Functional 3-D modelling of G protein coupled receptors

Uğur Sezerman

Central Dogma

Transcription

Translation

DNA

PROTEINS

mRNA

Motivation

• Knowing the structure of molecules enables us to understand its mechanism of function

• Current experimental techniques– X-ray cystallography– NMR

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.

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

Biology/Chemistry of Protein Structure

Primary

Secondary

Tertiary

Quaternary

Assembly

Folding

Packing

InteractionS T

R U

C T

U R

EP

R O

C E

S S

Protein Assembly

• occurs at the ribosome • involves dehydration

synthesis and polymerization of amino acids attached to tRNA:

NH - {A + B A-B + H O} -COO

• yields primary structure

2 n3+ -

Amino Acids

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

PROTEIN FOLDING PROBLEM• STARTING FROM AMINO ACID SEQUENCE

FINDING THE STRUCTURE OF PROTEINS IS CALLED THE PROTEIN FOLDING PROBLEM

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

The -helix

Ramachandran Plot• Pauling built models based on the following

principles, codified by Ramachandran:

(1) 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:(1) (phi) angle = rotation about N – C(2) (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!

Chou-Fasman ParametersName Abbrv P(a) P(b) P(turn) f(i) f(i+1) f(i+2) f(i+3)Alanine A 142 83 66 0.06 0.076 0.035 0.058Arginine R 98 93 95 0.07 0.106 0.099 0.085Aspartic Acid D 101 54 146 0.147 0.11 0.179 0.081Asparagine N 67 89 156 0.161 0.083 0.191 0.091Cysteine C 70 119 119 0.149 0.05 0.117 0.128Glutamic Acid E 151 37 74 0.056 0.06 0.077 0.064Glutamine Q 111 110 98 0.074 0.098 0.037 0.098Glycine G 57 75 156 0.102 0.085 0.19 0.152Histidine H 100 87 95 0.14 0.047 0.093 0.054Isoleucine I 108 160 47 0.043 0.034 0.013 0.056Leucine L 121 130 59 0.061 0.025 0.036 0.07Lysine K 114 74 101 0.055 0.115 0.072 0.095Methionine M 145 105 60 0.068 0.082 0.014 0.055Phenylalanine F 113 138 60 0.059 0.041 0.065 0.065Proline P 57 55 152 0.102 0.301 0.034 0.068Serine S 77 75 143 0.12 0.139 0.125 0.106Threonine T 83 119 96 0.086 0.108 0.065 0.079Tryptophan W 108 137 96 0.077 0.013 0.064 0.167Tyrosine Y 69 147 114 0.082 0.065 0.114 0.125Valine V 106 170 50 0.062 0.048 0.028 0.053

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

Homology Modeling: How it works

o Find template

o Align target sequence with template

o Generate model:- add loops- add

sidechains

o Refine model

1esr

TURALIGN: Constrained Structural Alignment Tool For

Structure Prediction

Motif Alignment Using Dynamic Algorithm

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 ac:0.4,sc:0.4,tc:0.2,cc:0

Thanks to

• Tural Aksel

Why Why Functional Classification?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.

G-protein coupled receptors G-protein coupled receptors (GPCRs)(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.

GPCR Functional Classification GPCR Functional Classification ProblemProblem

• 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.

Relationship between specific binding Relationship between specific binding of GPCRs into their ligands and their of GPCRs into their ligands and their

functional classificationfunctional classification

• 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.

• Subfamily classifications in GPCRDB are defined according to which ligands the receptor binds (based on chemical interactions rather than sequence homology).

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 2:1

ratio respectively– Derive the ecto-domains by using TMHMM (i.e n-

terminal, loop1, loop2, loop3)– Rewrite the sequences using 11 letter alphabets

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

Snake plot of the human beta-2 adrenoceptor

PROTEIN DATABASE

ID NAME Sequence n-term Loop1 ...

1 5H1A_RAT MDVFSF... acajejgdgd... jdaadbhe... ...

2 5H1A_FUGRU MDLRATS... bekccbec... aakjiceeiba.. ...

3 5H1A_HUMANMDVLSPG...

bdfbfcccaa... aibcfihjbaf... ...

4 5H1B_PANTR MEEPGAQ.. acckgfdifk kaibcfihj ...

5 5H1B_RABIT MEEPGAQ..acckgfdifkka...

ibcfihjbd ...

6 5H1B_SPAEH MEEPGAR...

acjadeecd bcaaad...

... ... ... ... ... ...

Train proteins; Ligand group: amines

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

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

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 “r”s for each ligand group pairs. These are the motifs that distinguish given group from the other groups

RESULTS

• Success rates for Information theory

CART RESULTS

The classification table showing the only patterns determining amines from all others

• 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

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

Occurence of EIG in Loop2 in Rhodopsin Family

Triplet JJI at exo-loop 2 in olfactory sub-family.

ConclusionConclusion

• 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.

• 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

Thanks to

• Murat Can Çobanoğlu

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

Highlighted 4 GPCRs for Structure Comparison

Species GPCR Ligand

human β2AR (Adrenergic)

inverse agonists carazolol

avian β1AR (Adrenergic)

antagonist cyanopindolol

human A2A (Adenosine) antagonist ZM241385

bovine Rhodopsin inverse agonist 11-cis retinal

Extracellular surfaces• The most significant structural divergences lie in the extracellular

loops and ligand-binding region

β2AR/β1AR

- contain a short α-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 β-sheet that caps the ligand and shielding the chromophore from bulk solvent and preventing Schiff base hydrolysis- amino terminus glycosylated

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 β2AR, β1AR and rhodopsin, however, for β2AR and β1AR are slightly more extracellular than rhodopsin.

• This difference results in a significant in key rotamer conformational transitions in GPCR activation

Ligand-Binding Pockets• In contrast to the β2AR, β1AR

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.

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

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.

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 α-helical structure, and the tyrosine faces into a pocket lined by TM2, TM3, TM6 and TM7.

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

Snake plot of the human beta-2 adrenoceptor