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Molecular Modelling of the Nicotinic Acetylcholine Receptor and Related Proteins Shiva Amiri Professor Mark S. P. Sansom and Dr. Philip C. Biggin D. Phil.

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Presentation on theme: "Molecular Modelling of the Nicotinic Acetylcholine Receptor and Related Proteins Shiva Amiri Professor Mark S. P. Sansom and Dr. Philip C. Biggin D. Phil."— Presentation transcript:

1 Molecular Modelling of the Nicotinic Acetylcholine Receptor and Related Proteins Shiva Amiri Professor Mark S. P. Sansom and Dr. Philip C. Biggin D. Phil. Symposium, October 6, 2005

2 The Nicotinic Acetylcholine Receptor (nAChR)  a ligand gated ion channel (LGIC) found in central and peripheral nervous system  endogenous ligand is acetylcholine (ACh) but reactive to many compounds such as nicotine, alcohol, and toxins  mutations lead to various diseases such as epilepsy, myasthenic syndromes, etc.  implicated in Alzheimer’s disease and Parkinson’s disease (not well understood)  mediates nicotine addiction Ligand binding domain (LB)  core of 10 β-strands, forming a β-sandwich  an N-terminal α-helix, two short 3 10 helices Transmembrane domain (TM)  4 α-helices in each subunit (M1-M4) Intracellular domain (IC)  α-helical, some residues still missing Unwin, Journal of Molecular Biology, March, 2005

3 Computational methods to study membrane proteins  There are very few crystal structures available for membrane proteins - can build structures and use them to perform a range of studies such as electrostatics, pore profiling, ligand docking, Molecular Dynamics simulations etc.  Studying the movement of proteins to gain insight into their function - various methods of using a structure to look at the dynamics of the protein  Docking of ligands onto receptors - drug design

4 Scoring criteria chosen {θ, z} theta (radians) z translation (Å) x Possible gate region Amiri et al., Mol. Mem. Biol, 2005 Generating Structures

5 Motion Analysis Atomistic Molecular Dynamics Simulations with GROMACS Coarse-grain Grouping of atoms Gaussian Network Model (GNM) Assessing the flexibility of structures depending on the number of neighbouring residues CONCOORD Generating random structures from a given structure within distant constraints In-house methods Grouping Eigenvectors to simplify MD data Ligand Docking Docking Nicotine and other ligands onto various frames of trajectories Water Looking at the behaviour of water in the binding pocket

6 Coarse-grain methods (1) ligand binding site Gaussian Network Model (GNM)  A coarse-grained method to approximate fluctuations of residues based on the number of neighbours within a cut-off radius  Information on the flexibility of the receptor, may outline functionally important regions of the protein

7 1 332 664 996 1328 1660 Residue number Covariance matrix showing which part of the protein moves together. The red parts show highest covariance and the blue indicates negative covariance (move in opposite directions) CONCOORD  Generates n number of structures that meet distance constraints  very quick: one run takes a few minutes  Output used in Principle Component Analysis (PCA) to describe major modes of motion Coarse-grain methods (2) Porcupine plot showing the nAChR’s two domains rotating in opposite directions. Suggests motions that could be involved in the gating mechanism http://s12-ap550.biop.ox.ac.uk:8078/dynamite_html/index.html http://s12-ap550.biop.ox.ac.uk:8078/dynamite_html/index.html (Barrett et al., 2004)

8 Molecular Dynamics Simulations  A method to study conformational changes at an atomistic level  MD of ligand binding region of AChBP (nAChR homologue)  several simulations are being carried out for nAChR: i) non-liganded simulations ii) with various ligands: nicotine, carbamylcholine, HEPES  One nanosecond takes ~ 5 days for this system  Actual gating of this receptor happens on a millisecond time-scale Covariance lines show which sections of the receptor are moving together http://s12-ap550.biop.ox.ac.uk:8078/dynamite_html/index.htmlhttp://s12-ap550.biop.ox.ac.uk:8078/dynamite_html/index.html (Barrett et al., 2004)

9 Molecular Dynamics Simulations continued … A simulation of AChBP (nAChR ligand binding domain homologue) with Nicotine in all 5 binding pockets.

10 MET 115 TRP 144 LEU 103 THR 145 CYS 188 CYS 189 The important residues in the binding pocket are shown. These residues have been shown to interact with the ligand. Two subunits of the nAChR are shown with Nicotine inside the binding pocket The Binding Pocket  Studying structural changes which occur in the binding pocket to better understand how binding of a ligand results in the functioning of the ion channel  looking at distances, dihedrals of surrounding residues, and the behaviour of water in the binding pocket

11 Water in the Binding Pocket  Bridging waters form hydrogen bonds between the ligand and surrounding residues (shown using Ligplot)  Water seems to play an important role in ligand binding. There are various zones in the binding pocket where waters are frequently present Zone 2 Zone 1 Zone 4 Zone 3 Zone 5

12 Water molecules which remain in their position in the Binding Pocket with Nicotine bound Water in the Binding Pocket

13 Docking  Docking various ligands such as nicotine, acetylcholine, imidacloprid (an insecticide) onto AChBP and the α7 nAChR to look at possible binding modes  An automated docking program docks ligands onto hundreds of frames from a trajectory NicotineHEPESCarbamylcholine Nicotine docked onto the AChBP binding site

14 Results  Structure Generation: Determined the structure of the α7 nAChR and several related proteins such as GABA A, 5HT 3, and Glycine receptor and used the models for various structural studies  Molecular Dynamics: i. Molecular dynamics studies of a homologue of the ligand binding (LB) domain of nAChR with Nicotine, Carbamylcholine, and HEPES ii. Studying the role of water in the binding pocket iii. MD of α7 mutants  Coarse-graining: i. GNM ii. CONCOORD iii. Grouping of eigenvectors from MD trajectories  Automated Docking: Automated docking of ligands (Nicotine, acetylcholine, carbamylcholine, insecticides) onto AChBP and nAChR (and its mutants) along a trajectory

15 Thanks to… Prof. Mark S. P. Sansom Dr. Philip C. Biggin Dr. Alessandro Grottesi Dr. Kaihsu Tai Dr. Zara Sands Dr. Oliver Beckstein Dr. Daniele Bemporad Dr. Jorge Pikunic Dr. Andy Hung Dr. Shozeb Haider Dr. Syma Khalid Dr. Pete Bond Dr. Kia Balali-Mood Dr. Hiunji Kim Dr. Bing Wu Sundeep Deol Yalini Pathy Jonathan Cuthbertson Jennifer Johnston Katherine Cox Robert D’Rozario Jeff Campbell Loredana Vaccaro John Holyoake Tony Ivetac Samantha Kaye Sylvanna Ho Benjamin Hall Emi Psachoulia Chze Ling Wee

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17 Future Work  Further investigation of the role of water in the binding pocket  Analysis of simulations of α7 nAChR mutants and docking along their trajectories  Development of further methods for understanding the motion of proteins from the limited structural data available  Combining coarse-grained and MD data…. i.e. Running GNM on various frames of a trajectory

18 Grouping eigenvectors  simplifying Molecular dynamics data by grouping the eigenvectors from the resulting trajectory. Coarse-grain methods (3)

19 MET 115 TRP 144 LEU 103 THR 145 CYS 188 CYS 189 The Binding Pocket The important residues in the binding pocket are shown. These residues have been shown to interact with the ligand.  Studying structural changes which occur in the binding pocket to better understand how binding of a ligand results in the functioning of the ion channel  looking at distances, dihedrals of surrounding residues, and the behaviour of water in the binding pocket

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21 Using computational techniques to study the most flexible regions of the nAChR. These residues could play a key role in the gating of the receptor. Red shows the most flexibility, with blue showing least movement.


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