Molecular dynamics sampling times

Figure 6. (a) 2-Gilorobutane at 50 K, 6x6 site-site potential, angular velocity autocorrelation functions. Crosshatdiing indicates computer noise difference between R and S enantiomers. (—) Racemic mixture, (b) As for (a), under the influence of a strong field E, producing a torque — 63 XE in each molecule of the molecular dynamics sample. (1) (—) Racemic mixture (2) (—) R enantiomer. Ordinate Normalized correlation function abscissa time, ps. 

A major challenge in polymer simulations is to produce a large number of uncorrelated configurations, to sample various chain conformations. In molecular dynamics, each time step involves small changes in the monomer positions. Significantly changing the chain conformation involves large... 

A typical molecular dynamics simulation comprises an equflibration and a production phase. The former is necessary, as the name imphes, to ensure that the system is in equilibrium before data acquisition starts. It is useful to check the time evolution of several simulation parameters such as temperature (which is directly connected to the kinetic energy), potential energy, total energy, density (when periodic boundary conditions with constant pressure are apphed), and their root-mean-square deviations. Having these and other variables constant at the end of the equilibration phase is the prerequisite for the statistically meaningful sampling of data in the following production phase. 

Molecular dynamics simulations are el ficient for searching the conformational space of medium-sized molecules and peptides. Different protocols can increase the elTicieiicy of the search and reduce the computer time needed to sample adequately the available conformations. 

Fiber R and M Karplus 1990. Enhanced Sampling in Molecular Dynamics Use of the Time-Dependent Hartree Approximation for a Simulation of Carbon Monoxide Diffusion through Myoglobin. Journal of the American Chemical Society 112 9161-9175. 

Thus, unlike molecular dynamics or Langevin dynamics, which calculate ensemble averages by calculating averages over time, Monte Carlo calculations evaluate ensemble averages directly by sampling configurations from the statistical ensemble. 

Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation.

If the amount of the sample is sufficient, then the carbon skeleton is best traced out from the two-dimensional INADEQUATE experiment. If the absolute configuration of particular C atoms is needed, the empirical applications of diastereotopism and chiral shift reagents are useful (Section 2.4). Anisotropic and ring current effects supply information about conformation and aromaticity (Section 2.5), and pH effects can indicate the site of protonation (problem 24). Temperature-dependent NMR spectra and C spin-lattice relaxation times (Section 2.6) provide insight into molecular dynamics (problems 13 and 14). 

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