To improve computational modeling in biology, we need to deepen our understanding of water and improve our models of solvation. Explicit water models are computationally expensive and implicit water models miss much of the physics, so computer simulations of biomolecules often don't predict experiments as well as they could. We propose to develop here a new approach to solvation that aims to be as accurate as explicit models and as fast as implicit models. We have four aims here: (1) To develop a 3D analytical model of water, to compute structures and energetics, (2) To compare explicit with implicit solvation simulations to learn the nature of water structuring in first and second solvation shells, (3) To develop Semi-explicit solvation, which should be faster than explicit, and more physical than implicit, and (4) To explore the energetic peculiarities of charged hydrophobic solutes, called ionenes, through theory and experiments. Our approach focuses on developing a different approach to solvation modeling, based more on the local statistical mechanics of each water molecule, rather than on continuum approximations (implicit), or brute force stochastic simulations. Our preliminary results give us optimism that this approach is working. Our model gives the density of water vs temperature as accurately as TIP4P-Ew but 6 orders of magnitude faster. The preliminary phase diagram of water looks fairly good. Our solvation model is capturing the free energies of solvation of neutrals and polar solutes about as accurately as explicit, and is about as fast to compute as GB, although it is not yet as accurate for ions.
The foundation of biological processes starts at the molecular level, and one of our key tools for understanding microscopic systems is computational modeling. Computer simulations of biomolecules often don't predict experiments as well as they could, and one of the primary reasons is limitations in the modeling of ever present water. We propose to develop new approaches for treating water that aim to deepen our understanding and lift the limitations of models for solvation.
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