Analytical Electrostatics: Methods and Biological Applications

分析静电学：方法和生物学应用

• 批准号: 8520321
• 负责人: ALEXEY VLAD ONUFRIEV
• 金额: $ 27.72万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8520321
• 关键词: Acceleration; Address; Area; Awareness; Biological; Biomedical Research; Charge; Communities; Docking; Drug Design; Effectiveness; Electrostatics; Equation; Equilibrium; Foundations; Free Energy; Funding; Goals; Hybrids; Hydration status; Lead; Methodology; Methods; Modeling; Molecular; Molecular Models; Nucleosomes; Physics; Population; Process; Property; Proteins; Relative (related person); Research; Sampling; Science; Sodium Chloride; Solutions; Solvents; Speed; Structure; Structure-Activity Relationship; System; Tail; Testing; Translating; Water; aqueous; base; design; drug discovery; improved; method development; models and simulation; molecular dynamics; molecular modeling; next generation; novel strategies; protein folding; research study; response; simulation; stem; structural biology; success; supercomputer; theories; tool

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.

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。这是相对更简单和更高效的GB模型它几乎专门用于分子动力学 (MD) 模拟，在从蛋白质折叠到分子对接的各个领域都取得了令人瞩目的成功，然而，此类近似模型的计算效率和多功能性目前要高得多，但其计算效率和多功能性却有所下降。相对准确度必须解决这些精度限制，以便充分利用分子模拟中隐式溶剂化模型提供的众多优势。此外，这些模型的速度限制也变得明显。最近，在之前的资助期间，我们开发了新的隐式水溶剂化模型，比生物分子建模界目前使用的流行的 GB 模型更准确、更高效。新模型直接解决了规范 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 年保持不变的 GB 框架中。新颖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。为了使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仔细实施、测试和进一步完善，预计它们会产生最大的影响。 因此，此次更新旨在将新模型纳入免费且流行的分子动力学模拟包中。我们在这方面的目标首先是提高应用于生物分子系统的MD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用分析静电模型的基础——连续、线性、局部响应静电的泊松形式主义。所提出的完全隐式分析模型将保留第一个水化壳的大部分溶剂化效应。