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Chem. 860 Molecular Simulations with Biophysical
Applications
Qiang CuiDepartment of Chemistry and Theoretical Chemistry Institute
University of Wisconsin, MadisonSpring, 2009
TopicsBasic ideas of biomolecular simulations
Empirical Force Fields
Equilibrium simulations: Basic Molecular Dynamics and (some) Monte Carlo
Non-equilibrium (time-dependent) properties
Some specialized techniques (Car-Parrinello; QM/MM, Transition path sampling...)
Current challenges (Multi-scale simulations)
Goal: learn how to design and carry out proper simulations for biophysical applications
(Bio)molecular Simulations
Evaluate analytic theories (solvation, rate, spectroscopy)
Help better interpret complex experimental data in structural and dynamical terms (spectra, diffraction, NMR)
In the absence of direct experimental data, observe the behavior of the system for mechanistic investigations or predictions
Equilibrium properties (thermodynamics, average structure and fluctuation)
Time-dependent properties (chemical reactions, conformational transitions/folding, diffusion)
Karplus, Petsko, Nature, 347, 631 (1991); Karplus, McCammon, Nat. Struct. Biol. 9, 646 (2002)
Use physical based techniques to numerically simulate the behavior of molecular systems
Unique power of simulations
High spatial and temporal resolution
Facilitate analysis of important factors for mechanistic investigations - easy to turn on and off specific contribution
“High-throughput” rational design of new ligands, biomolecules or (e.g., mutation) experiments
Obtain insights into processes difficult (or devastating) to do experimentally (Nuclear meltdown, galaxy collision)
Ultimately: stimulate new experiments
Observe - analyze (model building) - design
Example 1. Water channel
de Groot, Grubmuller, Science, 294, 2353 (2001); E. Tajkhorshid et al. Science, 296, 525 (2002)
“State-of-the-art” all-atom simulation: 100,000 atoms; ~100 ns
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Example 1.2 K+ channel
Berneche et al. Roux, Nature, 414, 73 (2001); 431, 830 (2004)
Example 1.3 Real-Time-dependence
Barrier (re)crossing Hammes-Schiffer @ PSU
Benkovic, Hammes-Schiffer, Science, 301, 1196 (2003)
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Example 2. Solvent effect on protein dynamics
Vitkup, Ringe, Petsko, Karplus, Nat. Struct. Biol. 7, 34 (2000)
Example 2.2 Solvent effect on protein-ligand dynamics
Loring et al. J. Phys. Chem. B 105, 4068 (2001)
Example 2.3 Diffuse IR band and proton storage site in bRExample 2.3 Diffuse IR band and proton storage site in bR
Gerwert et al. Nature, 439, 109 (2006) QC et al. PNAS, 105, 19672 (2008)
XH+
bRbR
Ex 3. Rational Design of proteins and ligands
ab initio design of a Novel fold
Kuhlman et al., Baker, Science, 302, 1364 (2003)
Incorporate catalytic function into proteinsDwyer et al., Hellinga,
Science, 304, 1967 (2004)
Basic elements Potential Function (force field): how atoms in biomolecules ( ) interact with each other and how biomolecules interact with the environment ( ).
Equilibrium statistical mechanics
Non-equilibrium statistical mechanics (MD only)
Molecular Dynamics (MD)
Monte Carlo (stochastic)
LimitationsPotential Energy Function (force field; QM level)
Limited conformational/chemical (e.g., titration) sampling (requires smart techniques!)
System finite size (depending on the range of interaction)
Bottom line: Design proper simulation for your question!"when one microsecond is a long time" Y. Duan, P. A. Kollman, Science, 282, 740 (1998) 1μs RMSD ~ 3 Å
LimitationsPotential Energy Function (force field; QM level)
Limited conformational/chemical (e.g., titration) sampling (requires smart techniques!)
System finite size (depending on the range of interaction)
Bottom line: Design proper simulation for your question!Coarse-grained models http://md.chem.rug.nl/~marrink/MOV/index.html
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