• Cristina Marconi
• Cristina Sciarrillo
www.vegazz.net
www.ddl.unimi.it