Analytical Electrostatics: Methods and Biological Applications

描述（由申请人提供）：现代生物分子科学的进步，从结构生物学到基于结构的药物设计，通过原子水平的建模和模拟方法极大地加速了理论和实验之间的差距。这类被广泛使用的方法之一，即所谓的隐式溶剂化，通过一个连续体以近似的方式表示溶剂的影响（通常是此类模拟中计算成本最高的部分），提供了显著的计算优势和通用性。目前，这种隐式溶剂化方法的实际“引擎”要么是广义玻恩（GB）模型，要么是更基本的泊松（或泊松-玻尔兹曼）方程的形式。它是相对简单和高效得多的GB模型，几乎完全用于分子动力学（MD）模拟，在从蛋白质折叠到分子对接的各个领域都显示出令人印象深刻的成功。然而，这种近似模型的更高的计算效率和通用性目前伴随着相对于传统的精度降低，但计算非常苛刻的显式溶剂方法。为了充分利用隐式溶剂化模型在分子模拟中提供的众多好处，必须解决这些精度限制。此外，这些模型的速度限制最近也变得明显，需要克服。在之前的资助期间，我们开发了隐式水溶液溶剂化的新模型，比生物分子建模界目前使用的流行的GB模型更准确和高效。新模型直接解决了标准GB模型中众所周知的缺陷，例如在过去20年中保持不变的GB框架中存在的二次结构偏差或错误的盐桥强度。新方法的组合有望将基于隐式溶剂化模型的MD模拟速度提高4个数量级。为了让建模社区从这些发展中受益，这些方法必须仔细实施、测试，并在分子动力学模拟的背景下进一步完善，因为它们有望产生最大的影响。因此，这次更新的目的是将新模型纳入免费提供的以及流行的分子动力学模拟包。我们在这方面的目标是，首先提高应用于生物分子系统的MD模拟的准确性，其次提高其速度。第三，前瞻性目标将是开发一个概念性的水溶液分析框架，超越当前实用分析静电模型的基础——连续体、线性、局部响应静电的泊松形式。所提出的完全隐式分析模型将保留第一水化壳的大部分溶剂化效应。

DESCRIPTION (provided by applicant): Progress in modern bio-molecular sciences, from structural biology to structure-based drug design, is greatly accelerated by methods of atomic-level modeling and simulations that bridge the gap between theory and experiment. One of the widely used methods of this kind, the so-called implicit solvation, provides significant computational advantages and versatility by representing the effects of solvent - often the most computationally expensive part of such simulations - in an approximate manner, via a continuum. Currently, the practical "engine'' of this implicit solvation methodology is either the generalized Born (GB) model or the more fundamental formalism of the Poisson (or Poisson-Boltzmann) equation. It is the relatively much simpler and more efficient GB model that has almost exclusively been used in molecular dynamics (MD) simulations where it has shown impressive success in a variety of areas, from protein folding to molecular docking. However, the much greater computational efficiency and versatility of such approximate models are currently accompanied with a reduced accuracy relative to the more traditional, but computationally very demanding explicit solvent approach. These accuracy limitations must be addressed in order to fully utilize the numerous benefits offered by the implicit solvation models in molecular simulations. In addition, the speed limitations of these models have also become apparent lately, and need to be overcome. During the period of previous funding, we have developed new models of implicit aqueous solvation that are more accurate and efficient than the popular GB models currently in use by the bio-molecular modeling community. The new models directly address the well-known deficiencies of the canonical GB models, such as secondary structure bias or erroneous salt-bridge strength, present in the very GB framework that remained unchanged over the past 20 years. A combination of novel approaches promises to speed-up MD simulations based on our implicit solvation models by up to 4 orders of magnitude. For the modeling community to benefit from these developments, the methods must be carefully implemented, tested, and further refined specifically in the context of Molecular Dynamics simulations where they are expected to make the highest impact. This renewal thus aims to incorporate the new models into freely available as well as popular Molecular Dynamics simulation packages. Our goals in this regard will be, first to improve the accuracy of MD simulations applied to bio-molecular systems, and second, to improve their speed. A third, forward looking goal will be to develop a conceptually new analytical framework of aqueous solvation that goes beyond the current foundation of practical analytical electrostatic models -- the Poisson formalism of continuum, linear, local response electrostatics. The proposed fully implicit, analytical models will retain most of the solvation effects of the first hydration shell. PUBLIC HEALTH RELEVANCE: Molecular modeling and simulations are indispensable tools in biomedical science and the drug discovery process. The proposed research will significantly enhance the capabilities of these tools and the likelihood of important discoveries by making them faster, more accurate, and more widely available.