# 1 the application of computational drug design to real life problems jan kelder molecular design...
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# 1
The application of computational drug design to real life problems
Jan Kelder Molecular Design & Informatics
N.V. Organon
Bioinformatics IV CMBI Nijmegen:
Computational Drug Discovery
1 Juni 2006
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# 2
Drug targets
Nuclear hormone receptors G-protein coupled receptors (GPCRs) Ion channel receptors Serine proteases Kinases and Phosphatases Phosphodiesterases and many more
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# 3
Drug target families
2%
2%
46%
4%
4%
15%
5%
22% KinasesGPCRsIon channelsSer proteasesPhosphatasesCys proteasesNuclear receptorsOthers
A. L. Hopkins, Nature Rev. Drug Disc. 1, 727 - 730 (2002)
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# 4
Drugs on the market by target families
1%
4%
6%
1%
47%
30%
7%
4%TransportersGPCRsIon channelsEnzymesDNAIntegrinsNuclear receptorsOthers
A. L. Hopkins, Nature Rev. Drug Disc. 1, 727 - 730 (2002)
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# 5
Drug discovery
Molecular Modification Screening (MTS and HTS) Virtual Screening - 3D databases Structure-Based Drug Design
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# 6
Molecular modification towards combined 5-HT2 and H1 antagonism
CH3
N
N
N
CH3
phenbenzamine (Antergan ®)
mianserin (Tolvon ®)
cyproheptadine (Periactin ®)
N
NCH3
CH3
H1 antagonist
H1 + 5-HT2 antagonistantidepressant
no antidepressant activity
no antidepressant activity
H1 + 5-HT2 antagonist
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# 7
Molecular modification
mianserin (Tolvon ®) cyproheptadine (Periactin ®)
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# 8
Molecular modification
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# 9
Molecular modification towards combined 5-HT2 and H1 antagonism
CH3
NN
N
N
CH3tripelennamine (Azaron ®)
mirtazapine (Remeron ®)
cyproheptadine (Periactin ®)
N
NCH3
N
CH3
H1 antagonist
H1 + 5-HT2 antagonistantidepressant
no antidepressant activity
no antidepressant activity
H1 + 5-HT2 antagonist
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# 10
Molecular modification
CH3
NN
Nmianserin mirtazapineCH3
N
N
Noradrenalin (NA) Noradrenalin (NA)NA uptake blocker --------alpha-2 antagonist alpha-2 antagonistalpha-1 antagonist ---------
Serotonin (5-HT) Serotonin (5-HT)5-HT2A-2C antagonist 5-HT2A-2C antagonist
Histamine HistamineH1 antagonist H1 antagonist
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# 11
Molecular modification
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# 12
Molecular modification
mianserin mirtazapine
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# 13
5-HT GPCR subtypes
5-HT7 HUMAN
5-HT1A HUMAN
5-HT1B HUMAN
5-HT1D HUMAN
5-HT1E HUMAN
5-HT1F HUMAN
5-HT5A HUMAN
5-HT5B MOUSE
5-HT4 HUMAN
5-HT6 HUMAN 5-HT2B HUMAN
5-HT2A HUMAN
5-HT2C HUMAN
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# 14
Molecular modification towards selective 5-HT2C antagonism
CH3
O
N
N
CH3
H
(S)-(+)-mianserin (R)-(+)-Org 3363 Org 37415
Org GC 94 SDZ SER-082 (+)
CH3
N
N
H
CH3
N
N H
CH3
N
N H
H
H
CH3
N
HH
N
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# 15
(R)-(+) Org 3363 and (+) - SDZ SER-082 :Two selective 5-HT2C antagonists
(R)-(+) Org 3363 (= Org 36743) SDZ SER-082 (+)
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# 16
Fit of (R)-(+) Org 3363 and (+) enantiomer of SDZ SER-82
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# 17
Drug discovery
Molecular Modification Screening (MTS and HTS) Virtual Screening - 3D databases Structure-Based Drug Design
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# 18
HTS Compound library
HTS
HitOptimization
LeadOptimization
ConfirmedHit• validated activity / structure purified sample
In vitro optimizationon potency & selectivity
Lead•fulfill potency / selectivity criteriaand show activity in in vitro, ex vivo, or in vivo proof of principle model
Development compound
ADMET
High Throughput Screening
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# 19
High throughput screeningHigh throughput screening200,000200,000
Confirmed activesConfirmed actives100-500100-500
Retesting solid (+ LC-MS)Retesting solid (+ LC-MS)50-20050-200
Retesting Retesting
Purification/ResynthesisPurification/Resynthesis10-5010-50
Lead compoundsLead compounds0-200-20
High Throughput Screening
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# 20
320 compounds/plateup to 150 plates/day
384-wells plate384-wells plate
Orally active LH agonist: Robot screening for LH receptor agonists
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# 21
HTS on human luteinizing hormone receptor agonists
N
NS S
NH2
O
O
Confirmed hit: EC50 = 1.4 M
N
NS S
NH2
N
O
O
H
Lead compound Org 41841: EC50 = 0.03 M (= 30 nM)
N
NS S
NH2
N
O
N
H
H
O
N
O
Optimized compound Org 42599: EC50 = 3.1 nM
Not orally active Orally active
Orally active
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# 22
LMW LH agonists: Org 42599 selected for development
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# 23
Drug discovery
Molecular Modification Screening (MTS and HTS) Virtual Screening - 3D databases Structure-Based Drug Design
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# 24
Decision Tree program
Oss
Rotterdam
Amsterdam
Groningen
Arnhem
Utrecht
Oss
Rotterdam
Amsterdam
Utrecht
Groningen
Arnhem
above
belowsea-level
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# 25
Decision Tree program
Oss
Rotterdam
Amsterdam
Groningen
Arnhem
Utrecht
west ofUtrecht?
south ofGroningen?
south ofAmsterdam?
west ofArnhem?
yes
no
no
no
no
yes
yes
yes
above
belowsea-level
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# 26
N
N XR4
R7
R6
R3
R2
R1
Y
• Synthesize first set of compounds based on LH agonist Lead Org 41841 (at least 50 compounds)
• Test LH receptor activity• Calculate Molecular Descriptors:
(molecular weight, lipophilicity, polar surface)• Build Decision Tree which separates active
from inactive compounds
Decision Tree program
CalculatedCalculatedmolecularmolecular
descriptorsdescriptors
activeactive
inactiveinactive
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# 27
Decision Tree pEC50 > 7.5(n = 201)
23 actives (> 7.5) 23/23 correctly classified (100 %)
178 inactives (< 7.5) 173/178 correctly classified ( 97 %)
N
NX
R4
R7
R6
R3
R2R1
Y
P S R 6 >18.7
Inactive (14)
Inactive (3 )
Inactive (7 )
Inactive (12)
Inactive (137)
A ctive (26 )
A ctive (2)
P S R 6 >17.3
P S R 4 >2 .8
M W R 7 >30 .0
M W R 6 >87 .1
P S R 2 >1 .3
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# 28
N
N XR4
R7
R6
R3
R2
R1
Y
• Select substitution site on molecular scaffold (R2 this time)• Design virtual library of compounds• Calculate Molecular Descriptors of all virtual compounds• Apply Decision Tree and predict active and inactive
compounds• Select, synthesize and test active compounds
Decision Tree program
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# 29
Virtual library design
N
N
NH
S S
NH2
N
O
O
Br
Selection of amines based on availability (ACD database) and predicted potency
(pEC50 LH-CHO > 8.0; decision tree model derived from 250 analogues)
N
N
NH
S S
NH2
N
O
O
NR1
R2
HN
R1
R2 +
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# 30
Virtual library design
ACDSelect
Reagents
N
N X
R1
R2
R3
R4
R6
R7
Y
Generate
Library
Predict
Actives
65predictedactives
65predictedactives
1934library
compounds
1934library
compounds
1934amines
1934amines
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# 31
Virtual library for substitution at R2 derived from 1934 amines:
65 actives
1869 inactives
N
N XR4
R7
R6
R3
R2
R1
Y
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# 32
Predicted LMW LH agonists
C lip A rt0
1
2
3
4
5
6
7
8
9
pEC50 CHO-LH
CMP01
CMP02
CMP03
CMP04
CMP05
CMP06
CMP07
CMP08
CMP09
CMP10
CMP11
CMP12
CMP13
CMP14
CMP1516/26 correctly predicted > 8.0 (62 %)23/26 correctly predicted > 7.5 (88 %)
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# 33
3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists
X-ray structure of mesulergine
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# 34
3D query derived from mesulergine
6-Membered aromatic ringat a distance of 5.18 Angstromof a basic N atom (type 14D)with a tolerance of 1.0
Two aliphatic carbon atomsconnected to the basic N atom
Exclusion sphere placed in the direction where the basic N atomcan be protonated at a distanceof 7.0 Angstrom with a radius of5.3 Angstrom
A second exclusion sphere is placedat a distance of 7.0 Angstrom of thebasic N atom in the direction of theN-CH3 bond with a radius of 4.5Angstrom
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# 35
3D-database pharmacophore searches 5-HT2C and 5-HT2A antagonists
Chembase:
79716 3D structures
9229 hits (11.6 % of 79716)
1500 hits available for testing
979 hits used for testing after elimination of 521 compounds tested before on 5-HT2C receptor binding
113 5-HT2C ligands found (11.5 % of 979)
211 5-HT2A ligands found (21.5 % of 979)
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# 36
Comparison between MTS screen and 3D-database pharmacophore search
Chembase:
Mesulergine (5-HT2C) Ketanserin (5-HT2A) # hits > 95 % competition # hits > 95 % comp.
MTS screening 49 (4.9 %) 83 (8.3 %) (1000 compounds)
3D pharmacophore 113 (11.5 %) 211 (21.5 %)screening (979 compounds)
3D pharmacophore 283 (18.9 %) 470 (31.3 %)screening(1500 compounds) 3.9 x 3.8 x
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# 37
Results 3D-database pharmacophore searches 5-HT2C antagonists
NCH3
HH
H
Org 9283
Two compounds were selected that showedalready interesting 5-HT2C antagonisticpotency and selectivity (Org 9283 and Org 20659)
Org 9283 has been chosen as the lead compoundfor developing selective 5-HT2C antagonists aspotential antidepressants/anxiolytics
WO 98377EP 98-201462
5
5.5
6
6.5
7
7.5
8
8.5
9
5-HT2C 5-HT2A 5-HT2B 5-HT1A
SDZ SER-082
mianserin
Org 3363
Org 37415
Org 9283
mesulergine
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# 38
Drug discovery
Molecular Modification Screening (MTS and HTS) Virtual Screening - 3D databases Structure-Based Drug Design
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# 39
Protein Data Bank
20946 structures
17752 X-ray structures 3194 NMR structures
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# 40
Drug targets
Nuclear hormone receptors G-protein coupled receptors (GPCRs) Ion channel receptors Serine proteases Kinases and Phosphatases Phosphodiesterases and many more
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# 41
NR4A2-NOT
NR4A3-NOR1
NR4A1-NGFI
NR5A1-SF1
NR5A2-FTF
NR6A1-GCNF
NR2F1-COTF
NR2F2-ARP1
NR2F6-EAR2
NR2E3-PNR
NR2B1-RRXA NR2B2-RRXB
NR2A2-HN4G
NR2E1-TLX
NR2C1-TR2-11
NR2C2-TR4
NR2B3-RRXG
NR2A1-HNF4
NR0B1-DAX1NR0B2-SHP
NR1C1-PPAR
NR1C2-PPAS
NR1C3-PPAT
NR1D1-EAR1
NR1D2-BD73
NR1I3-CAR
NR1H2-NER
NR1H3-LXR
NR1H4-FAR
NR1I1-VDR
NR1B3-RRG1
NR1F3-RORG
NR1F2-RORB
NR1F1-ROR1NR1A2-THB1
NR1A1-THA1NR1I2-PXR
NR1B2-RRB2NR1B1-RRA1
NR3C1-GCR
NR3C4-ANDR
NR3C3-PRGRNR3A1-ESTR
NR3A2-ERBT
NR3B1-ERR1
NR3B2-ERR2
NR3C2-MCR
NR3B2-ERR3
Hormone receptors
Dimerisation
Lipid metabolism
Drug metabolism
Cholesterol metabolism
Cell growth
Development
48 nuclearreceptors
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# 42
NR4A2-NOT
NR5A2-FTF
NR2B1-RRXA NR2B2-RRXBNR2A1-HNF4
NR1C1-PPAR
NR1C2-PPAS
NR1C3-PPAT
NR1H2-NER
NR1H3-LXR
NR1H4-FAR
NR1I1-VDR
NR1B3-RRG1NR1F2-RORB
NR1F1-ROR1NR1A2-THB1
NR1I2-PXR
NR1B1-RRA1
NR3C1-GCR
NR3C4-ANDR
NR3C3-PRGRNR3A1-ESTR
NR3A2-ERBT
NR3B2-ERR3
25 X-rays LBD
NR3C2-MCR
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# 43
DCA/B FE
FE
FE
DAX1
Heterodimers:
CAR, RXR, RAR, TR,
PPAR, HNF4, ER
Heterodimers:
SF1
Drug targets: Nuclear hormone receptors (typical and atypical)
LBDDBD
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# 44
ER nuclear receptor domains
AB
D
F
E
C
LBD
DBD
DCA/B FE
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# 45
Ligand binding domains (LBD) nuclear hormone receptors
Progesterone receptor (PR) 1A28, 1E3K Androgen receptor (AR) 1I37, 1I38, 1E3G Estrogen receptor (ER) 1A52, 1ERE, 1ERR, 1QKM 1QKN, 1QKT, 1QKU, 3ERD 3ERT, 1G50, 1HJ1 Glucocorticoid receptor (GR) 1M2Z, 1NHZ, 1P93 Mineralocorticoid receptor (MR) 1Y9R, 1YA3 Vitamin D3 receptor (VDR) 1DB1, 1IE8, 1IE9 Retinoic acid receptor (RAR) 1EXA, 1EXX, 1FCX, 1FCY 1FCZ, 2LBD, 3LBD, 4LBD Retinoid X receptor (RXR) 1LBD, 1FBY, 1G1U, 1G5Y 1DKF, 1FM6, 1FM9 Peroxisome proliferator-activated rec. 1K74, 1K7L, 1KKQ, 1PRG (PPAR) 2PRG, 3PRG, 4PRG,
1GWX 2GWX, 3GWX
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# 46
Steroid hormone receptors
O
H
H
H
O
ProgesteroneTestosteroneDihydrotestosteroneEstradiolAldosteroneCorticosteroneCalcitrioletc
H-bond donor HD1 ----
---- H-bond donor HD2
Progesterone
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# 47
LBD nuclear progesterone receptor in complex with progesterone
PDB code 1A28
P.B. Sigler and S.P. Williams , Nature 393, 392 - 396 (1998)
Q
R
T
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# 48
Synthetic Steroidal Progestogens
O
O
progesterone (1933)
ethisterone (1938)O
OHCH
northisterone (1956)O
OHCH
O
OHCH
norethynodrel (1957)
OHCH
lynestrenol (1962)
O
CHOH
norgestrel (1966)
CHCH2
OH
desogestrel (1981)
O
CHOH
gestodene (1987)
norgestimate (1986)
CH
N
OAc
OH
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# 49
Synthetic Steroidal Progestogens
drospirenone (2000)
O
O
Oetonogestrel (1999)
O
CHCH2
OH
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# 50
LBD nuclear progesterone receptor in complex with etonogestrel (model)
PDB code 1A28
Q
R
T
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# 51
LBD nuclear androgen receptor in complex with dihydrotestosterone
PDB code 1I37
J.S. Sack et al. , Proc. Nat. Acad. Sci. USA 98, 4904 - 4909 (2001)
Q
R
T
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# 52
Non-steroidal androgens
NH
O2N
OH
H
H
Kaken (WO 0127086)
NH
O
CF3
O
N
CF3
Ligand (WO 00116139)
NH
O
CF3
NC
SOH
NH
CH2R OR = Cl, H
Univ. of Tennessee
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# 53
LBD nuclear androgen receptor in complex with Kaken compound (MD simulation)
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# 54
Kaken compound (MD minimum) + dihydrotestosterone (DHT)
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# 55
Homology modelling
In case no experimental 3D structure of the LBD of a nuclear receptor is available homology modelling can be tried
Template selection Sequence alignment between target and template Model building Optimization of the model Validation Ligand docking
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# 56
Homology model LBD nuclear vitamin D3 receptor vs. experimental structure Homology model (green)
LBD VDR based on LBD PPAR
X-ray structure VDR (blue) Alignment:
D. R. Boer et al , Thesis University of Utrecht (2001)
33 % similarity for residues 131 - 42753 % similarity for residues 226 - 427
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# 57
LBDs nuclear vitamin D3 receptor and PR in complex with calcitriol and progesterone
PDB code 1DB1PDB code 1A28
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# 58
Drug targets
Nuclear hormone receptors G-protein coupled receptors (GPCRs) Ion channel receptors Serine proteases Kinases and Phosphatases Phosphodiesterases and many more
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# 59
Bovine rhodopsin X-ray model
K. Palczewski et al., Science 289, 739 - 745 (2000)
PDB code 1F88
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# 60
X-ray models GPCR and G-protein
ß
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# 61
Ligand binding domains (LBD) G-protein coupled receptors(GPCRs)
Follicle Stimulating Hormone (FSH) receptor Luteinizing Hormone (LH) receptor Thyroid Stimulating Hormone (TSH) receptor Serotonin (5-HT) receptors and many more
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# 62
TRANSMEMBRANE REGION hLH receptor + Org 41841 (MD)
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# 63
Lead optimization LH agonistLH receptor homology model
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# 64
TRANSMEMBRANE REGION hLH receptor + Org 42599 (MD)
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# 65
LO LMW LH agonists
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# 66
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# 67
Luteinizing hormone (LH) + LMW LH agonist Org 41841
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# 68
LH receptor activation
LHRTM domain
EC domain LH/hCG
LMW LH agonist
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# 69
LFR/FLR chimeric receptors
transient transfection LFR/FLR.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
LHR-LH
LHR-FSH
FSHR-LH
FSHR-FSH
LFR-LH
LFR-FSH
FLR-LH
FLR-FSH
fold
incr
ease
0
0.1
1
10
100
Chimeric receptors respond as expected
mU/ml
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# 70
LTR/TLR chimeric receptors
Org 42599 binds in TM domain of LH receptor
transient transfection LTR/TLR - ORG42599H.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
LHR TSHR LTR TLR
fold
incr
ease
0
-11
-9
-7
-6
M
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# 71
Ligand binding domains (LBD) G-protein coupled receptors(GPCRs
Follicle Stimulating Hormone receptor Luteinizing Hormone receptor Thyroid Stimulating Hormone receptor Serotonin (5-HT) receptors and many more
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# 72
Serotonin GPCRs
OH
NH
NH3+
AcetylcholineNoradrenalinAdrenalinDopaminSerotoninHistaminOpioidetc
Acidic residue Asp ---- ---- H-bond acceptor Ser/Thr
Serotonin
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# 73
5-HT2C GPCR transmembrane model based on bacteriorhodopsin
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# 74
Mutation studies 5-HT2C receptor
serotonin 8.0 6.5 5.3
Org 35018 7.6 6.4 5.5
OH
NH
NH2
NH
NH2
OH
S
NH2
Wildtype S219A F327A
5-HT2C mutant mutant
receptor receptor receptor
pKi pKi pKi
tryptamine 6.9 6.6 5.2
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# 75
5-HT2C GPCR model based on bovine rhodopsin
D
S
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# 76
5-HT GPCRs: Structure based query
TM3: CxxxxxxDxxxxxxxxxxxxxxxxDRY
TM5: xxxxxxxxSxxxFxxPxx TM5: xxxxxxxxTxxxFxxPxx
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# 77
5-HT GPCRs
1869 GPCRs
879 GPCRs (unique)
71 GPCRs
12 5-HT GPCRs
59 GPCRs 3 new
Structure based query
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# 78
Conclusions
The PDB forms a rich source of experimental structures that expands rapidly
Homology modeling is useful in cases where experimental structures are not yet available and good templates exist
Knowledge of how ligands bind to proteins can be utilised to suggest annotations of unknown protein sequences