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，通过弥合理论和实验之间差距的原子级建模和模拟方法大大加速。这种广泛使用的方法之一，即所谓的隐式溶剂化，通过连续体以近似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），从而提供了显着的计算优势和多功能性。目前，这种隐式溶剂化方法的实用“引擎”要么是广义的玻恩（GB）模型，要么是更基本的泊松（或泊松-玻尔兹曼）方程形式。它是相对简单得多、更高效的 GB 模型，几乎专门用于分子动力学（MD）模拟，在分子动力学（MD）模拟中，它在从蛋白质折叠到分子对接的各个领域都取得了令人印象深刻的成功。然而，这种近似模型更高的计算效率和多功能性目前伴随着相对精度的降低。为了充分利用分子模拟中隐式溶剂化模型的众多优势，必须解决这些精度限制，并且需要克服。在之前的资助期间，我们开发了比生物分子建模界目前使用的流行的 GB 模型更准确和更高效的新模型。 GB 框架中存在的经典 GB 模型（例如二级结构偏差或错误的盐桥强度）在过去 20 年中保持不变，新方法的组合有望将基于我们的隐式溶剂化模型的 MD 模拟速度提高多达 4 个数量级。为了让建模界从这些发展中受益，这些方法必须在分子动力学模拟的背景下进行仔细的实施、测试和进一步完善，从而有望产生最大的影响。我们在这方面的目标首先是提高应用于生物分子系统的分子动力学模拟的准确性，其次是提高其速度，第三个前瞻性目标是开发一种概念上新的水溶剂化分析框架，该框架超越了当前实际分析静电模型的基础——连续、线性、局部响应静电学的泊松形式。第一水化壳的溶剂化作用。 公共卫生相关性：分子建模和模拟是生物医学和药物发现过程中不可或缺的工具。拟议的研究将通过使这些工具更快、更准确和更广泛地使用来显着增强这些工具的功能和重要发现的可能性。

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.