1. Bioinformatics and molecular modelling studies of membrane proteins Shiva Amiri Professor Mark S.P. Sansom June 1, 2004

2.  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 Membrane proteins Nelson, M. Comparative Neurophysiology, 2000.

3.  function is dependent upon the binding of a ligand.  examples of LGICs: nAChR, GABA A and GABA C receptors, 5HT 3 receptor, Glycine receptor sdf Ligand gated ion channels (LGICs) Sperelakis, N., Cell Physiology Source Book

4.  problem: difficult to obtain high resolution crystallographic images of membrane proteins Unwin et.al, Nature, 26 June 2003  some success using cryo-electron microscopy coupled with Fourier Transforms, i.e. Unwin’s 4Å image of the TM region.  but still no full structure of any LGIC Structure prediction Unwin et.al, Nature, 26 June 2003

5.  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 α-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 My project

6. α-7 nAChR  some properties – - cationic channel - homopentamer - four transmembrane regions (M1-M4) M2 M3 M4 M1 LB TM why nAChR?  mutations in genes coding for nAChR can result in Parkinson’s disease, Alzheimer’s disease, myasthenia gravis, frontal lope epilepsy, etc.  plays a role in nicotine addiction

7. The process … homology modelling - Modeller, Procheck 2 PDBs {θ max, z max } ZAlign termini distancesbad contacts( Unwin distances ) analysis – xfarbe plots make model using chosen { θ, z} procheck GROMACS energy minimization motion analysis: GNM CONCOORD electrostatics (Kaihsu Tai) pore dimensions - HOLE homology models of other LGICs

8. transmembrane domain alignment Homology modeling – transmembrane domain

9.  the homology model of the TM region with the Torpedo marmorata structure (PDB: 1OED - 4 Å) and the chick α-7 sequence using MODELLER M1 M3 M2 M4 Homology modelling – transmembrane domain

10. ligand binding domain alignment Homology modelling – ligand binding domain

11.  the homology model of the LB domain with acetylcholine binding protein (AChBP) as the structure (PDB: 1I9B – 2.7 Å) and the chick α-7 sequence using MODELLER Homology modelling – ligand binding domain α α α α α

12.  combining the transmembrane domain with the ligand binding domain  producing data upon rotations and translations to allow the user to choose an optimal model The software

13. 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 z x y

14. 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 Scoring criteria

15. termini distance – distance between the N-terminus of the LB domain and the C-terminus of the TM domain ARG 205 THR 206 Scoring criteria continued …

16. bad contacts – number of residues that are closer than a cut-off distance. LB TM LB TM Scoring criteria continued …

17. termini distance z translation (Å) theta (radians) bad contacts Unwin distance z translation (Å) theta (radians) Plots of scoring criteria

18. termini + bad contacts theta (radians) z translation (Å) Linear combinations of scoring criteria termini + Unwin theta (radians) termini + bad contacts + Unwin z translation (Å) x chosen {θ, z}

19.  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 Choosing the best model

20. Putting ACRB together – test case

21. Plots for ACRB alignment bad contacts theta (radians) z translation (Å) x termini termini + bad contacts

22.  Gaussian network model (GNM)  CONCOORD Course grain methods of motion analysis

23.  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 Gaussian network model (GNM)

24. AChBP – theoretical vs. experimental B-values experimental theoretical

25. Theoretical B-values of the model

26.  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 GNM results ligand binding site toxin binding sites nAChR model coloured by generated B-values

27.  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 CONCOORD’s output CONCOORD

28.  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) Eigenvector plot - LB

29.  the spikes show greater freedom of motion for the outer helices  the spikes are pointed either down or up, no uniform direction Eigenvector plot - TM

30.  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 Eigenvector plot – nAChR model

31.  first eigenvector shows twisting motion of receptor  opening and closing of the pore as the subunits rotate First eigenvector

32.  GABA and glycine receptors (anion selective channel) - structure being used is the current model for the α-7 nAChR  Simulations on TM region of model and other LGICs – Oliver Beckstein - looking at the M2 helix and its relevant motions Homology models of other LGICs M2s of α-7 nAChR

33.  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  modelling methods for LGICs  predicted structure of α -7 nAChR  used various methods (GNM, CONCOORD) to look at motions of the predicted structure of α -7 nAChR  models of anionic LGICs (GABA and glycine) using current α-7 nAChR structure Summary Future work

34.  ACRB + TolC Aligning other membrane proteins

35. 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 D’Rozario Dr. Syma Khalid John Holyoake Dr. Bing Wu Tony Ivetac George Patargias Sylvanna Ho Samantha Kaye Thanks to:

37.  covariance line plots indicate which parts of the protein are correlated or move together Covariance line plot – nAChR model

38. Principal component analysis Loredana Vaccaro Used to reduce the dimensionality of a data set for a 3N dimensional data set covariance matrix diagonalisation 3N eigenvectors (orthogonal = independent of each other) eigenvalues (contribution of each eigenvector to the whole motion) keep the first eigenvectors reduced data set C ij = t )(x j,t - t )> t identify the major motions of the protein

39. Hydrophobic girdle M2 alignment