Bioinformatics and molecular modelling studies of membrane proteins

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Bioinformatics and molecular modelling studies
of membrane proteins

• Shiva Amiri
• Professor Mark S.P. Sansom
• June 1, 2004

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• constitute approximately 25 of the genome
• important drug targets
• - nerve and muscle excitation
• - hormonal secretion
• - sensory transduction
• - control of salt and water balance etc.
• malfunctions result in various diseases

Nelson, M. Comparative Neurophysiology, 2000.
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• function is dependent upon the binding of a
  ligand.
• examples of LGICs nAChR, GABAA and GABAC
  receptors, 5HT3 receptor, Glycine receptor

Sperelakis, N., Cell Physiology Source Book
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problem difficult to obtain high resolution
crystallographic images of membrane proteins

• some success using cryo-electron microscopy
  coupled with Fourier Transforms, i.e. Unwins 4Å
  image of the TM region.
• but still no full structure of any LGIC

Unwin et.al, Nature, 26 June 2003
Unwin et.al, Nature, 26 June 2003
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• to take available structural data and put the
  pieces together
• main focus so far using available information
  to predict the structure
• and motions of the a-7 nicotinic
  acetylcholine receptor (nAChR)
• we have
• 4Å cryo-EM structure of AChR transmembrane
  domain
• 2.7Å crystal structure of ligand binding
  domain homolog
• task to combine the two domains
• the use of bioinformatics and simulation tools to
  study functionally
• relevant motions of LGICs

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• mutations in genes coding for nAChR can result
  in Parkinsons disease, Alzheimers disease,
  myasthenia gravis, frontal lope epilepsy, etc.
• plays a role in nicotine addiction

• some properties
• - cationic channel
• - homopentamer
• - four transmembrane regions
• (M1-M4)

LB
M3
M2
M4
M1
TM
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transmembrane domain alignment
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• the homology model of the TM region with the
  Torpedo marmorata structure
• (PDB 1OED - 4 Å) and the chick a-7 sequence
  using MODELLER

M2
M1
M3
M4
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ligand binding domain alignment
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• the homology model of the LB domain with
  acetylcholine binding protein (AChBP) as the
  structure (PDB 1I9B 2.7 Å) and the chick a-7
  sequence using MODELLER

a
a
a
a
a
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• combining the transmembrane domain with the
  ligand binding domain
• producing data upon rotations and translations to
  allow the user to choose an optimal model

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z
straighten and align each domain with respect to
the z-axis
rotate and translate about z-axis- angle of
rotation and steps of translations are
user-defined
y
x
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• Unwin distance distance between residues from
  the TM domain and the LB domain that are meant to
  come into close proximity

LYS 44
ASP 264
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• termini distance distance between the
  N-terminus of the LB domain and the C-terminus of
  the TM domain

ARG 205
THR 206
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• bad contacts number of residues that are
  closer than a cut-off distance.

LB
LB
TM
TM
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termini distance
Unwin distance
theta (radians)
theta (radians)
theta (radians)
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termini Unwin
termini bad contacts
theta (radians)
theta (radians)
chosen ?, z
x
theta (radians)
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• model chosen based on scoring criteria data
• once a good model was decided on, energy
  minimization using GROMACS was carried out to
  ensure the electrostatic legitimacy of the model
• - GROMACS joins the two domains at their
  termini
• - experimenting with how far can the domain be
  before GROMACS refuses to join them
• procheck is run to check the validity of the
  structure

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termini
bad contacts
theta (radians)
theta (radians)
termini bad contacts
x
theta (radians)
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• Gaussian network model (GNM)
• CONCOORD

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• a course-grained model to approximate
  fluctuations of residues
• Information on the flexibility and function of
  the protein
• produces theoretical B-values
• residues considered as balls and the distance
  between neighbouring residues are springs
• B-values generally in agreement with
  crystallographic data

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• some results were as expected, with more freedom
  of motion for the outer helices of the TM region
• identification of the ligand binding site and
  also of toxin binding sites

ligand binding site
toxin binding sites
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• generates protein conformations around a given
  structure based on distance constraints
• suggests plausible motions of the protein
• principal component analysis (PCA) is applied on
  the 500 resulting structures from CONCOORD
• available at dynamite.biop.ox.ac.uk/dynamite
  (Paul Barrett)
• - used to generate eigenvector (porcupine)
  plots and covariance line plots using CONCOORDs
  output

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• porcupine plots have an x number of spikes, each
  spike representing the element of the eigenvector
  associated with each c-alpha atom of the protein
• although this is a homo-pentamer, there is
  asymmetry between the subunits (closed state)

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• the spikes show greater freedom of motion for
  the outer helices
• the spikes are pointed either down or up, no
  uniform direction

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• when combined, the spikes have a more organized
  pattern, with LB region spikes all rotating to
  one side and the TM spikes rotating in the
  opposite direction, suggesting a twisting motion
  of the receptor
• the middle of the structure is not as mobile

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• first eigenvector shows twisting motion of
  receptor
• opening and closing of the pore as the subunits
  rotate

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• GABA and glycine receptors (anion selective
  channel)
• - structure being used is the current model for
  the a-7 nAChR
• Simulations on TM region of model and other LGICs
  Oliver Beckstein
• - looking at the M2 helix and its relevant
  motions

M2s of a-7 nAChR
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• modelling methods for LGICs
• predicted structure of a-7 nAChR
• used various methods (GNM, CONCOORD) to look
  at motions of the predicted structure of a-7
  nAChR
• models of anionic LGICs (GABA and glycine)
  using current a-7 nAChR structure

• models of other LGICs
• motion analysis of other LGICs
• looking at the hydrophobic girdle (M2) of LGICs
  to study patterns of conservation and the
  behaviour of these residues during gating
• simulation studies of constructed models

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• ACRB TolC

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• Prof. Mark S.P. Sansom Oliver Beckstein
• Dr. Phil Biggin Sundeep Deol
• Dr. Kaihsu Tai Yalini Pathy
• Dr. Paul Barrett Jonathan Cuthbertson
• Dr. Alessandro Grotessi Pete Bond
• Dr. Andy Hung Katherine Cox
• Dr. Daniele Bemporad Jennifer Johnston
• Dr. Jorge Pikunic Jeff Campbell
• Dr. Shozeb Haider Loredana Vaccaro
• Dr. Zara Sands Robert DRozario
• Dr. Syma Khalid John Holyoake
• Dr. Bing Wu Tony Ivetac
• George Patargias Sylvanna Ho
• Samantha Kaye

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• covariance line plots indicate which parts of
  the protein are correlated or move together

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Principal component analysisLoredana Vaccaro
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Hydrophobic girdle
M2 alignment
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