Simulation force fields

Figure 2. Cation-cation and cation-anion radial distribution functions for molten NaCl, obtained from simulation (force field given in Ref. [285]). Charge ordering is clearly visible, as described in the text.

Applying an all-atom parallel tempering simulation of the protein HP-36 in the ECEPP/2 force field [33] using an implicit solvent model [34] the authors of [27] have measured the diffusion of labeled replicas in temperature space. The simulated temperature interval is chosen such that at the lowest temperature = 250 K the protein is in a folded state and the highest temperature T ax = 1000 K ensures that the protein can fully unfold for the simulated force field. The measured local diffusivity for the random walk between these two extremal temperatures is shown in Fig. 7. A very strong modulation of the local diffusivity is found along the temperature. Note the logarithmic scale of the ordinate. The pronounced minimum of the local diffusivity around T 500 K points to a severe bottleneck in the simulation which by measurements of the specific heat has been identified as the helix-coil... 

AMBER = assisted model building with energy refinement force field CHARMM = chemistry at Harvard macromolecu-lar mechanics force field MP4SDQ = Mpller-Plesset fourth-order perturbation theory with corrections for single, double, and quadruple excitations OPLS = optimized potentials for liquid simulation force field TZP = triple-zeta -f polarization. 

The GROMOS (Groningen molecular simulation) force field has been developed by Berendsen et al. for proteins and nucleic acids (see GROMOS Force Field). It is based essentially on the same formula for energy calculations as the AMBER force field ... 

The OPLS (optimized potentials for liquid simulations) force fields express the potential energy of molecular systems in a classical framework consisting of harmonic terms for bond... 

If generic properties of polymers need to be determined, it is often sufficient to rely on lattice models. For comparison with experiments of particular melts and blends, more sophisticated off-lattice models are typically applied. These models are described by force fields that determine the interactions between atoms or groups of atoms, and the quality of the modeling is essential for the predictive quality of the simulations. Force field parameters can be derived from direct comparison with experimental data, from quantum mechanical calculations, or both. In the first part of this section, we present generic polymer models that are commonly used in molecular simulations without focussing on any particular substance. Emphasis is placed on lattice and simple off-lattice models that will also be discussed in the next three sections. Section 1.5 is dedicated to chemically realistic descriptions. 

The complexity of polymeric systems make tire development of an analytical model to predict tlieir stmctural and dynamical properties difficult. Therefore, numerical computer simulations of polymers are widely used to bridge tire gap between tire tlieoretical concepts and the experimental results. Computer simulations can also help tire prediction of material properties and provide detailed insights into tire behaviour of polymer systems. A simulation is based on two elements a more or less detailed model of tire polymer and a related force field which allows tire calculation of tire energy and tire motion of tire system using molecular mechanisms, molecular dynamics, or Monte Carlo teclmiques 1631. 

Atomistically detailed models account for all atoms. The force field contains additive contributions specified in tenns of bond lengtlis, bond angles, torsional angles and possible crosstenns. It also includes non-bonded contributions as tire sum of van der Waals interactions, often described by Lennard-Jones potentials, and Coulomb interactions. Atomistic simulations are successfully used to predict tire transport properties of small molecules in glassy polymers, to calculate elastic moduli and to study plastic defonnation and local motion in quasi-static simulations [fy7, ( ]. The atomistic models are also useful to interiDret scattering data [fyl] and NMR measurements [70] in tenns of local order. 

While simulations reach into larger time spans, the inaccuracies of force fields become more apparent on the one hand properties based on free energies, which were never used for parametrization, are computed more accurately and discrepancies show up on the other hand longer simulations, particularly of proteins, show more subtle discrepancies that only appear after nanoseconds. Thus force fields are under constant revision as far as their parameters are concerned, and this process will continue. Unfortunately the form of the potentials is hardly considered and the refinement leads to an increasing number of distinct atom types with a proliferating number of parameters and a severe detoriation of transferability. The increased use of quantum mechanics to derive potentials will not really improve this situation ab initio quantum mechanics is not reliable enough on the level of kT, and on-the-fly use of quantum methods to derive forces, as in the Car-Parrinello method, is not likely to be applicable to very large systems in the foreseeable future. 

However, in many applications the essential space cannot be reduced to only one degree of freedom, and the statistics of the force fluctuation or of the spatial distribution may appear to be too poor to allow for an accurate determination of a multidimensional potential of mean force. An example is the potential of mean force between two ions in aqueous solution the momentaneous forces are two orders of magnitude larger than their average which means that an error of 1% in the average requires a simulation length of 10 times the correlation time of the fluctuating force. This is in practice prohibitive. The errors do not result from incorrect force fields, but they are of a statistical nature even an exact force field would not suffice. 

Aqvist, J., Warshel, A. Simulation of enzyme reactions using valence bond force fields and other hybrid quantum/classical approaches. Chem. Rev. 93... 

The problems that occur when one tries to estimate affinity in terms of component terms do not arise when perturbation methods are used with simulations in order to compute potentials of mean force or free energies for molecular transformations simulations use a simple physical force field and thereby implicitly include all component terms discussed earlier. We have used the molecular transformation approach to compute binding affinities from these first principles [14]. The basic approach had been introduced in early work, in which we studied the affinity of xenon for myoglobin [11]. The procedure was to gradually decrease the interactions between xenon atom and protein, and compute the free energy change by standard perturbation methods, cf. (10). An (issential component is to impose a restraint on the... 

The fifth and final chapter, on Parallel Force Field Evaluation, takes account of the fact that the bulk of CPU time spent in MD simulations is required for evaluation of the force field. In the first paper, BOARD and his coworkers present a comparison of the performance of various parallel implementations of Ewald and multipole summations together with recommendations for their application. The second paper, by Phillips et AL., addresses the special problems associated with the design of parallel MD programs. Conflicting issues that shape the design of such codes are identified and the use of features such as multiple threads and message-driven execution is described. The final paper, by Okunbor Murty, compares three force decomposition techniques (the checkerboard partitioning method. 

Many problems in force field investigations arise from the calculation of Coulomb interactions with fixed charges, thereby neglecting possible mutual polarization. With that obvious drawback in mind, Ulrich Sternberg developed the COSMOS (Computer Simulation of Molecular Structures) force field [30], which extends a classical molecular mechanics force field by serai-empirical charge calculation based on bond polarization theory [31, 32]. This approach has the advantage that the atomic charges depend on the three-dimensional structure of the molecule. Parts of the functional form of COSMOS were taken from the PIMM force field of Lindner et al., which combines self-consistent field theory for r-orbitals ( nr-SCF) with molecular mechanics [33, 34]. 

Inadequate availability of experimental data can considerably inhibit the development of improved energy functions for more accurate simulations of energetic, structural, and spectroscopic properties. This has led to the development of class II force fields such as CFF and the Merck Molecular Force Field (MMFF), which are both based primarily on quantum mechanical calculations of the energy surface. The purpose of MMFF, which has been developed by Thomas Halgren at Merck and Co., is to be able to handle all functional groups of interest in pharmaceutical design. 

M. Jalaie, K. B. Lipkowitz, Published force field parameters for molecular mechanics, molecular dynamics, and Monte Carlo simulations, in Reviews in Computational Chemistry, Vol. 14, K.B. Lipkowitz, D. B. Boyd (Eds.), Wiley-VCH, New York, 2000, pp. 441-486. 

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