Title: Functional 3-D modelling of G protein coupled receptors
1Functional 3-D modelling of G protein coupled
receptors
Ugur Sezerman
2Central Dogma
3Motivation
- Knowing the structure of molecules enables us to
understand its mechanism of function - Current experimental techniques
- X-ray cystallography
- NMR
4X-Ray Crystallography
- crystallize and immobilize single, perfect
protein - bombard with X-rays, record scattering
diffraction patterns - determine electron density map from scattering
and phase via Fourier transform - use electron density and biochemical knowledge of
the protein to refine and determine a model
"All crystallographic models are not equal. ...
The brightly colored stereo views of a protein
model, which are in fact more akin to cartoons
than to molecules, endow the model with a
concreteness that exceeds the intentions of the
thoughtful crystallographer. It is impossible for
the crystallographer, with vivid recall of the
massive labor that produced the model, to forget
its shortcomings. It is all too easy for users of
the model to be unaware of them. It is also all
too easy for the user to be unaware that, through
temperature factors, occupancies, undetected
parts of the protein, and unexplained density,
crystallography reveals more than a single
molecular model shows. -
Rhodes, Crystallography Made Crystal Clear p.
183.
5NMR Spectroscopy
- protein in aqueous solution, motile and
tumbles/vibrates with thermal motion -
- NMR detects chemical shifts of atomic nuclei with
non-zero spin, shifts due to electronic
environment nearby - determine distances between specific pairs of
atoms based on shifts, constraints - use constraints and biochemical knowledge of the
protein to determine an ensemble of models
determining constraints
using constraints to determine secondary structure
6Biology/Chemistry of Protein Structure
Assembly Folding Packing Interaction
- Primary
- Secondary
- Tertiary
- Quaternary
P R O C E S S
S T R U C T U R E
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8Protein Assembly
9Amino Acids
10Forces driving protein folding
- It is believed that hydrophobic collapse is a key
driving force for protein folding - Hydrophobic core
- Polar surface interacting with solvent
- Minimum volume (no cavities) Van der Walls
- Disulfide bond formation stabilizes
- Hydrogen bonds
- Polar and electrostatic interactions
11PROTEIN FOLDING PROBLEM
- STARTING FROM AMINO ACID SEQUENCE FINDING THE
STRUCTURE OF PROTEINS IS CALLED THE PROTEIN
FOLDING PROBLEM
12Secondary Structure
- non-linear
- 3 dimensional
- localized to regions of an amino acid chain
- formed and stabilized by hydrogen bonding,
electrostatic and van der Waals interactions
13The a-helix
14Ramachandran Plot
- Pauling built models based on the following
principles, codified by Ramachandran - bond lengths and angles should be similar to
those found in individual amino acids and small
peptides - (2) peptide bond should be planer
- (3) overlaps not permitted, pairs of atoms no
closer than sum of their covalent radii - (4) stabilization have sterics that permit
hydrogen bonding - Two degrees of freedom
- ? (phi) angle rotation about N C?
- ? (psi) angle rotation about C? C
- A linear amino acid polymer with some folds is
better but still not functional nor completely
energetically favorable ? packing!
15Chou-Fasman Parameters
16HOMOLOGY MODELLING
- Using database search algorithms find the
sequence with known structure that best matches
the query sequence - Assign the structure of the core regions obtained
from the structure database to the query
sequence - Find the structure of the intervening loops using
loop closure algorithms
17Homology Modeling How it works
- Find template
- Align target sequence
- with template
- Generate model
- - add loops
- - add sidechains
- Refine model
181esr
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21TURALIGN Constrained Structural Alignment Tool
For Structure Prediction
22Motif Alignment Using Dynamic Algorithm
Template
Target
Template
Target
23RESULTS
- For all the experiments done, our algorithm
perfectly matched functional sites and motifs
given as input to the program. - 1csh vs 1iomA
- RMSD 2.50
- 1csh vs 1k3pA
- RMSD 2.12
- 1k3pA vs 1iomA
- RMSD 3.03
- 1b6a vs 1xgsA
- RMSD 2.23
- 1fp2A vs 1fp1D
- RMSD 2.98
- At average we got the best results for 5
experiments - RMSD 2.57 with ac0.4,sc0.4,tc0.2,cc0
24Thanks to
25 Why Functional Classification?
- Huge amount of data accumulated via genome
sequencing projects. ? - Costly experimental structure prediction methods
(X-ray NMR), takes months/year. ? - Also computational structure prediction methods
are not accurate enough.
26 G-protein coupled receptors (GPCRs)
- Vital protein bundles with versatile functions.
- Play a key role in cellular signaling, regulation
of basic physiological processes by interacting
with more than 50 of prescription drugs. - Therefore excellent potential therapeutic target
for drug design and the focus of current
pharmaceutical research.
27GPCR Functional Classification Problem
- Although thousands of GPCR sequences are known,
the crystal structure solved only for one GPCR
sequence at medium resolution to date.
- For many of them, the activating ligand is
unknown. - Functional classification methods for automated
characterization of such GPCRs is imperative.
28Relationship between specific binding of GPCRs
into their ligands and their functional
classification
- Subfamily classifications in GPCRDB are defined
according to which ligands the receptor binds
(based on chemical interactions rather than
sequence homology).
- According to the binding of
GPCRs
with different ligand
types,
GPCRs are classified
into
at least six families. - The correlation between sub-family classification
and the specific binding of GPCRs to their
ligands can be computationally explored for Level
2 subfamily classification of Amine Level 1
subfamily.
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30Benchmark Dataset
- Dataset
- 352 amines, 595 peptides, 1898 olfactory, 355
rhodopsin, 56 prostanoid - Derive GPCR proteins from GPCRDB SWISS-PROT
through internet - Group the proteins according to their ligand
specificity (i.e amines, peptides, olfactory,
rhodopsin, prostanoid) - Seperate proteins into train and test groups with
21 ratio respectively - Derive the ecto-domains by using TMHMM (i.e
n-terminal, loop1, loop2, loop3) - Rewrite the sequences using 11 letter alphabets
31Classification of Amino acids
Class Amino Acids Class Amino Acids
a I,V,L,M g G
b R,K,H h W
c D,E i C
d Q,N j Y,F
e S,T k P
f A
32Snake plot of the human beta-2 adrenoceptor
33PROTEIN DATABASE
Train proteins Ligand group amines
ID NAME Sequence n-term Loop1 ...
1 5H1A_RAT MDVFSF... acajejgdgd... jdaadbhe... ...
2 5H1A_FUGRU MDLRATS... bekccbec... aakjiceeiba.. ...
3 5H1A_HUMAN MDVLSPG... bdfbfcccaa... aibcfihjbaf... ...
4 5H1B_PANTR MEEPGAQ.. acckgfdifk kaibcfihj ...
5 5H1B_RABIT MEEPGAQ.. acckgfdifkka... ibcfihjbd ...
6 5H1B_SPAEH MEEPGAR... acjadeecd bcaaad...
... ... ... ... ... ...
34FINDING MOST COMMON PATTERNS FOR EACH LIGAND GROUP
- Form triplets for n-terminal, loop1, loop2 and
loop3 seperately - For 11 letter alphabet 1331 different triplets
- For each triplet find proteins in certain ligand
group those containing the current triplet at a
given location and keep the data in vectors - Find the ratio of occurence of each triplet in a
given GPCR protein type(i.e amines) in a given
location (i.e loop1) - Insert the triplets into SQL database with their
ratios - Sort the triplets according to their ratios
35VECTORS
ID WORD PROTEINS
1 aaa 5H1A_RAT, 5H1A_FUGRU, ...
2 aab 5HT1_APLCA, 5HTA_DROME, ...
3 aac 5HT1_APLCA, 5HTA_DROME, 5H1A_PONPY
4 aad none
... ... ...
1328 kkh 5H1B_FUGRU , 5HTA_DROME...
1329 kki none
1330 kkj 5H1F_RAT
1331 kkk none
36FINDING DISTINGUISHING MOTIFS I
- Compare the ratios of triplets of a certain
ligand group with the occurence of this triplet
with the other ligand groups one by one(aaa in
amines 0.5 in peptides 0.1 r 0.5/0.1 - Keep the motifs with n(150) highest rs for each
ligand group pairs. These are the motifs that
distinguish given group from the other groups
37RESULTS
- Success rates for Information theory
38CART RESULTS
The classification table showing the only
patterns determining amines from all others
39- Index Triplet Family
- 1 CAA Amine
- 2 AIB Amine
- 3 HIJ Prostanoid
- 4 AEA Hormone-protein
- 5 JAA Hormone-protein
- 6 AAD TRH
- 7 ADA TRH
- 8 JCK Melatonin
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42i.e. Variable importance of the amine determining
patterns
Patterns Relative Importance
Loop 1 caa 100
Loop 1 gbh 97.46
Loop 3 iak 83.767
Loop 1 gjh 64.62
Loop 1 gda 51.101
Loop 2 aed 44.942
Loop 1 agj 43.636
Loop 1 aag 31.099
Loop 1 dca 22.736
Loop 3 akc 17.737
Loop 1 hjj 16.511
N-term afa 12.811
N-term eea 0
43Occurence of EIG in Loop2 in Rhodopsin Family
44Triplet JJI at exo-loop 2 in olfactory sub-family.
45 Conclusion
- Exploiting the fact that there is a
non-promiscuous relationship between the specific
binding of GPCRs into their ligands and their
functional classification, our method classifies
Level 1 subfamilies of GPCRs with a high
predictive accuracy of 98. - The presented machine learning approach, bridges
the gulf between the excess amount of GPCR
sequence data and their poor functional
characterization. - The method also finds binding motifs of GPCRs to
their specific ligands which can be exploited for
drug design to block these site - With such an accurate and automated GPCR
classification method, we are hoping to
accelerate the pace of identifying proper GPCRs
and their ligand binding scheme to facilitate
drug discovery especially for neurological
diseases.
46- Ligand binding motifs and their site information
can be used as contraints to build better models. - Highly conserved sites from alignment of GPCR
families can also be used as constraints
47Thanks to
48Class A Rhodopsin like
- The largest and most diverse family of GPCRs
- Conserved sequence motifs
- Unique signal-transduction activities
- Important members
- Adrenergic Receptors
- Adenosine Receptors
- Chemokine Receptors
- Dopamine Receptors
- Histamine Receptors
- Opsins
49Highlighted 4 GPCRs for Structure Comparison
Species GPCR Ligand
human ß2AR (Adrenergic) inverse agonists carazolol
avian ß1AR (Adrenergic) antagonist cyanopindolol
human A2A (Adenosine) antagonist ZM241385
bovine Rhodopsin inverse agonist 11-cis retinal
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51Extracellular surfaces
- The most significant structural divergences lie
in the extracellular loops and ligand-binding
region
ß2AR/ß1AR - contain a short a-helix that is stabilized by intra- and inter-loop disulphide bonds - N-terminal regions are disordered
A2A - lacks a predominant secondary structure and expose the ligand-binding cavity to extracellular bulk solvent
rhodopsin forms a short ß-sheet that caps the ligand and shielding the chromophore from bulk solvent and preventing Schiff base hydrolysis amino terminus glycosylated
52Ligand-Binding Pockets
- For both adrenergic receptors and rhodopsin,
ligand binding is mediated by polar and
hydrophobic contact residues from TM3, TM5, TM6
and TM7. - Ligand superpositions are partly overlapping for
ß2AR, ß1AR and rhodopsin, however, for ß2AR and
ß1AR are slightly more extracellular than
rhodopsin. - This difference results in a significant in key
rotamer conformational transitions in GPCR
activation
53Ligand-Binding Pockets
- In contrast to the ß2AR, ß1AR and rhodopsin, the
ligand of A2A ( Adenosin) receptor binds in a
mode that is roughly perpendicular to the bilayer
plane, and the packing interactions with the
protein, mostly with TM6 and TM7.
54Ligand-Binding Pockets
- Despite the highly conserved seven transmembrane
architecture, GPCRs can support a wide variety of
ligand-binding modes - Also high conservation in the ligand-binding
pocket is observed as well as in other
subfamilies of GPCRs - probably explains some of the difficulty in
obtaining potent subtype-selective compounds in
pharmaceutical discovery programs
55Cytoplasmic surfaces of the GPCR structures
- Major structural difference between the
ligand-activated GPCRs and rhodopsin lies in the
ionic lock between the highly conserved E/DRY
motif on TM3 and a glutamate residue on TM6. - Conserved among all family A GPCRs, these amino
acids form a network of polar interactions that
bridges the two transmembrane helices,
stabilizing the inactive-state conformation.
56Cytoplasmic surfaces of the GPCR structures
- One common feature is the chemical environment
surrounding residues of the highly conserved
NPXXY motif. The cytoplasmic end of TM7, in which
this motif is located, participates in key
conformational changes associated with GPCR
activation. - The proline in this motif causes a distortion in
the a-helical structure, and the tyrosine faces
into a pocket lined by TM2, TM3, TM6 and TM7.
57Mechanism for Activation
- Structures of opsin provide clues to the
transmembrane helix rearrangements that can be
expected as a result of agonist binding - Most importantly, the side chain of Trp 265 (the
toggle switch) moves into space previously
occupied by the ionone ring of retinal - The cytoplasmic end of TM6 is shifted more than 6
Å outwards from the centre of the bundle
58Snake plot of the human beta-2 adrenoceptor