Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling

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Virginia Tech

Biomolecular Modeling is playing an important role in many practical applications such as biotechnology and structure-based drug design. One of the essential requirements of Biomolecular modeling is an accurate description of the solvent (water). The challenge is to make this description computationally facile that is reasonably fast, simple, robust and easy to incorporate into existing software packages. The most rigorous procedure to model the effect of aqueous solvent is to explicitly model every water molecule in the system. For many practical applications, this approach is computationally too intense, as the number of required water atoms is on average one order of magnitude larger than the number of atoms of the molecule of interest.

Implicit solvent models, in which solvent molecules are represented by a continuum function, have become a popular alternative to explicit solvent methods as they are computationally more efficient. The Generalized Born (GB) implicit solvent has become quite popular due to its relative simplicity and computational efficiency. However, recent studies showed serious deficiencies of many GB variants when applied to Biomolecular Modeling such as an over- stabilization of alpha helical secondary structures and salt bridges.

In this dissertation we present two new GB models aimed at computing solvation properties with a reasonable compromise between accuracy and speed. The first GB model, called NSR6, is based on a numerically surface integration over the standard molecular surface. When applied to a set of small drug-like molecules, NSR6 produced an accuracy, with respect to experiments, that is essentially at the same level as that of the expensive explicit solvent treatment. Furthermore, we developed an analytic GB model, called AR6, based on an approximation of the volume integral over the standard molecular volume. The accuracy of the AR6 model is tested relative to the numerically exact NSR6. Overall AR6 produces a good accuracy and is suitable for Molecular Dynamics simulations which is the main intended application.

Molecular Modeling, Implicit solvents, Generalized Born Model