Analytical Electrostatics: Methods and Biological Applications

描述（由申请人提供）：原子级建模和模拟方法极大地加速了现代生物分子科学（从结构生物学到基于结构的药物设计）的进展，这些方法弥合了理论与实验之间的差距。这种广泛使用的方法之一，所谓的隐式溶剂化，提供了显着的计算优势和通用性，通过表示溶剂的影响-通常是这种模拟中计算成本最高的部分-以近似的方式，通过连续。目前，这种隐式溶剂化方法的实际“引擎”是广义玻恩（GB）模型或泊松（或泊松-玻尔兹曼）方程的更基本的形式主义。它是相对更简单和更有效的GB模型，几乎专门用于分子动力学（MD）模拟，在各种领域，从蛋白质折叠到分子对接，它已经显示出令人印象深刻的成功。然而，更大的计算效率和通用性，这种近似模型目前伴随着相对于更传统的，但计算上非常苛刻的显式溶剂的方法的准确性降低。这些准确性的限制，必须加以解决，以充分利用分子模拟中的隐式溶剂化模型提供的众多好处。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，我们开发了新的隐式水溶剂化模型，比生物分子建模社区目前使用的流行GB模型更准确和有效。新模型直接解决了经典GB模型的众所周知的缺陷，例如二级结构偏差或错误的盐桥强度，这些缺陷存在于过去20年来保持不变的GB框架中。一个新的方法相结合，有望加快MD模拟的基础上，我们的隐式溶剂化模型高达4个数量级。为了使建模社区从这些发展中受益，这些方法必须在分子动力学模拟的背景下仔细实施，测试和进一步完善，预计它们将产生最大的影响。 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。在这方面，我们的目标将是，第一，以提高应用于生物分子系统的MD模拟的准确性，第二，以提高其速度。第三，前瞻性的目标将是开发一个概念上新的分析框架的水溶剂化，超越目前的基础上的实际分析静电模型-泊松形式主义的连续，线性，局部响应静电。建议的全隐式，分析模型将保留第一水化壳的溶剂化效应。 公共卫生相关性：分子建模和模拟是生物医学科学和药物发现过程中不可或缺的工具。拟议中的研究将大大提高这些工具的能力，并通过使它们更快，更准确，更广泛地获得重要发现的可能性。

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.