Modeling Aqueous Solvation Biology

描述(申请人提供)：为了改进生物学中的计算建模，我们需要加深对水的理解，并改进我们的溶剂化模型。显式水模型的计算成本很高，而隐式水模型忽略了很多物理问题，因此生物分子的计算机模拟往往不能很好地预测实验。我们在这里提出了一种新的求解化方法，其目标是与显式模型一样精确，与隐式模型一样快。我们有三个目标：(1)发展计算水的结构和能量的三维解析和积分方程法，(2)比较显式和隐式溶剂化模拟，以了解溶剂化壳层中水结构的性质，以及(3)发展一种半显式方法，该方法比显式快，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机制，而不是连续统近似(隐式)或蛮力随机模拟。我们的初步结果让我们乐观地认为，这种方法正在奏效。我们的模型给出的水与温度的密度与TIP4P-Ew一样精确，但速度快6个数量级。水的初步相图看起来很好。我们的溶剂化模型几乎和显式一样准确地捕捉了中性和极性溶质的溶剂化自由能，并且计算速度几乎和GB一样快。我们最近在盲样计算溶剂化模拟事件中的结果是非常令人鼓舞的。 与公共健康相关：生物过程的基础始于分子水平，我们理解微观系统的关键工具之一是计算模型。生物分子的计算机模拟往往不能很好地预测实验，其中一个主要原因是对曾经存在的水进行建模的局限性。我们建议开发新的水处理方法，旨在加深我们的理解，并消除溶剂化模型的局限性。

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.