Phase Equilibrium Theory of Aqueous Two-Phase Extraction: International Collaboration

该项目的目的是制定一个全面的、综合的 理论，基于统计力学，蛋白质溶解度在两个- 相聚合物水溶液，其含有聚乙二醇 和葡聚糖或聚乙二醇和盐。 目标是 预测自由能、水相分离和蛋白质分配 系数的模型含水聚合物两相系统作为一个 一些重要参数的函数：蛋白质和聚合物 浓度，蛋白质和聚合物分子量，蛋白质电荷， pH、蛋白质类型、聚合物类型、盐类型、离子强度、温度 和亲和配体的结合能。 理论上来说 通过在国际上进行的一系列实验， 与世界著名的双水相专家合作 提取，教授博士M。R.酶研究所的库拉 技术在杜塞尔多夫，德国。 实验将在 在库拉博士的实验室和北卡罗来纳州 大学实验室。 尽管预计的重要性，水两相萃取 未来的分离技术，没有全面的 存在预测蛋白质分配的理论框架， 这些系统。 这种方法是基于统计的， 力学，将允许一个调查的相对重要性， 排除体积效应和电子相互作用，并预测 蛋白质分配系数和离子分配系数的值 系数 这项研究应该提供深入了解分子 蛋白质分配的基础和产量的有用的相关性 蛋白质分配系数

The aim of this project is the development of a general, comprehensive theory, based on statistical mechanics, of protein solubility in two- phase aqueous polymer solutions containing either polyethylene glycol and dextran or polyethylene glycol and salt. The objective is to predict free energies, aqueous phase separations and protein partition coefficients for a model aqueous polymer two-phase system as a function of a number of important parameters: protein and polymer concentrations, protein and polymer molecular weights, protein charge, pH, protein type, polymer type, salt type, ionic strength, temperature and the binding energy of affinity ligands. The theory will be supported by a series of experiments conducted in international collaboration with a world-renown expert in aqueous two phase extraction, Prof. Dr. M. R. Kula of the Institute for Enzyme Technology in Dusseldorf, Germany. The experiments will be performed both in Dr. Kula's laboratory and in the North Carolina State University laboratory. Despite the projected importance of aqueous two-phase extractive techniques for future separations technology, no comprehensive theoretical framework exists for predicting protein partitioning in these systems. This approach, which is based in statistical mechanics, will allow one to investigate the relative importance of excluded volume effects and electronic interactions and to predict values for both protein partition coefficients and ion partition coefficients. The research should provide insight into the molecular basis of protein partitioning and yield a useful correlation of protein partition coefficients.