Computational Modeling of Solvation

In Chapter 2 we described the molecular mechanics approach to computing the structures and energies of organic molecules in the gas phase. There are also quantum mechanical methods for achieving the same goals, and these are discussed in some detail in Chapter 14. But, of course, most chemistry occurs in solution, and theorists, therefore, have made great 

More Foldamers Folding Driven by Solvophobic Effects 

Another foldamer strategy involves oligo(phenylene ethynylene) structures that fold into helical conformations, creating tubular cavities. The folding is driven primarily by solvophobic effects—the nonpolar aromatic portions want to get away from the polar solvent, while the polar ethylene oxide side chains are exposed. Favorable aromatic-aromatic interactions may also be involved. These helical structures resemble a common protein motif—the a/ (3-barrel—and are also promising scaffolds for future study. 

NeLson, J. C., Saven, ]. G., Moore, J. S., and Wolynes, P. G. Solvophobi-cally Driven Folding of Nonbiological Oligomers. Science, IT , 1793-1796 (1997). 

The modeling of a solvent—a liquid phase—is especially challenging. In the gas phase, the molecules can be treated as isolated species that are easily modeled using quantum mechanics (Chapter 14) or molecular mechanics (Chapter 2). Modeling a solid is certainly challenging, but at least in the crystalline state there is periodic order, which in principle, simplifies the problem. Still, accurate computer modeling of solids is a major challenge. 

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Muller, N. "Search for a Realistic View of Hydrophobic Effects." Acc. Chem. Res., 23,23 (1990). Computational Modeling of Solvation... 

It has been argued that the accuracy necessary to discriminate between the native and unfolded state of a protein is typically 1 kcal mol per residue." Empirical force fields have not yet reached that level of accuracy. However, it can be expected that computational models of solvated proteins will be accurate enough to predict the thermodynamic equilibrium of protein folding. 

The present chapter thus provides an overview of the current status of continuum models of solvation. We review available continuum models and computational techniques implementing such models for both electrostatic and non-electrostatic components of the free energy of solvation. We then consider a number of case studies, with particular focus on the prediction of heterocyclic tautomeric equilibria. In the discussion of the latter we center attention on the subtleties of actual chemical systems and some of the dangers of applying continuum models uncritically. We hope the reader will emerge with a balanced appreciation of the power and limitations of these methods. 

Recapitulating the foregoing discussion, it is clearly not our opinion that solution experiments appear to be inadequate for the purpose of comparison with molecular theories. However, we want to point out that due to the evident shortcomings of present theoretical and computational facilities a distinct scepticism is necessary in order to avoid the production of meaningless data. Of course, the solution experiments remain the main source of information, the data of which must be explained by theory. At the present stage of knowledge it is only possible to pick out selected properties of solutions which can be described satisfactorily on a molecular basis. For instance, referring to Frank and Wen s model of solvation shells W, the structure of the inner shell should not be modified too much by... 

G. Scalmani, V. Barone, K. N. Kudin, C. S. Pomelli, G. E. Scuseria and M. J. Frisch, Achieving linear-scaling computation cost for the polarizable continuum model of solvation, Theoret. Chem. Acc., Ill (2004) 90. 

Once the computational model of the molecule is created, it is of most interest to study its properties in the natural environment, in particular, water solvent. Surrounding the molecule with water, allows us to study the solvation process. Like molecules, the solvent may be also described with different levels of accuracy. Beginning with all-atom models of water,48,49 which allow for the studies of solvent structure around solutes but are time consuming and the results are model dependent, to continuous dielectric models,50- 52 which are faster but less accurate and give no knowledge about the solvent itself. Thus, the difference in the level of description for both models is either an advantage or a drawback. These models are commonly known as explicit or implicit solvent models, respectively. 

Quantum chemical methods aim to treat the fundamental quantum mechanics of electronic structure, and so can be used to model chemical reactions. Such quantum chemical methods are more flexible and more generally applicable than molecular mechanics methods, and so are often preferable and can be easier to apply. The major problem with electronic structure calculations on enzymes is presented by the very large computational resources required, which significantly limits the size of the system that can be treated. To overcome this problem, small models of enzyme active sites can be studied in isolation (and perhaps with an approximate model of solvation). Alternatively, a quantum chemical treatment of the enzyme active site can be combined with a molecular mechanics description of the protein and solvent environment the QM/MM approach. Both will be described below. 

Developments in experimental and computational science have shed light on phenomena in bioenvironments and condensed phases that pose significant challenges for theoretical models of solvation [27]. Tapia [22] raises the important distinction between solvation theory and solvent effects theory. Solvation theory is concerned with direct evaluation of solvation free energies this is extensively covered by recent reviews [16,17]. Solvent-effect theory concerns changes induced by the medium onto electronic structure and molecular properties of the solute. Solvent-effect theory is concerned with molecular properties of the solvated molecule relative to the properties in vacuo as such it focuses on chemical features suitable for studying systems at the microscopic level [23]. Extensive reviews of different computational methods are given in a book by Warshel [24]. 

Figure 15.3 shows the results of computer simulations of solvation of a model ion in acetonitrile (CH3CN). The simulations produce the solvation function S t) for... 

The technique has been described in Section 10.15. In summary, a model of solvation is decided upon. The computer uses Monte Carlo or molecular dynamics methods and a simulation of the solvation pattern emerges. The beauty of the method lies in the capacity to vary the model at will by varying the type and number of interactions considered for each model. This will give a simulation for each set of conditions which can then be compared with each other and with results from all the experimental methods described earlier. In effect the computer is used to help find a model which fits experiment. 

J. Sadlej and M. Pecul, Properties and Spectroscopies Computational Modelling of the Solvent-Solute Effeet on NMR Molecular Parameters by a Polarizable Continuum Model , in Continuum Solvation Models in Chemical Physics, eds. B. Mennueci and R. Cammi, John Wiley Sons, Ltd., Chichester, UK, 2007, p. 125. 

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