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3D Structures of Biological Macromolecules Part 5: Protein Structure Prediction and Protein Folding Jürgen Sühnel [email protected] Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena Centre for Bioinformatics Jena / Germany Supplementary Material: http://www.fli-leibniz.de/www_bioc/3D/ Molecular Mechanics (Force Field) http://cmm.info.nih.gov/modeling/guide_documents/molecular_mechanics_document.html How Do We Get the Parameters ? Experimental Data (Examples: Geometrical Parameters) Quantum-chemical Calculations (Examples: Charges) Quantum Chemistry Quantum Chemistry Quantum-chemical Calculations: Telomeric DNA Quantum-chemical Calculations: Telomeric DNA Quantum-chemical Calculations: Telomeric DNA Quantum-chemical Calculations: Telomeric DNA Geometry Optimization Optimization Methods Optimization Methods Optimization Methods – Steepest Descent Selection of an initial point x0 Determination of direction and step size for calculating the next point Optimization Methods – Conjugate Gradients Method Optimization Methods – Newton-Raphson Methods g -. gradient h - Hessian Molecular Dynamics Simulation of Protein Folding – Molecular Dynamics AMBER GROMOS CHARMM TINKER Molecular Dynamics Simulation Protein Capsid Of Filamentous Bacteriophage Ph75 From Thermus Thermophilus 1HGV, extended structure 1HGV, actual structure 1HGV, 61% helix, 1.928 ns 1HGV, 75% helix, 3.428 ns Images created using VMD (Visual Molecular Dynamics) (HUMPHREY, W., DALKE, A. and SCHULTEN, K., 1996.VMD - Visual Molecular Dynamics. Journal Molecular Graphics,14, pp33-38). Molecular Dynamics Packages amber.scripps.edu Molecular Dynamics Packages www.igc.ethz.ch/gromos/ Molecular Dynamics Packages www.charmm.org Molecular Dynamics Packages dasher.wustl.edu/tinker/ Visualizing and Analyzing Molecular Dynamics Simulations www.ks.uiuc.edu/Research/vmd/ Folding Surface for Lysozyme Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Protein Folding States Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Monitoring Protein Folding by Experimental Methods Dobson, Sali, Karplus, Angew. Chem. Int. Ed. 1998, 37, 868. Monitoring Protein Folding by Experimental Methods Paxco, Dobson, Curr. Opin. Struct. Biol. 1996, 6, 630. Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Villin headpiece domain (PDB code: 1vii) Actin binding site highlighted 36 amino acids Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Radius of Gyration The radius of gyration Rg is defined by the root-mean-square distance between all atoms in a molecule and the centroid. In a globular protein the radius of gyration Rg can be predicted with reasonable accuracy from the relationship Rg(pred) = 2.2 N 0.38 where N is the number of amino acids. Protein Folding by Molecular Dynamics Protein Folding by Molecular Dynamics Statistical Potentials wij(r) ij(r) * – - interaction free energy pair density reference pair density at infinite separation Statistical potentials can be determined by simply counting interactions of a specific type in a dataset of experimental structures. The distance dependence may or may not be taken into account. If not, the interaction free energy is usually called a contact potential. It represents an average over distances shorter than some cutoff distance rc. Thomas, Dill, J. Mol. Biol. 1996, 257, 457-469 Lattice Folding Lattice Algorithm • • • • • • Red = hydrophobic, Blue = hydrophilic If Red is near empty space E = E+1 If Blue is near empty space E = E-1 If Red is near another Red E = E-1 If Blue is near another Blue E = E+0 If Blue is near Red E = E+0 Ab Initio Protein Structure Prediction http://rosettadesign.med.unc.edu/ Ab Inition Protein Structure Prediction - Rosetta Structure representation: Only main-chain heavy atoms and Cbeta-atom of sidechains are taken into account, Bond lengths and bond angles are held constant and correspond to the alanine geometry. The only remaining geometrical variables are the backbone torsion angles. Structure generation: Generation of fragment libraries from experimental structures (3 and 9 amino acids). Splicing together fragments of proteins of known structure with similar sequences. The conformational space defined by these fragments is then searched by a Monte Carlo procedure with an energy function that favors compact structures with paired beta-strands and buried hydrophobic amino acids. A total of 1000 independent simulations are carried out (starting from different random number seeds) for each query sequence. The resulting structures are clustered. Initial evaluation by a scoring function Low-scoring conformations are identified by simulated annealing with a move set that involves replacing the torsion angles of a segment of the chain with a related amino acid sequence. Further evaluation. Protein Backbone Torsion Angles and Ramachandran Plot ROSETTA Results Simons, Strauss, Baker. J. Mol. Biol. 2001, 306, 1191-1199. Computational Thermostabilization Computational Thermostabilization Computational Thermostabilization Computational Thermostabilization PDB code: 1ox7 Cytosine deaminase (CD) catalyzes the deamination of cytosine (converts cytosine to uracil) and is only present in prokaryotes and fungi, where it is a member of the pyrimidine salvage pathway. The enzyme is of interest both for antimicrobial drug design and gene therapy applications against tumors. Computational Thermostabilization Prediction of stabile mutations with Rosetta Design Computational Thermostabilization Computational Thermostabilization Computational Thermostabilization Computational Thermostabilization Computational Thermostabilization Superposition of double and triple mutant structures (PDB codes: 1ysb, 1ysd) A23L I140L V108I IMB LINUX Cluster by IBM 1 Frontend 2 Storage Nodes 26 Compute Nodes Compute nodes: 2 x 2.4GHz Intel Xeon [tm] processors, 1GByte RAM, 40 GByte local IDE Hard Disk Frontend: Mirrored 73 GByte SCSI Disk Interconnect: Myrinet Disk array: 10 x 73 GByte Fiber Channel Disks Operating system: Linux Red-Hat 7.3 Cluster software: CSM (Cluster Systems Management), GPFS (General Parallel File System) Cluster vs. Grid Computing Clusters are made up of dedicated components and all components in a cluster are exclusively owned and managed as part of the cluster. All resources are known, fixed and usually uniform in configuration. It is a static environment. Grids differ from clusters because grids share resources from and among independent system owners. Grids are configured from computer systems that are individually managed and used both as independent systems and as part of the grid. Thus, individual components are not 'fixed' in the grid and the overall configuration of the grid changes over time. This results in a dynamic system that continually assesses and optimises its utilisation of resources. EUROGRID - BioGRID www.eurogrid.org/wp1.html Simulation of Protein Folding Simulation of Protein Folding Thousand trillon FLOPs IBM Blue Gene Project | System-on-a-Chip Approach ~ 65.000 processors teraflop – a trillion floating point operations per second IBM Blue Gene Project | System-on-a-Chip Approach