Analytical Electrostatics: Methods and Biological Applications.

分析静电学：方法和生物学应用。

• 批准号: 7142905
• 负责人: ALEXEY VLAD ONUFRIEV
• 金额: $ 21.88万
• 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 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7142905
• 关键词: computer simulation; conformation; high throughput technology; ion transport; ionic bond; mathematical model; model design /development; molecular dynamics; solvents; water

DESCRIPTION (provided by applicant): The broad goals of this project are the development of novel theoretical models and practical computational tools that will improve and facilitate the process of modeling and simulating bio-molecules. The new models will be based on "implicit solvent" approach in which individual water molecules and mobile solvent ions are replaced by a continuous medium with the average properties of the solvent. Currently, the "engine" of the methodology - responsible for the estimation of the key electrostatic interactions - is either the generalized Born (GB) or the Poisson Boltzmann model (PB). The GB model is computationally efficient, but lacks the critical accuracy of the fundamental, but computationally expensive PB approach. Within the proposed approach, exact solutions of the PB equation for typical molecular shapes will serve as the foundation for deriving computationally efficient, analytical models. The models will go beyond the current generation of the generalized Born (GB) models, in both accuracy and efficiency. New important features will be added, such as the ability to compute electrostatic potential at every point in space: potential generated by a bio-molecule is often a key determinant of its function. For large compounds, e.g. multi-protein complexes, viral capsids, the ribosome or the nucleosome, the proposed approach may be the only practical way to generate potential maps with the power of a desktop computer. Approaches specifically targeted to speed-up simulations based on the implicit solvent models will be developed. They will be based upon coarse-graining of the charge distribution and will not have the significant artifacts typical of the "standard" schemes in which interactions beyond a specified distance are neglected. The methods will yield at least a 10-fold increase in computational speed for large bio-molecular structures. The use of the new models will be expanded to applications where the GB model is currently not applied, but where computational speed and accuracy are critical, for example in quantum mechanics-molecular mechanics (QM-MM) calculations on bio-molecules. The fast, analytical models of solvation will become more dependable. The models will be used to gain insights into the molecular mechanism of enhanced flexibility of short DNA fragments. RELEVANCE: Molecular modeling and simulations are nowadays indispensable tools in biomedical science and the drug discovery process. The proposed methods will significantly enhance their accuracy and speed.

描述（由申请人提供）：该项目的主要目标是开发新的理论模型和实用的计算工具，以改善和促进建模和模拟生物分子的过程。新模型将基于“隐式溶剂”方法，其中单个水分子和移动的溶剂离子被具有溶剂平均性质的连续介质取代。目前，“引擎”的方法-负责估计的关键静电相互作用-是广义玻恩（GB）或泊松玻尔兹曼模型（PB）。GB模型计算效率高，但缺乏基本的关键精度，但计算昂贵的PB方法。在所提出的方法中，PB方程的典型分子形状的精确解将作为计算效率高，分析模型推导的基础。该模型在精度和效率上都将超过当前一代的广义玻恩（GB）模型。将增加新的重要功能，例如计算空间中每个点的静电势的能力：生物分子产生的电势通常是其功能的关键决定因素。对于大的化合物，例如多蛋白质复合物，病毒衣壳，核糖体或核小体，所提出的方法可能是唯一实用的方法来生成潜在的地图与台式计算机的功率。将开发基于隐式溶剂模型的专门针对加速模拟的方法。它们将基于电荷分布的粗粒化，并且不会具有“标准”方案中典型的显著伪影，其中忽略了超过指定距离的相互作用。这些方法将使大型生物分子结构的计算速度提高至少10倍。新模型的使用将扩展到目前未应用GB模型但计算速度和准确性至关重要的应用，例如生物分子的量子力学-分子力学（QM-MM）计算。溶剂化的快速分析模型将变得更加可靠。这些模型将用于深入了解短DNA片段增强灵活性的分子机制。相关性：分子建模和模拟是当今生物医学科学和药物发现过程中不可或缺的工具。所提出的方法将显着提高其准确性和速度。