U.S.-France Cooperative Research: Implicit Solvent Models for Biomolecular Simulations

[128783]这个为期三年的美国-法国计算生物物理学合作奖涉及斯克里普斯研究所和法国斯特拉斯堡分子和细胞生物学与遗传学研究所的研究人员。美国的大卫·a·凯斯、查尔斯·l·布鲁克斯三世和法国的托马斯·西蒙森领导了这个合作项目。联合努力的目的是开发和分发用于蛋白质和核酸分子动力学模拟的计算机代码。具体目标包括参数化和实现广义波恩溶剂模型的关键测试性能，以及核磁共振（核磁共振）结构改进和生物分子行为的新应用。研究人员在生物分子模拟和广泛使用的计算机程序方面具有互补的专业知识：CNS/XPLOR (Simonson), CHARMM （Brooks）和AMBER （Case）。新的分布式计算机代码将允许新型的生物分子模拟。增强的广义Born模型将用于结构预测、蛋白质-配体相互作用预测、蛋白质折叠、结构-功能分析。该奖项代表了美国方面向国家科学基金会和法国国家科学研究中心（CNRS）提出的平行提案。美国国家科学基金会将承担美国研究人员和研究生的旅费和生活费。CNRS将支持法国参与者访问美国。该项目为研究生的研究培训增加了国际层面，并增加了未来合作伙伴关系的机会。

0128783CaseThis three-year award for U.S.-France collaboration in computational biophysics involves researchers at the Scripps Research Institute and the Institute for Molecular and Cellular Biology and Genetics in Strasbourg, France. David A. Case, Charles L. Brooks III in the US and Thomas Simonson in France lead the collaborative program. The joint effort is aimed at the development and distribution of computer codes for molecular dynamics simulations of proteins and nucleic acids. The specific aims include performance of critical tests for parameterizations and implementation of the generalized Born solvent model and new applications for NMR (Nuclear Magnetic Resonance) structure refinement and for behavior of biomolecules. The investigators have complementary expertise in biomolecular simulation and in widely used computer programs: CNS/XPLOR (Simonson), CHARMM (Brooks) and AMBER (Case). The new distributed computer codes will allow new types of biomolecular simulations. The enhanced generalized Born model will be used for structure prediction, prediction of protein-ligand interactions, protein folding, structure-function analyses. This award represents the US side of parallel proposals to the NSF and the French National Center for Scientific Research (CNRS). NSF will cover travel funds and living expenses of the US investigators and graduate students. The CNRS will support visits to the US by the French participants. The project adds an international dimension to research training of graduate students and enhances opportunities for future collaborative partnerships.