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Functional 3-D modelling of G protein coupled receptors

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Title: PREDICTION of LIGAND-GPCR BINDING PATTERNS Author: bekir Last modified by: U ur Sezerman Created Date: 3/23/2006 10:06:27 AM Document presentation format – PowerPoint PPT presentation

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Title: Functional 3-D modelling of G protein coupled receptors


1
Functional 3-D modelling of G protein coupled
receptors
Ugur Sezerman
2
Central Dogma
3
Motivation
  • Knowing the structure of molecules enables us to
    understand its mechanism of function
  • Current experimental techniques
  • X-ray cystallography
  • NMR

4
X-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.
5
NMR 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
6
Biology/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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8
Protein Assembly
9
Amino Acids
10
Forces 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

11
PROTEIN FOLDING PROBLEM
  • STARTING FROM AMINO ACID SEQUENCE FINDING THE
    STRUCTURE OF PROTEINS IS CALLED THE PROTEIN
    FOLDING PROBLEM

12
Secondary 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

13
The a-helix
14
Ramachandran 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!

15
Chou-Fasman Parameters
16
HOMOLOGY 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

17
Homology Modeling How it works
  • Find template
  • Align target sequence
  • with template
  • Generate model
  • - add loops
  • - add sidechains
  • Refine model

18
1esr
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21
TURALIGN Constrained Structural Alignment Tool
For Structure Prediction
22
Motif Alignment Using Dynamic Algorithm
Template
Target
Template
Target
23
RESULTS
  • 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

24
Thanks to
  • Tural Aksel

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.

27
GPCR 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.

28
Relationship 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.

29
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30
Benchmark 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

31
Classification 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
32
Snake plot of the human beta-2 adrenoceptor
33
PROTEIN 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...
... ... ... ... ... ...
34
FINDING 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

35
VECTORS
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
36
FINDING 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

37
RESULTS
  • Success rates for Information theory

38
CART 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

40
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42
i.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
43
Occurence of EIG in Loop2 in Rhodopsin Family
44
Triplet 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

47
Thanks to
  • Murat Can Çobanoglu

48
Class 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

49
Highlighted 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
50
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51
Extracellular 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
52
Ligand-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

53
Ligand-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.

54
Ligand-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

55
Cytoplasmic 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.

56
Cytoplasmic 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.

57
Mechanism 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

58
Snake plot of the human beta-2 adrenoceptor
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