Structure and Properties of Blue Copper Azurin Under Applied Compression : Simulation Studies Andrew Hung 1, Jian-wei Zhao 2, Jason J. Davis 2 and Mark.

Slides:



Advertisements
Similar presentations
TEMPLATE DESIGN © Statistical Coupling Analysis of the Photosystem II D1 Protein Janan Zhu 1 ; Nicholas Polizzi 2 ; 1.
Advertisements

Homework 2 (due We, Feb. 5): Reading: Van Holde, Chapter 1 Van Holde Chapter 3.1 to 3.3 Van Holde Chapter 2 (we’ll go through Chapters 1 and 3 first. 1.Van.
A New Analytical Method for Computing Solvent-Accessible Surface Area of Macromolecules.
Behaviour of velocities in protein folding events Aldo Rampioni, University of Groningen Leipzig, 17th May 2007.
Bioinf. Data Analysis & Tools Molecular Simulations & Sampling Techniques117 Jan 2006 Bioinformatics Data Analysis & Tools Molecular simulations & sampling.
Dynamical behaviour of water in nanopores by computer simulations Oliver Beckstein* and Mark S. P. Sansom Laboratory of Molecular Biophysics, Department.
Water layer Protein Layer Copper center: QM Layer Computing Redox Potentials of Type-1 Copper Sites Using Combined Quantum Mechanical/Molecular Mechanical.
Computer Simulation of Biomolecules and the Interpretation of NMR Measurements generates ensemble of molecular configurations all atomic quantities Problems.
Proteins in Bionanotechnology Computational Studies Andrew Hung, Oliver Beckstein, Robert D’Rozario, Sylvanna S.W. Ho and Mark S.P. Sansom Laboratory of.
Chap. 4. Problem 1. Part (a). Double and triple bonds are shorter and stronger than single bonds. Because the length of a peptide bond more closely resembles.
Molecular Dynamics Simulations of Compressional Metalloprotein Deformation Andrew Hung 1, Jianwei Zhao 2, Jason J. Davis 2, Mark S. P. Sansom 1 1 Department.
Insight into peptide folding role of solvent and hydrophobicity dynamics of conformational transitions.
2010 RCAS Annual Report Jung-Hsin Lin Division of Mechanics, Research Center for Applied Sciences Academia Sinica Dynamics of the molecular motor F 0 under.
 The generated models are used in various coarse-grain and other molecular modelling studies.  Coarse-grain analysis includes: Gaussian Network Models.
Molecular Dynamics Arjan van der Vaart PSF346 Center for Biological Physics Department of Chemistry and Biochemistry Arizona State.
Electron tunneling in structurally engineered proteins Photosynthesis, respiration, nitrogen fixation, drug metabolism, DNA synthesis, and immune response.
Protein backbone Biochemical view:
Volume 11, Issue 8, Pages (August 2003)
Determination of Free Energy Landscapes via Computer Simulations and its Application to the DNA i-motif Vasileios A. Tatsis, Raghvendra P. Singh.
Voltage-Dependent Hydration and Conduction Properties of the Hydrophobic Pore of the Mechanosensitive Channel of Small Conductance  Steven A. Spronk,
Analysis and Evaluation of Channel Models: Simulations of Alamethicin
Constraints Imposed by the Membrane Selectively Guide the Alternating Access Dynamics of the Glutamate Transporter GltPh  Timothy R. Lezon, Ivet Bahar 
Maik Goette, Martin C. Stumpe, Ralf Ficner, Helmut Grubmüller 
Conformational Change in an MFS Protein: MD Simulations of LacY
Steered Molecular Dynamics Studies of Titin I1 Domain Unfolding
Richard J. Law, Keith Munson, George Sachs, Felice C. Lightstone 
Volume 124, Issue 1, Pages (January 2006)
Giovanni Settanni, Antonino Cattaneo, Paolo Carloni 
Volume 11, Issue 8, Pages (August 2003)
Yeast RNA Polymerase II at 5 Å Resolution
Volume 8, Issue 2, Pages (August 2001)
UG Wagner, M Hasslacher, H Griengl, H Schwab, C Kratky  Structure 
The Mechanism of the Translocation Step in DNA Replication by DNA Polymerase I: A Computer Simulation Analysis  Andrei A. Golosov, Joshua J. Warren, Lorena.
Volume 13, Issue 4, Pages (February 2004)
Shozeb Haider, Gary N. Parkinson, Stephen Neidle  Biophysical Journal 
Volume 130, Issue 6, Pages (September 2007)
Large-Scale Conformational Dynamics of the HIV-1 Integrase Core Domain and Its Catalytic Loop Mutants  Matthew C. Lee, Jinxia Deng, James M. Briggs, Yong.
Liqun Zhang, Susmita Borthakur, Matthias Buck  Biophysical Journal 
Michel A. Cuendet, Olivier Michielin  Biophysical Journal 
Volume 13, Issue 1, Pages (January 2005)
How Does a Voltage Sensor Interact with a Lipid Bilayer
Volume 107, Issue 3, Pages (August 2014)
Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach
Analysis and Evaluation of Channel Models: Simulations of Alamethicin
G. Fiorin, A. Pastore, P. Carloni, M. Parrinello  Biophysical Journal 
Dániel Szöllősi, Gergely Szakács, Peter Chiba, Thomas Stockner 
Discovery Through the Computational Microscope
Volume 124, Issue 5, Pages (March 2006)
Volume 96, Issue 7, Pages (April 2009)
Validating Solution Ensembles from Molecular Dynamics Simulation by Wide-Angle X- ray Scattering Data  Po-chia Chen, Jochen S. Hub  Biophysical Journal 
Sequential Unfolding of Individual Helices of Bacterioopsin Observed in Molecular Dynamics Simulations of Extraction from the Purple Membrane  Michele.
Marcos Sotomayor, Klaus Schulten  Biophysical Journal 
Volume 108, Issue 10, Pages (May 2015)
Dissecting DNA-Histone Interactions in the Nucleosome by Molecular Dynamics Simulations of DNA Unwrapping  Ramona Ettig, Nick Kepper, Rene Stehr, Gero.
Absence of Ion-Binding Affinity in the Putatively Inactivated Low-[K+] Structure of the KcsA Potassium Channel  Céline Boiteux, Simon Bernèche  Structure 
Velocity-Dependent Mechanical Unfolding of Bacteriorhodopsin Is Governed by a Dynamic Interaction Network  Christian Kappel, Helmut Grubmüller  Biophysical.
Kristen E. Norman, Hugh Nymeyer  Biophysical Journal 
Volume 85, Issue 5, Pages (May 1996)
Karina Kubiak, Wieslaw Nowak  Biophysical Journal 
In Search of the Hair-Cell Gating Spring
OmpT: Molecular Dynamics Simulations of an Outer Membrane Enzyme
Solution Structure of the Proapoptotic Molecule BID
Mijo Simunovic, Gregory A. Voth  Biophysical Journal 
Structure of an IκBα/NF-κB Complex
Volume 78, Issue 6, Pages (June 2000)
Tertiary structure of an immunoglobulin-like domain from the giant muscle protein titin: a new member of the I set  Mark Pfuhl, Annalisa Pastore  Structure 
Volume 98, Issue 4, Pages (February 2010)
Distribution of Halothane in a Dipalmitoylphosphatidylcholine Bilayer from Molecular Dynamics Calculations  Laure Koubi, Mounir Tarek, Michael L. Klein,
Volume 15, Issue 6, Pages (September 2004)
Molecular Dynamics Simulation of a Synthetic Ion Channel
Presentation transcript:

Structure and Properties of Blue Copper Azurin Under Applied Compression : Simulation Studies Andrew Hung 1, Jian-wei Zhao 2, Jason J. Davis 2 and Mark S.P. Sansom 1 1 Department of Biochemistry, University of Oxford, OX1 3QU, United Kingdom 2 Department of Chemistry, University of Oxford, OX1 3QU, United Kingdom Introduction Azurin (PDBID : 4AZU [1]) is a member of the blue-copper protein family, and plays a crucial role in electron transfer (ET) in biological redox systems. It is a globular protein, with its core comprising of eight (8) antiparallel β-strands, as well as a single inserted α-helix. It has been studied as a possible bioelectronic device [2] ; however, the ET mechanism is not understood. Recently, the influence of the structure of azurin on its electron tunneling properties have been studied using conductance- atomic force microscopy (C-AFM), in which a single azurin molecule is immobilised on a conducting AFM tip [3]. All experiments were carried out in ambient atmosphere, in which the protein is assumed to be lightly hydrated. In the current work, we perform molecular dynamics (MD) studies of the structural response of azurin to applied compression in order to determine the changes in protein structure and dynamics which may be responsible for the experimentally-observed behaviour. In Vacuo Compression Molecular Dynamics Simulations Hydrated-Protein Compression Figure 2. Cartoon of C-AFM set-up. Azurin is secured to the tip via Cys3 and Cys26 sulfur-Au covalent bonding [3]. The Cu center initially faces towards the graphite (HOPG) surface. Figure 3. Qualitative representation of tunneling barrier height wrt force Figure 1. Tube-and-arrows representation of azurin. The Cu center is liganded to 5 sidechains : His117, His45, Cys112, Met121, and carbonyl O of Gly45. Simulation parameters : All simulations performed using the code GROMACS [4], with the GROMOS96 forcefield. Electrostatics were treated with the particle-mesh Ewald (PME) method. Van der Waals cutoff radiis were set at 10 A. Bond restraints were placed between the Cu ion and the 5 coordinating ligands to preserve their X-ray structure bond lengths during all simulations. Structural Properties According to the number density model, biological electron tunneling efficacy is directly related to the atom number density in the protein medium connecting the electron donor and acceptor sites. Thus, determining the overall protein structure with respect to compression as well as its number density may help explain the C-AFM conduction behaviour. Essential Dynamics Analysis Concerted motions of atoms near the acceptor and donor sites could play a role in ET over large distances by enabling the protein to readily adopt a transition state configuration required for electron transfer [5]. For the C-AFM experiment, where the acceptor and donor sites are not localised, concerted motions across the whole protein may play a role in through-protein electron tunneling. Figure 4. Simulation set-up for protein compression in vacuo. Azurin was placed on top of a simple cubic slab of united-atom CH 4 molecules, with the protein aligned so that the Cys3 and Cys26 residues are placed at the top of the primary cell and the Cu site near the bottom. Stepwise reduction in primary z length results in effective protein compression. A MD trajectory was collected after each compression stage. Three stages of compression are shown : initial (left), at 27 Å (center) and 17 Å (right). Figure 5. Secondary structure wrt compression. Pink =  -helix, yellow = β-strands, green=turns, white = coils. The protein remains folded despite high compression, with helical and  -strand elements intact up to a compression distance of Å Further Work Aspects of the work currently being pursued include the continued compression of the hydrated protein system; development of more realistic tip and surface models (taking tip geometry into account); and quantum mechanical calculations of the azurin protein in various stages of compression to elucidate the mechanism of tunneling at the electronic level. Additionally, similar experimental and computational studies may be performed on other electron transport metalloproteins and compared with the results acquired for azurin in the current work. References [1] H. Nar et al., J. Mol. Biol. 218, p427 (1991) [2] R. Rinaldi et al., Adv. Mat. 14, p1453 (2002) [3] J. Zhao, J. J. Davis, Nanotechnology 14(9), p1023 (2003) [4] D. van der Soel et al., Gromacs User Manual version 3.1, Groningen, The Netherlands, Internet : (2002) [5] C. Arcangeli et al., Biophys. Chem. 78, p247 (1999) Figure 6. Protein-center number density wrt compression. There is an the initial, monotonous increase of atom density at the protein center with compression; a maximum plateau is reached at 20-30Å, corresponding to the range at which protein unfolding commences. This plateau may correspond to that observed experimentally. Essential Dynamics Analysis Figure 8. Hydrated azurin was generated by extracting the protein and 393 closest water molecules after a 1ns MD run of the fully solvated protein cell. In the initial configuration, an evenly-thick shell of water surrounds the protein surface. Water distribution around two faces of azurin after 3ns, with the protein represented as a solvent-accessible surface are shown. Note the absence of water at and around the hydrophobic residues (white patches; left). Effectively half of the protein surface is hydrated Figure 9. Simulation set-up for hydrated azurin system. Reduction of the z direction primary cell length results in protein compression. A vacuum space of 15 Å was placed between the upper and lower slab surfaces to prevent mirror-image interactions. MD trajectory (1ns) was collected at each compression stage. Shown are tip-surface distances of 42 Å (left), 30 Å (center) and 24 Å (right). Figure 7. Essential dynamics (ED) analyses were performed on trajectories at several compression stages. Overlay plots of the first eigenvector (showing the largest scale concerted motions) are shown. Fluctuations across the entire protein are reduced with compression. At high compression (right; 20 A), there is negligible concerted motion. This may play a role in lowering the efficiency of ET with applied force, and contribute to the plateau behaviour in barrier height observed experimentally. System Set-up Figure 12. In contrast to the in vacuo system, there is uneven reduction in concerted motions across the protein with compression. Initially, concerted motions occur across the protein (left). At 30 Å, fluctuations in regions of low hydration are suppressed, while regions of high hydration show significant fluctuation, which suggests the role of waters in facilitating concerted motions (and possibly ET) when the protein is compressed. At higher compression, only one loop shows significant fluctuaions (right figure; labelled) while there is effectively no major concerted motions elsewhere. Structural Properties Figure 10. Secondary structure wrt compression. Pink =  -helix, yellow = β-strands, green=turns, white = coils. The protein remains folded within the current compression range. Comparison with the in vacuo secondary structure plot shows that hydration does not appear to weaken the structural integrity of the protein under applied unidirectional force. Figure 11. Root-mean-square displacement (RMSD) for the C  atoms. Total simulation time is labelled on the x-axis, with correspoding tip- surface separations labelled on the graph. There is relatively little deviation from the initial X-ray structure up to approximately 3.4nm despite a significant reduction in the actual tip-surface distance, because at this compression the protein adopts an orientation which minimises its z- direction profile. Protein deformation commences at ~3.2nm separation. Cys3-Cys26 Maximum fluctuation region IRC Bionanotechnology