Computer Simulations of Molecular Recognition in Antibody- Protein Association

抗体-蛋白质关联中分子识别的计算机模拟

• 批准号: 9604223
• 负责人: Shankar Subramaniam
• 金额: $ 9.98万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-05-15 至 1999-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9604223&HistoricalAwards=false
• 关键词: Computer; Simulations; Molecular; Recognition; Antibody

The principal theoretical methodology proposed here is Brownian dynamics (BD). The basic idea in Brownian dynamics simulations is to model the complexation of two proteins undergoing relative motion according to Brownian motion under the influence of interprotein forces. The forces are often electrostatic in origin and continuum approaches such as the Poisson-Boltzmann (PB) method have been employed. In most BD calculations, the associating proteins have been described by simple models and while this has yielded qualitatively correct results, it has been difficult to predict absolute association rate constants. Also, only simple models of hydrodynamic interactions have been considered. We propose here to a) develop a detailed residue-level model for substrate/target and b) incorporate more accurate hydrodynamic effects into the method. It is increasingly recognized that simulations would serve as a valuable companion to experimental investigations and we want to establish the validity of Brownian dynamics as a useful method for studying kinetics of macromolecular association.

这里提出的主要理论方法是布朗动力学（BD）。布朗动力学仿真中的基本思想是在抑制蛋白力的影响下根据布朗运动进行相对运动的两种蛋白质的络合。 这些力通常是使用静电和连续方法，例如泊松玻尔兹曼（PB）方法。 在大多数BD计算中，相关蛋白已通过简单模型描述，尽管这产生了定性正确的结果，但很难预测绝对关联率常数。 同样，仅考虑了流体动力相互作用的简单模型。 我们在此处提出a）为底物/目标开发详细的残基级模型，b）将更准确的流体动力效应纳入该方法。人们越来越认识到，模拟将成为实验研究的宝贵伴侣，我们希望建立布朗动力学的有效性，作为研究大分子关联动力学的有用方法。