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）。布朗动力学模拟的基本思想是模拟两个蛋白质在蛋白质间力的影响下根据布朗运动进行相对运动的复合。 力通常是静电的起源和连续的方法，如泊松-玻尔兹曼（PB）方法已被采用。 在大多数BD计算中，缔合蛋白质已经由简单的模型描述，虽然这已经产生了定性正确的结果，但很难预测绝对缔合速率常数。 此外，只考虑了简单的水动力相互作用模型。 我们在这里建议a）开发一个详细的残留物水平模型的底物/目标和B）纳入更准确的流体动力学效应的方法。人们越来越认识到，模拟将作为一个有价值的伴侣，实验研究，我们希望建立一个有用的方法，研究大分子缔合动力学的布朗动力学的有效性。