by Siavoush Dastmalchi Tabriz University of Medical Sciences Tabriz-Iran
description
Transcript of by Siavoush Dastmalchi Tabriz University of Medical Sciences Tabriz-Iran
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by
Siavoush DastmalchiTabriz University of Medical Sciences
Tabriz-Iran
Modelling the Structures of G Protein-Coupled Receptors Aided
by Three-Dimensional Validation
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Biological functions of IMPs:
• Channels and Pumps (Cl-channels, Na+/K+-ATPase)
• Maintaining appropriate contact (fibronectin receptors)
• Signal transducing proteins (GPCRs)• Respiratory enzyme complexes• Toxin and pathogen entry regulating proteins
(neuraminidase)
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Sparse structural knowledge about IMPs
• Few high-resolution 3D structures are available
• Difficult to grow crystals
IMPs are abundant
• 35-40% of yeast genes
• and ~20% of all human genes code for IMPs
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0
50
100
150
200
250
300
1985 1990 1995 2000 2005
Year
No
of s
eque
nces
. 10
^3
05101520253035404550
No
of s
truc
ture
s . 1
0^3
All swiss-prot entries
All PDB structures
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Structurally solved IMPs:
• Bacteriorhodopsin• Cytochrome C oxidase• Light harvesting complex• Photosynthetic reaction center• Porin
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IMP graphics
BacteriorhodopsinMaltoporin
Cytochrome c oxidase
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Required information:
• Location of transmembrane (TM) segments• Basic topology of TM domain• Orientation of TM segments• Relative depth of TM segments in lipid
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1 2 3 4
1 2 3 4
1 2
3 4
N
F
OR
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Baldwin(1997) modelPalczewski (2000) 2.8Å
Bovine Rhodopsin
Extracellular
Intracellular
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Palczewski et al. 2000 Science 289: 739-745.
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Profiles–3D algorithm
from
InsightII molecular modeling package
Developed in David Eisenberg’s lab.
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0.80
0.40
0.00
E P2
P1
B1
B2
B3
0 40 80 120Area buried (Å2)Fraction polar
Environmental classes
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W F … K RB1 1.11 1.28 … -1.37 -1.80B1 0.92 0.96 … -3.04 -1.52B1 c 0.96 1.40 … -2.78 -2.35...
..
.
..
.
..
.
..
.
..
.E 0.81 -0.83 … -1.54 -1.12E c -2.06 -1.63 … -0.10 -0.41
3D–1D scoring tableC
LA
SSE
S
RESIDUES
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Valpred
Graphs
Tables
Graphics
Options
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Dastmalchi Dastmalchi et alet al. 2002 . 2002 Mol. SimulationMol. Simulation 28: 845-851. 28: 845-851.
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05
10152025303540
32 52 72 92 112
Compatibility score values
Pop
ulat
ion
(num
ber)
Distribution of raw Profiles-3D and lipid-corrected compatibility score values of 493 GPCRs models represented by open circle and closed circle respectively.
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Improvement of Compatibility Score
• It is clear that by simulation of a lipid environment
around the model the total compatibility score is
increased.
• This is an indication that for reasonable modelling of
IMPs, the correct environment needs to be used in
term of model building and verification.
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43
5 67
GPCR Helical BundleGPCR Helical BundleNN
CC
S-S
S-S
P
N-linked glycosylation
Disulphide bond
Covalently attachedpalmitate
Phosphorylation
P
PG protein
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0.960.970.980.991.001.011.021.031.041.051.06
0 60 120 180 240 300
Rotational degree
Nor
mal
ised
Lip
id-c
orre
cted
co
mpa
tibili
ty s
core
1234567
Helix
Average of normalised lipid-corrected compatibility scores for the models of GPCRs vs rotation of individual helices.
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9092949698
100102104
0 60 120 180 240 300
Rotational degree
Tota
l com
patib
ility
sco
re 1234567
Helix
Total of lipid-corrected compatibility scores of the human galanin receptor model vs rotation of individual helices.
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3
4
5
6
7
8
0 60 120 180 240 300
Rotation (degree)
CAD value
HIHIIHIIIHIVHVHVIHVII
Helix
1
2
3
4
5
6
7
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RMSD comparison of Rhodopsin models generated using different methods with its
crystal structureMethod Reference RMSD (Å)
REPIMPS 3.3
PREDICT Shacham et al. Med Res Rev (2001) 21: 472.
2.9
ROSETTA Yarov-Yarovoy et al. Proteins (2006) 62: 1010.
3.8
Baldwin Baldwin et al. J Mol Biol (1997) 272: 144.
3.2
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Helical wheel representation of helical segments 1-4 of hGalR1
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Mean LCS for each group
112.0108.0
104.0100.0
96.092.0
88.084.0
80.076.0
72.068.0
Distribution of LCS of the models of hGalR1
Freq
uenc
y
12000
10000
8000
6000
4000
2000
0
Std. Dev = 5.93
Mean = 88.5
N = 78125.00
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1122
77
6644
33
55
Extracellular
Intracellular
hGALR1-GALANIN COMPLEX
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Summary & Conclusion• Our method can be used for evaluation of 3D models of GPCRs• Advantages of this method lies in its 3D characteristic & environmental
sensitivity
• In the case GPCRs, we used simple modelling procedure & the sampling method didn’t cover all possible conformational space
• Predicting the positions and incorporating the possible irregular geometries in the helical regions, and using sampling methods in positioning different segments of TMHs, followed by scoring the models with our method may further improve the quality of the models.
• Results provided here suggest that probably there is no unique consensus template for modeling GPCRs. However, Baldwin model or even the crystal structure of rhodopsin can be a good starting point. Meanwhile, the quality of the model can be assessed and improved by taking in to account environmental considerations.
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Acknowledgments
• Thank you all
• Thanks to the Organizers of InCoB2007
• Dr Bret Church (Sydney University)
• Dr Michael Morris (Sydney University)