Modeling Aqueous Solvation Biology

描述（申请人提供）：为了提高生物学的计算建模，我们需要加深对水的理解，改进我们的溶剂化模型。显式水模型在计算上是昂贵的，而隐式水模型遗漏了很多物理原理，因此生物分子的计算机模拟通常不能很好地预测实验结果。我们在这里提出了一种新的解决方法，旨在像显式模型一样准确，像隐式模型一样快速。我们有三个目标：(1)开发三维解析和积分方程方法来计算水的结构和能量，(2)比较显式和隐式溶剂化模拟，以了解溶剂化壳中水结构的性质，(3)开发半显式溶剂化方法，该方法比显式更快，比隐式更物理。我们的方法更多地基于每个水分子的局部统计力学，而不是连续近似（隐式）或蛮力随机模拟。我们的初步结果让我们乐观地认为，这种方法是有效的。我们的模型给出的水密度与温度的关系与TIP4P-Ew一样精确，但速度快了6个数量级。水的初步相图看起来不错。我们的溶剂化模型捕获了中性和极性溶质的溶剂化的自由能，几乎和显式一样精确，并且计算速度和GB一样快。我们最近在盲SAMPL计算溶剂化建模事件中的结果非常令人鼓舞。

DESCRIPTION (provided by applicant): 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 here a new approach to solvation that aims to be as accurate as explicit models and as fast as implicit models. We have three aims: (1) To develop 3D analytical and integral-equation approaches to compute structures and energetics of water, (2) To compare explicit with implicit solvation simulations to learn the nature of water structuring in solvation shells, and (3) To develop a Semi-Explicit method for solvation, which is faster than explicit, and more physical than implicit. Our approach is 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 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. Our recent results in the blind SAMPL computational solvation modeling event are highly encouraging. PUBLIC HEALTH RELEVANCE: 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.