Analytical Electrostatics: Methods and Biological Applications

分析静电学：方法和生物学应用

• 批准号: 8182362
• 负责人: ALEXEY VLAD ONUFRIEV
• 金额: $ 28.27万
• 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/8182362
• 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. 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.

描述（由申请人提供）：从结构生物学到基于结构的药物设计的现代生物分子科学的进展，通过原子级建模和模拟方法大大加速，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，即所谓的隐式溶剂化，通过代表溶剂的效果（通常是此类仿真中最昂贵的部分最昂贵的部分），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”是普遍的BORN（GB）模型，或者是Poisson（或Poisson-Boltzmann）方程的更基本的形式主义。它是相对简单，更有效的GB模型，它几乎独特地用于分子动力学（MD）模拟中，它在摩尔型中的模拟量很大，这是一定的型号。但是，目前，相对于更传统的计算效率和多功能性的精度降低了，但是在计算上，必须完全解决这些准确的效果。资助我们已经开发了新的隐式水溶性溶剂化模型，这些模型比生物分子建模社区目前正在使用的流行GB模型更准确，更高效。新模型直接解决了典型GB模型的众所周知的缺陷，例如二级结构偏置或错误的盐桥强度，这在过去20年中一直保持不变。一种新型方法的组合有望根据我们的隐式溶剂化模型加速MD模拟，最多可以使用4个数量级。为了使建模社区从这些发展中受益，必须在分子动力学模拟的背景下仔细实施，测试和进一步完善这些方法，以期会产生最大的影响。 因此，这种续约旨在将新模型纳入自由使用以及流行的分子动力学仿真软件包中。我们在这方面的目标将是首先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。第三个前瞻性目标将是开发一个概念上的水溶性溶剂化分析框架，该框架超出了当前实用的分析静电模型的基础，即连续性，线性，局部响应静电的泊松形式。提出的完全隐式，分析模型将保留第一个水合壳的大多数溶剂化效应。 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必不可少的工具。拟议的研究将显着增强这些工具的能力，并通过使它们更快，更准确且更广泛地提供重要发现的可能性。